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<div class="WordSection1">
<p class="MsoNormal" style="text-align:justify"><a name="_Hlk529360999"></a><a name="OLE_LINK1"></a><a name="OLE_LINK2"></a><a name="OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span lang="EN-US" style="font-family:"Calibri",sans-serif;color:#1F4E79"><o:p> </o:p></span></span></span></span></a></p>
<p class="MsoNormal" style="text-align:justify"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><b><span lang="EN-US" style="font-family:"Calibri",sans-serif;color:#1F4E79">TL;DR
– CFP IEEE TNNLS special issue on Deep Representation and Transfer Learning for Smart and Connected Health. Submission Deadline 31<sup>st</sup> March 2019.
</span></b></span></span></span></span><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><b><span lang="EN-US" style="font-family:"Calibri",sans-serif;color:#1F4E79"><o:p></o:p></span></b></span></span></span></span></p>
<p class="MsoNormal" style="text-align:justify"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span lang="EN-US" style="font-family:"Calibri",sans-serif;color:#1F4E79"><o:p> </o:p></span></span></span></span></span></p>
<p class="MsoNormal" style="text-align:justify"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span lang="EN-US" style="font-family:"Calibri",sans-serif;color:#1F4E79">Call
for Papers:<o:p></o:p></span></span></span></span></span></p>
<p class="MsoNoSpacing" align="center" style="text-align:center"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><b><span style="font-size:14.0pt;font-variant:small-caps;mso-fareast-language:ZH-CN"><o:p> </o:p></span></b></span></span></span></span></p>
<p class="MsoNoSpacing"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><b><span style="font-size:14.0pt;font-variant:small-caps;mso-fareast-language:ZH-CN">IEEE
Transactions on Neural Networks and Learning Systems<o:p></o:p></span></b></span></span></span></span></p>
<p class="MsoNoSpacing"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><b><span style="font-size:12.0pt;color:#1F3864;mso-fareast-language:ZH-CN">Special
Issue on </span></b></span></span></span></span><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><b><span style="font-size:12.0pt;color:#1F3864">Deep Representation
and Transfer Learning for Smart and Connected Health<o:p></o:p></span></b></span></span></span></span></p>
<p class="MsoNormal"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><b><span lang="EN-US" style="font-family:"Calibri",sans-serif;color:#1F3864"><o:p> </o:p></span></b></span></span></span></span></p>
<p class="MsoNoSpacing" style="text-align:justify"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><b><span style="font-size:14.0pt;font-variant:small-caps;color:#1F3864">Important
Dates<a name="OLE_LINK10"></a><a name="OLE_LINK15"></a><a name="OLE_LINK28"></a><a name="OLE_LINK23"><span style="mso-bookmark:OLE_LINK28"><span style="mso-bookmark:OLE_LINK15"><span style="mso-bookmark:OLE_LINK10"><o:p></o:p></span></span></span></a></span></b></span></span></span></span></p>
<p class="MsoNoSpacing"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK23"><span style="mso-bookmark:OLE_LINK28"><span style="mso-bookmark:OLE_LINK15"><span style="mso-bookmark:OLE_LINK10"><span style="color:red">3</span></span></span></span></span></span></span></span></span><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK15"><span style="mso-bookmark:OLE_LINK10"><span style="color:red">1
March 2019 – <a name="OLE_LINK30"></a><a name="OLE_LINK29"><span style="mso-bookmark:OLE_LINK30">Deadline for manuscript submission</span></a><o:p></o:p></span></span></span></span></span></span></span></p>
</div>
<span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#00000A;mso-fareast-language:EN-US"><br clear="all" style="page-break-before:auto">
</span>
<div class="WordSection2">
<p class="MsoNoSpacing"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK15"><span style="mso-bookmark:OLE_LINK10"><span style="color:red">30
June 2019 – Reviewer’s comments to authors <o:p></o:p></span></span></span></span></span></span></span></p>
<p class="MsoNoSpacing"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK15"><span style="mso-bookmark:OLE_LINK10"><span style="color:red">31
August 2019 – Submission of revised papers<o:p></o:p></span></span></span></span></span></span></span></p>
<p class="MsoNoSpacing"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK15"><span style="mso-bookmark:OLE_LINK10"><span style="color:red">31
October 2019 – Final decision of acceptance <o:p></o:p></span></span></span></span></span></span></span></p>
<p class="MsoNoSpacing"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK15"><span style="mso-bookmark:OLE_LINK10"><span style="color:red">30
November 2019 – Camera-ready papers <o:p></o:p></span></span></span></span></span></span></span></p>
<p class="MsoNoSpacing"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK15"><span style="mso-bookmark:OLE_LINK10"><span style="color:red">December
2019</span></span></span></span></span></span></span><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="color:red">-February 2020 – Tentative
publication date</span></span></span></span></span><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><b><span style="font-size:12.0pt;color:red">
</span></b></span></span></span></span><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="color:red"><o:p></o:p></span></span></span></span></span></p>
<p class="MsoNoSpacing" style="text-align:justify"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><b><span style="font-size:7.0pt"><o:p> </o:p></span></b></span></span></span></span></p>
<p class="MsoNoSpacing" style="text-align:justify"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><b><span style="font-size:14.0pt;font-variant:small-caps;color:#1F3864">Aims
and Scope</span></b></span></span></span></span><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><b><span style="font-size:14.0pt;font-variant:small-caps">:</span></b></span></span></span></span><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><b><span style="font-size:7.0pt"><o:p></o:p></span></b></span></span></span></span></p>
<p class="MsoNormal" style="text-align:justify"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><a name="OLE_LINK11"></a><a name="OLE_LINK12"><span style="mso-bookmark:OLE_LINK11"><span lang="EN-US" style="font-size:11.0pt;font-family:"Calibri",sans-serif">Deep
neural networks have proven to be efficient learning systems for supervised and unsupervised tasks in a wide range of challenging applications. However, learning complex data representations using deep neural networks can be difficult due to problems such
as lack of data, exploding or vanishing gradients, high computational cost, or incorrect parameter initialization, among others. Transfer Learning (TL) can facilitate the learning of data representations by taking advantage of transferable features learned
by a model in a source domain, and adapting the model to a new domain. This approach has demonstrated to produce better generalization performance than random weight initialization, and has produced state-of-the-art results in signal and visual processing
tasks. Accordingly, emerging and challenging domains, such as smart and connected health (SCH), can benefit from new theoretical advancements in representation and transfer learning (RTL) methods.<o:p></o:p></span></span></a></span></span></span></span></p>
<p class="MsoNormal" style="text-align:justify"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK12"><span style="mso-bookmark:OLE_LINK11"><span lang="EN-US" style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></span></span></span></span></span></span></p>
<p class="MsoNormal" style="text-align:justify"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK12"><span style="mso-bookmark:OLE_LINK11"><span lang="EN-US" style="font-size:11.0pt;font-family:"Calibri",sans-serif">One
of the main advantages of TL is its potential to be applied in a wide range of domains and for different learning tasks. For instance, in facial affect recognition, the representations learned by a deep model trained to recognize faces in an unsupervised fashion
can be employed and improved by a second model to perform emotion recognition in supervised manner. Nonetheless, learning data representations that provide a good degree of generalization performance remains a challenge. This is due to issues such as the inherent
trade-off between retaining too much information from the input and learning universal features. Similarly, despite the obvious advantages of TL, effective use of parameters learned by a given model in a different domain is a challenge, particularly when there
is limited data in the target domain. This challenge increases when the joint distribution of the input features and output labels is different in the target domain. In addition, determining how to reject unrelated information or remove dataset bias during
TL is yet to be solved. Other limitations are caused by lack of existing theoretical approaches<a name="OLE_LINK61"></a><a name="OLE_LINK60"><span style="mso-bookmark:OLE_LINK61"> in</span></a><a name="OLE_LINK68"><span style="mso-bookmark:OLE_LINK61"><span style="mso-bookmark:OLE_LINK60">
</span></span></a>RTL capable of explaining or interpreting the learning process of deep models, or determining how to best learn a set of data representations that are ideal for a given task, whether in a regression or classification problem.
<a name="OLE_LINK54"></a><a name="OLE_LINK53"></a><a name="OLE_LINK50"></a><a name="OLE_LINK49"><span style="mso-bookmark:OLE_LINK50"><span style="mso-bookmark:OLE_LINK53"><span style="mso-bookmark:OLE_LINK54">Therefore, new n theoretical mechanisms and algorithms
are required to improve </span></span></span></a><a name="OLE_LINK57"></a><a name="OLE_LINK59"><span style="mso-bookmark:OLE_LINK57"><span style="mso-bookmark:OLE_LINK54"><span style="mso-bookmark:OLE_LINK53"><span style="mso-bookmark:OLE_LINK50"><span style="mso-bookmark:OLE_LINK49">the
performance and learning process of deep </span></span></span></span></span></a>neural networks.
<o:p></o:p></span></span></span></span></span></span></span></p>
<p class="MsoNormal" style="text-align:justify"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK12"><span style="mso-bookmark:OLE_LINK11"><span lang="EN-US" style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></span></span></span></span></span></span></p>
<p class="MsoNormal" style="text-align:justify"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK12"><span style="mso-bookmark:OLE_LINK11"><a name="OLE_LINK66"></a><a name="OLE_LINK67"><span style="mso-bookmark:OLE_LINK66"><span lang="EN-US" style="font-size:11.0pt;font-family:"Calibri",sans-serif">Despite
these constraints, RTL will play an essential role in building the next generation of intelligent systems designed to assists humans with their daily needs. Consequently, domains of great interest to human society, such as SCH, will benefit from new advancements
in RTL. For instance, one of the main challenges in designing effective SCH applications is overcoming the lack of labelled data. RTL can overcome this limitation by training a model to learn universal data representations on larger corpora in a different
domain, and then adapting the model for use in a SCH context. Similarly, RTL can be used in conjunction with generative adversarial networks to overcome class imbalance problems by generating new healthcare-related data, which can also be used to improve the
generalization performance of deep models in SCH applications. Furthermore, RTL can be used to initialize and improve the learning of deep reinforcement learning models designed for continuous learning in patient-centered cognitive support systems, among others.
Nonetheless, the use of RTL in designing SCH applications requires overcoming problems such as dataset bias or neural network co-adaptation.<o:p></o:p></span></span></a></span></span></span></span></span></span></p>
<p class="MsoNormal" style="text-align:justify"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK12"><span style="mso-bookmark:OLE_LINK11"><span style="mso-bookmark:OLE_LINK67"><span style="mso-bookmark:OLE_LINK66"><span lang="EN-US" style="font-size:11.0pt;font-family:"Calibri",sans-serif"></span></span></span><a name="OLE_LINK88"></a></span></span></span></span></span></span><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK12"><span style="mso-bookmark:OLE_LINK11"><span lang="EN-US" style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></span></span></span></span></span></span></p>
<p class="MsoNormal" style="text-align:justify"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK12"><span style="mso-bookmark:OLE_LINK11"><span lang="EN-US" style="font-size:11.0pt;font-family:"Calibri",sans-serif">This
special issue on Deep Representation and Transfer Learning <a name="OLE_LINK63">
for Smart and Connected Health</a> invites researchers and practitioners to present novel
<a name="OLE_LINK14"></a><a name="OLE_LINK13"><span style="mso-bookmark:OLE_LINK14">contributions addressing theoretical aspects of representation and transfer learning.
</span></a><a name="OLE_LINK32"></a><a name="OLE_LINK35"><span style="mso-bookmark:OLE_LINK32"><span style="mso-bookmark:OLE_LINK14"><span style="mso-bookmark:OLE_LINK13">The special issue will provide a collection of high quality research articles addressing</span></span></span></a><span style="mso-bookmark:OLE_LINK14"><span style="mso-bookmark:OLE_LINK13">
theoretical work aimed to improve the generalization performance of deep models, as well as new theory attempting to explain and interpret both concepts. State-of-the-art works on applying representation and transfer learning
<a name="OLE_LINK64"></a><a name="OLE_LINK65"><span style="mso-bookmark:OLE_LINK64">for developing smart and connected health</span></a> intelligent systems are also very welcomed.
</span></span>Topics of interest for this special issue include but are not limited to:</span></span></span></span></span></span></span><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK12"><span style="mso-bookmark:OLE_LINK11"><span lang="EN-US" style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></span></span></span></span></span></span></p>
<p class="MsoNormal"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK12"><span style="mso-bookmark:OLE_LINK11"><span lang="EN-US" style="font-size:11.0pt"><o:p> </o:p></span></span></span></span></span></span></span></p>
</div>
<span lang="EN-US" style="font-size:11.0pt;font-family:"Times New Roman",serif;color:black;mso-fareast-language:EN-US"><br clear="all" style="page-break-before:auto">
</span>
<div class="WordSection3">
<p class="MsoNormal" style="text-align:justify;text-indent:14.2pt"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK12"><span style="mso-bookmark:OLE_LINK11"><i><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#00000A">Theoretical
Methods:</span></i></span></span></span></span></span></span><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK12"><span style="mso-bookmark:OLE_LINK11"><span lang="EN-US" style="font-size:11.0pt"><o:p></o:p></span></span></span></span></span></span></span></p>
<p class="MsoListParagraph" style="margin-left:1.0cm;mso-add-space:auto;text-indent:-14.15pt;mso-list:l3 level1 lfo1">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK12"><span style="mso-bookmark:OLE_LINK11"><![if !supportLists]><span lang="EN-US" style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span lang="EN-US" style="font-size:11.0pt">Distributed representation learning;</span><span lang="EN-US"><o:p></o:p></span></span></span></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l1 level1 lfo2">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK12"><span style="mso-bookmark:OLE_LINK11"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>Transfer learning; <o:p></o:p></span></span></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l1 level1 lfo2">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK12"><span style="mso-bookmark:OLE_LINK11"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>Invariant feature learning;<o:p></o:p></span></span></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l1 level1 lfo2">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK12"><span style="mso-bookmark:OLE_LINK11"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>Domain adaptation; <o:p></o:p></span></span></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l1 level1 lfo2">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK12"><span style="mso-bookmark:OLE_LINK11"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>Neural network interpretability theory; </span></span><o:p></o:p></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l1 level1 lfo2">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>Deep neural networks; <o:p></o:p></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l1 level1 lfo2">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>Deep reinforcement learning;<o:p></o:p></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l1 level1 lfo2">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>Imitation learning; <o:p></o:p></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l1 level1 lfo2">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>Continuous domain adaptation learning; <o:p></o:p></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l1 level1 lfo2">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>Optimization and learning algorithms for DNNs;<o:p></o:p></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l1 level1 lfo2">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>Zero and one-shot learning;<a name="OLE_LINK55"></a><a name="OLE_LINK56"><span style="mso-bookmark:OLE_LINK55"><o:p></o:p></span></a></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l1 level1 lfo2">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK56"><span style="mso-bookmark:OLE_LINK55"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>Domain invariant learning;</span></span> <o:p></o:p></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l1 level1 lfo2">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>RTL in generative and adversarial learning; <o:p></o:p></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l1 level1 lfo2">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>Multi-task learning and Ensemble learning; <o:p></o:p></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.2pt;mso-list:l1 level1 lfo2">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>New learning criteria and evaluation metrics in RTL;<o:p></o:p></span></span></span></span></p>
<p class="MsoNoSpacing" style="text-align:justify;text-indent:7.1pt"><span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><i> Application Areas:
</i><o:p></o:p></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l2 level1 lfo3">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><a name="OLE_LINK58"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>Health monitoring;<o:p></o:p></a></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l2 level1 lfo3">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK58"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>Health diagnosis and interpretation;<o:p></o:p></span></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l2 level1 lfo3">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK58"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>Early health detection and prediction;<o:p></o:p></span></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l2 level1 lfo3">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK58"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>Virtual patient monitoring;<o:p></o:p></span></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l2 level1 lfo3">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK58"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>RTL in medicine; <o:p></o:p></span></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l2 level1 lfo3">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK58"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>Biomedical information processing;<o:p></o:p></span></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l2 level1 lfo3">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK58"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>Affect recognition </span>and mining;<o:p></o:p></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l2 level1 lfo3">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>Health and affective data synthesis; <o:p></o:p></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l2 level1 lfo3">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>RTL for<a name="OLE_LINK69"></a><a name="OLE_LINK70"><span style="mso-bookmark:OLE_LINK69"> virtual reality in healthcare;<o:p></o:p></span></a></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l2 level1 lfo3">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK70"><span style="mso-bookmark:OLE_LINK69"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>P</span></span><a name="OLE_LINK76"></a><a name="OLE_LINK741"></a><a name="OLE_LINK75"></a><span style="mso-bookmark:OLE_LINK76">hysiological information processing;<o:p></o:p></span></span></span></span></span></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify;text-indent:-14.15pt;mso-list:l2 level1 lfo3">
<span style="mso-bookmark:OLE_LINK3"><span style="mso-bookmark:OLE_LINK2"><span style="mso-bookmark:OLE_LINK1"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK76"><![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]>Affective human-machine interaction</span>; <o:p>
</o:p></span></span></span></span></p>
<span style="mso-bookmark:OLE_LINK1"></span><span style="mso-bookmark:OLE_LINK2"></span><span style="mso-bookmark:OLE_LINK3"></span></div>
<span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#00000A;mso-fareast-language:EN-US"><br clear="all" style="page-break-before:auto">
</span>
<div class="WordSection4"></div>
<b><span style="font-size:14.0pt;font-family:"Calibri",sans-serif;color:#00000A;mso-fareast-language:EN-US"><br clear="all" style="page-break-before:always">
</span></b>
<div class="WordSection5">
<p class="MsoNoSpacing"><span style="mso-bookmark:_Hlk529360999"><a name="OLE_LINK751"></a><b><span style="font-size:14.0pt;font-variant:small-caps;color:#1F3864">Guest Editors<o:p></o:p></span></b></span></p>
<p class="MsoNoSpacing" style="text-indent:14.2pt"><span style="mso-bookmark:_Hlk529360999"><b><i>Vasile Palade</i></b>, Coventry University, UK<o:p></o:p></span></p>
<p class="MsoNoSpacing" style="text-indent:14.2pt"><span style="mso-bookmark:_Hlk529360999"><b><i>Stefan Wermter</i></b>, University of Hamburg, Germany<o:p></o:p></span></p>
<p class="MsoNoSpacing" style="text-indent:14.2pt"><span style="mso-bookmark:_Hlk529360999"><a name="OLE_LINK8"></a><a name="OLE_LINK9"><span style="mso-bookmark:OLE_LINK8"><b><i>Ariel Ruiz-Garcia</i></b>, Coventry University, UK<o:p></o:p></span></a></span></p>
<p class="MsoNoSpacing" style="text-indent:14.2pt"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK9"><span style="mso-bookmark:OLE_LINK8"><b><i>Antonio de Padua Braga</i></b>, University of Minas Gerais, Brazil<o:p></o:p></span></span></span></p>
<p class="MsoNoSpacing" style="text-indent:14.2pt"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK9"><span style="mso-bookmark:OLE_LINK8"><b><i>Clive Cheong Took</i></b>, Royal Holloway (University of London), UK<o:p></o:p></span></span></span></p>
<p class="MsoNoSpacing" style="text-align:justify;text-indent:14.2pt"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK9"><span style="mso-bookmark:OLE_LINK8"><o:p> </o:p></span></span></span></p>
<p class="MsoNoSpacing" style="text-align:justify"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK9"><span style="mso-bookmark:OLE_LINK8"><b><span style="font-size:14.0pt;font-variant:small-caps;color:#1F3864">Submission Instructions
<o:p></o:p></span></b></span></span></span></p>
<ol style="margin-top:0cm" start="1" type="1">
<li class="MsoNoSpacing" style="text-align:justify;mso-list:l0 level1 lfo4"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK9"><span style="mso-bookmark:OLE_LINK8">Read the Information for Authors at
</span></span></span><a href="https://cis.ieee.org/publications/t-neural-networks-and-learning-systems"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK9"><span style="mso-bookmark:OLE_LINK8">https://cis.ieee.org/publications/t-neural-networks-and-learning-systems</span></span></span><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK9"><span style="mso-bookmark:OLE_LINK8"></span></span></span></a><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK9"><span style="mso-bookmark:OLE_LINK8">.<o:p></o:p></span></span></span></li><li class="MsoNoSpacing" style="text-align:justify;mso-list:l0 level1 lfo4"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK9"><span style="mso-bookmark:OLE_LINK8">Submit your manuscript at the TNNLS webpage (</span></span></span><a href="http://mc.manuscriptcentral.com/tnnls"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK9"><span style="mso-bookmark:OLE_LINK8">http://mc.manuscriptcentral.com/tnnls</span></span></span><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK9"><span style="mso-bookmark:OLE_LINK8"></span></span></span></a><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK9"><span style="mso-bookmark:OLE_LINK8">)
and follow the submission procedure. Please, clearly indicate on the first page of the manuscript and in the cover letter that the manuscript is submitted to this special issue. Send an email to the
</span></span></span><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK9"><span style="mso-bookmark:OLE_LINK8"><span style="font-size:12.0pt">guest editors Ariel Ruiz-Garcia (</span></span></span></span><a href="mailto:ariel.ruiz-garcia@coventry.ac.uk"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK9"><span style="mso-bookmark:OLE_LINK8"><span style="font-size:12.0pt">ariel.ruiz-garcia@coventry.ac.uk</span></span></span></span><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK9"><span style="mso-bookmark:OLE_LINK8"></span></span></span></a><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK9"><span style="mso-bookmark:OLE_LINK8"><span style="font-size:12.0pt">)
and Vasile Palade (</span></span></span></span><a href="mailto:vasile.palade@coventry.ac.uk"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK9"><span style="mso-bookmark:OLE_LINK8"><span style="font-size:12.0pt">vasile.palade@coventry.ac.uk</span></span></span></span><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK9"><span style="mso-bookmark:OLE_LINK8"></span></span></span></a><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK9"><span style="mso-bookmark:OLE_LINK8"><span style="font-size:12.0pt">)
</span>with subject “TNNLS special issue submission” to notify about your submission.
<o:p></o:p></span></span></span></li><li class="MsoNoSpacing" style="text-align:justify;mso-list:l0 level1 lfo4"><span style="mso-bookmark:_Hlk529360999"><span style="mso-bookmark:OLE_LINK9"><span style="mso-bookmark:OLE_LINK8">Early submissions are welcome. We will start the review process as
soon as we receive your contributions.</span></span></span><span style="mso-bookmark:OLE_LINK8"></span><span style="mso-bookmark:OLE_LINK9"></span><span style="mso-bookmark:_Hlk529360999"><b><span style="font-size:12.0pt">
</span></b> </span><span style="mso-bookmark:_Hlk529360999"><span style="color:black;background:white"> </span> <o:p></o:p></span></li></ol>
<span style="mso-bookmark:_Hlk529360999"></span>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify"><o:p> </o:p></p>
<p class="MsoNoSpacing" style="margin-left:1.0cm;text-align:justify">For any other questions please contact
<span style="font-size:12.0pt">Ariel Ruiz-Garcia (<a href="mailto:ariel.ruiz-garcia@coventry.ac.uk">ariel.ruiz-garcia@coventry.ac.uk</a>).</span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:windowtext"><o:p> </o:p></span></p>
</div>
<p style="line-height: 15.0pt;"><strong><span style="font-size: 13pt; font-family: 'Arial',sans-serif; color: #005eb8;">University of the Year for Student Experience<br>
</span></strong><span style="font-size: 9.0pt; font-family: 'Arial',sans-serif; color: black;">The Times and Sunday Times Good University Guide 2019</span></p>
<p style="mso-margin-top-alt: 3.0pt; margin-right: 0cm; margin-bottom: 3.0pt; margin-left: 0cm; line-height: 12.0pt;">
<strong><span style="font-size: 13pt; font-family: 'Arial',sans-serif; color: #005eb8;">2nd for Teaching Excellence</span></strong><br>
<span style="font-size: 9.0pt; font-family: 'Arial',sans-serif; color: black;">Times Higher Education UK (TEF) metrics ranking 2017 – Gold winner</span></p>
<p style="mso-margin-top-alt: 3.0pt; margin-right: 0cm; margin-bottom: 3.0pt; margin-left: 0cm; line-height: 12.0pt;">
<strong><span style="font-size: 13pt; font-family: 'Arial',sans-serif; color: #005eb8;">5th UK Student City</span></strong><br>
<span style="font-size: 9.0pt; font-family: 'Arial',sans-serif; color: black;">QS Best Student Cities Index 2018</span></p>
<p style="line-height: 15.0pt;"><strong><span style="font-size: 13pt; font-family: 'Arial',sans-serif; color: #005eb8;">13th in Guardian University Guide 2019</span></strong><br>
<span style="font-size: 9.0pt; font-family: 'Arial',sans-serif; color: black;">of 121 UK institutions ranked
</span></p>
<p style="margin-top: 15.0pt; line-height: 8.25pt;"><span style="font-size: 7.0pt; font-family: 'Arial',sans-serif;">NOTICE<br>
<br>
This message and any files transmitted with it is intended for the addressee only and may contain information that is confidential or privileged. Unauthorised use is strictly prohibited. If you are not the addressee, you should not read, copy, disclose or otherwise
use this message, except for the purpose of delivery to the addressee.<br>
<br>
Any views or opinions expressed within this e-mail are those of the author and do not necessarily represent those of Coventry University.</span></p>
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