Miracle-dz/UniFAR-FTUs huggingface dataset:
Stroke Lesion Detection Using Convolutional Neural Networks

Stroke is an injury that affects the brain tissue, mainly caused by changes in the blood supply to a particular region of the brain. As consequence, some specific functions related to that affected region can be reduced, decreasing the quality of life of the patient. In this work, we deal with the problem of stroke detection in Computed Tomography (CT) images using Convolutional Neural Networks (CNN) optimized by Particle Swarm optimization (PSO). We considered two different kinds of strokes, ischemic and hemorrhagic, as well as making available a public dataset to foster the research related to stroke detection in the human brain. The dataset comprises three different types of images for each case, i.e., the original CT image, one with the segmented cranium and an additional one with the radiological density's map. The results evidenced that CNN's are suitable to deal with stroke detection, obtaining promising results.
Epitope resurfacing on dengue virus-like particle vaccine preparation to induce broad neutralizing antibody

Dengue fever is caused by four different serotypes of dengue virus (DENV) which is the leading cause of worldwide arboviral diseases in humans. Virus-like particles (VLPs) containing flavivirus prM/E proteins have been demonstrated to be a potential vaccine candidate; however, the structure of dengue VLP is poorly understood. Herein VLP derived from DENV serotype-2 were engineered becoming highly matured (mD2VLP) and showed variable size distribution with diameter of ~31 nm forming the major population under cryo-electron microscopy examination. Furthermore, mD2VLP particles of 31 nm diameter possess a T = 1 icosahedral symmetry with a groove located within the E-protein dimers near the 2-fold vertices that exposed highly overlapping, cryptic neutralizing epitopes. Mice vaccinated with mD2VLP generated higher cross-reactive (CR) neutralization antibodies (NtAbs) and were fully protected against all 4 serotypes of DENV. Our results highlight the potential of ‘epitope-resurfaced’ mature-form D2VLPs in inducing quaternary structure-recognizing broad CR NtAbs to guide future dengue vaccine design.
Finite-Time  $\mathcal{L}_{2}$  Leader–Follower Consensus of Networked Euler–Lagrange Systems With External Disturbances

This paper is concerned with finite-time <inline-formula> <tex-math notation="LaTeX">$ {\mathcal {L}_{2}}$ </tex-math></inline-formula> leader–follower consensus of networked Euler–Lagrange systems in the presence of external disturbances. A distributed finite-time <inline-formula> <tex-math notation="LaTeX">$ {\mathcal {L}_{2}}$ </tex-math></inline-formula> control protocol is proposed by using backstepping design such that a group of follower agents modeled by Euler–Lagrange systems can follow a desired leader agent and achieve leader-follower consensus in finite time. Moreover, the finite-time <inline-formula> <tex-math notation="LaTeX">$ {\mathcal {L}_{2}}$ </tex-math></inline-formula> gain is less than or equal to a prescribed value. A simulation example of a network composed of seven two-link manipulators is given to show the effectiveness of the theoretical results.
Exploring Data Augmentation to Improve Music Genre Classification with ConvNets

In this work we address the automatic music genre classification as a pattern recognition task. The content of the music pieces were handled in the visual domain, using spectrograms created from the audio signal. This kind of image has been successfully used in this task since 2011 by extracting handcrafted features based on texture, since it is the main visual attribute found in spectrograms. In this work, the patterns were described by representation learning obtained with the use of convolutional neural network (CNN). CNN is a deep learning architecture and it has been widely used in the pattern recognition literature. Overfitting is a recurrent problem when a classification task is addressed by using CNN, it may occur due to the lack of training samples and/or due to the high dimensionality of the space. To increase the generalization capability we propose to explore data augmentation techniques. In this work, we have carefully selected strategies of data augmentation that are suitable for this kind of application, which are: adding noise, pitch shifting, loudness variation and time stretching. Experiments were conducted on the Latin Music Database (LMD), and the best obtained accuracy overcame the state of the art considering approaches based only in CNN.
Exploring Sentence Vector Spaces through Automatic Summarization

Given vector representations for individual words, it is necessary to compute vector representations of sentences for many applications in a compositional manner, often using artificial neural networks. Relatively little work has explored the internal structure and properties of such sentence vectors. In this paper, we explore the properties of sentence vectors in the context of automatic summarization. In particular, we show that cosine similarity between sentence vectors and document vectors is strongly correlated with sentence importance and that vector semantics can identify and correct gaps between the sentences chosen so far and the document. In addition, we identify specific dimensions which are linked to effective summaries. To our knowledge, this is the first time specific dimensions of sentence embeddings have been connected to sentence properties. We also compare the features of different methods of sentence embeddings. Many of these insights have applications in uses of sentence embeddings far beyond summarization.
CD8+ T-cells contribute to lesion stabilization in advanced atherosclerosis by limiting macrophage content and CD4+ T-cell responses

AIMS T lymphocytes play an important role in atherosclerosis development, but the role of the CD8+ T-cell remains debated, especially in the clinically relevant advanced stages of atherosclerosis development. Here, we set out to determine the role of CD8+ T-cells in advanced atherosclerosis. METHODS AND RESULTS Human endarterectomy samples analysed by flow cytometry showed a negative correlation between the percentage of CD8+ T-cells and macrophages, suggesting a possible protective role for these cells in lesion development. To further test this hypothesis, LDLr-/- mice were fed a western-type diet (WTD) for 10 weeks to induce atherosclerosis, after which they received CD8α-depleting or isotype control antibody for 6 weeks. Depletion of CD8+ T-cells in advanced atherosclerosis resulted in less stable lesions, with significantly reduced collagen content in the trivalve area, increased macrophage content and increased necrotic core area compared with controls. Mechanistically, we observed that CD8 depletion specifically increased the fraction of Th1 CD4+ T-cells in the lesions. Treatment of WTD-fed LDLr-/- mice with a FasL-neutralizing antibody resulted in similar changes in macrophages and CD4+ T-cell skewing as CD8+ T-cell depletion. CONCLUSION These findings demonstrate for the first time a local, protective role for CD8+ T-cells in advanced atherosclerosis, through limiting accumulation of Th1 cells and macrophages, identifying a novel regulatory mechanism for these cells in atherosclerosis.
The Impact of Closed Incision Negative Pressure Therapy on Postoperative Breast Reconstruction Outcomes

Background: Studies report that incision management with closed incision negative pressure therapy (ciNPT) may provide clinical benefits, including protecting surgical incisions, for postsurgical closed incisions (eg, orthopedic, sternotomy, and colorectal). This retrospective analysis compared postoperative outcomes in patients who received ciNPT versus standard of care (SOC) for incision management after breast reconstruction postmastectomy. Methods: Patient demographics, chemotherapy exposure, surgical technique, ciNPT use, number of drains, time to drain removal, and 90-day postoperative complication rates were analyzed from records of 356 patients (ciNPT = 177, SOC = 179) with 665 closed breast incisions (ciNPT = 331, SOC = 334). Results: Overall complication rate was 8.5% (28/331) in ciNPT group compared with 15.9% (53/334) in SOC group (P = 0.0092). Compared with the SOC group, the ciNPT group had significantly lower infection rates [7/331 (2.1%) versus 15/334 (4.5%), respectively; P = 0.0225], dehiscence rates [8/331 (2.4%) versus 18/334 (5.4%), respectively; P = 0.0178], necrosis rates [17/331 (5.1%) versus 31/334 (9.3%), respectively; P = 0.0070], and seroma rates [6/331 (1.8%) versus 19/334 (5.7%), respectively; P = 0.0106]. The ciNPT group required significantly fewer returns to operating room compared with the SOC group [8/331 (2.4%) versus 18/334 (5.4%), respectively; P = 0.0496]. Time to complete drain removal per breast for ciNPT versus SOC groups was 9.9 versus 13.1 days (P < 0.0001), respectively. Conclusions: Patients who received ciNPT over closed incisions following postmastectomy breast reconstruction experienced a shorter time to drain removal and significantly lower rates of infection, dehiscence, necrosis, and seromas, compared with the SOC group. Randomized controlled studies are needed to corroborate the findings in our study.
Automatic Chromosome Classification using Deep Attention Based Sequence Learning of Chromosome Bands

Chromosome Karyotyping refers to the task of segmenting and classifying individual chromosome images obtained from stained cell images microphotographed during the metaphase stage of cell division. The karyotyped images are useful for diagnosis of genetic disorders such as down syndrome, turner syndrome and certain types of cancers. In many hospitals and labs, a significant amount of manual effort and time is spent on segmenting and classifying the individual chromosome images. Recently, deep learning models have been applied to automate this task with promising results. An important characteristic of a chromosome is the presence of sequence of dark and light bands produced by giemsa staining which is used by cytogeneticists to manually perform karyotyping. We propose Residual Convolutional Recurrent Attention Neural Network (Res-CRANN) which exploits this property of band sequence for chromo-some classification. Res-CRANN is end-to-end trainable in which a sequence of feature vectors, extracted from the feature maps produced by convolutional layers of Residual neural networks (ResNet) is fed into Recurrent Neural Networks (RNN) and subsequently, an attention mechanism is applied on top of RNN output sequences which are further classified into one of the 24 labels. The attention mechanism after recurrent layers facilitates the network to learn to pay selective attention to the sequence of bands and relate them to different classes of chromosomes. We demonstrate the proposed architecture’s efficacy on a publicly available Bioimage chromosome classification dataset and observe that our model outperforms the baseline models created using traditional deep convolutional neural network and ResNet-50 by approximately 3% Top-1 classification accuracy.
A Hybrid Sampling Method Based on Safe Screening for Imbalanced Datasets with Sparse Structure

Learning from imbalanced datasets is challenging due to the existence of imbalanced class distribution and other problems such as class overlapping, high dimensionality of the dataset and small sample size. This paper focuses on handling the class imbalanced problem with sparsity. A hybrid sampling method of “Under-sampling + Over-sampling” is proposed. Under-sampling selects informative instances and features from the original dataset, whereas Over-sampling balances the under-sampled dataset. Considering that safe double screening can quickly identify and remove non-informative instances and features from the dataset with sparse structure, it is adopted as Under-sampling in the hybrid sampling method. Under-sampling based on safe double screening can obtain all of the boundary instances and the informative features for classification by utilizing sparse structure of datasets. Experimental results show that safe double screening can reduce the number of instances and features and capture the true imbalance ratio of a dataset. The classifiers built on the sampled datasets by using the hybrid sampling method obtain better classification performance, especially for the data in the minority class. This hybrid sampling method is suitable for constructing a classifier based on decision boundary on sparse imbalanced dataset in which the number of features is large.
Report of the 2017-2018 Student Affairs Standing Committee

EXECUTIVE SUMMARY The 2017-2018 American Association of Colleges of Pharmacy (AACP) Student Affairs Standing Committee addressed charges related to student wellness and resilience and identified ways where AACP can assist member organizations to build positive wellbeing in students. The Committee report provides nine recommendations to AACP, three suggestions for colleges and schools of pharmacy, and one proposed policy statement related to student wellness and resilience. The report focuses on themes of consequences of burnout and declining resilience, culture shift around wellness, creating community around times of grief, partnerships with member organizations to create campus cultures that promote overall wellbeing and strategies to help students to manage stress in healthy ways. Committee members challenge AACP, and other professional organizations, to include the student voice when future programs and strategies are developed. Finally, this report provides future recommendations for the Student Affairs Standing Committee.
The Economics and Management of Non-Traditional Trademarks

Economists have largely neglected the phenomenon of NTTMs and its consequences for society so far, partly because of their limited interest in the social returns of trademarks in general. After reviewing the handful of economic studies on this matter, I present a first systematic empirical analysis of the extent of NTTM filings, with a focus on the number of filings, their nature, and the actors behind those filings. Using data from the USPTO, I find that NTTMs are steadily increasing, they are filed by very different types of firms, but they remain a relative small phenomenon, as compared to all trademark filings. I conclude by discussing avenues for further research.
Examining Leakage of Access Counts in ORAM Constructions

Oblivious RAM is a cryptographic primitive that embodies one of the cornerstones of privacy-preserving technologies for database protection. While any Oblivious RAM (ORAM) construction offers access pattern hiding, there does not seem to be a construction that is safe against the potential leakage due to knowledge about the number of accesses performed by a client. Such leakage constitutes a privacy violation, as client data may be stored in a domain specific fashion. In this work, we examine this leakage by considering an adversary that can probe the server that stores an ORAM database, and who takes regular snapshots of it. We show that even against such a weak adversary, no major ORAM architecture is resilient, except for the trivial case, where the client scans the whole database in order to access a single element. In fact, we argue that constructing a non-trivial ORAM that is formally resilient seems impossible. Moreover, we quantify the leakage of different constructions to show which architecture offers the best privacy in practice.
The Scalable Version of Probabilistic Linear Discriminant Analysis and Its Potential as A Classifier for Audio Signal Classification

Probabilistic Linear Discriminant Analysis (PLDA) has exhibited good performance in face recognition and speaker verification. However, it is not widely used as a general-purpose classifier. The major limitation of PLDA lies in that, in the original formulation, the modeling part and the prediction part require the inversion of large matrices, whose sizes are proportional to the number of training vectors in a class. The original formulation of PLDA is not scalable if there are many training vectors, because the matrices will become too large to be inverted. In the literature, some scalable versions for the modeling part have been proposed. In this paper, we propose the scalable version for the prediction part, which completes the scalable version of PLDA. This makes PLDA able to handle a large number of training data, enabling PLDA to be used as a general-purpose classifier for different classification tasks. We then apply PLDA as the classifier to three different audio signal classification tasks, and compare its performance with Support Vector Machine (SVM), which is a widely used general-purpose classifier. Experimental results show that PLDA performs very well and can be even better than SVM, in terms of classification accuracy.
Dynamic Effect of Decreased Connecting Stiffness for Flexible Robotic Manipulator Based on Vibration Energy Distribution Chracterisitic

This paper proposes an approach to reveal the dynamiceffect of decreased connecting stiffness for the flexible robotic manipulator (FRM)based on vibration energy distribution characteristic. Firstly, the coupled dynamic model of the FRM system is established. Based onthe established coupled dynamic model, the steady-state power flow isstudied to conduct a comprehensive evaluation for the dynamic characteristics of the FRM with varyingconnecting stiffness. Base on the results, dynamic effect of the decreased stiffness connecting jointsis revealed and it indicates that the connecting stiffness decreasehas significant influence on the dynamic characteristic of the FRM and should be accurately controlled.
Cross-Domain Image-Based 3D Shape Retrieval by View Sequence Learning

We propose a cross-domain image-based 3D shape retrieval method, which learns a joint embedding space for natural images and 3D shapes in an end-to-end manner. The similarities between images and 3D shapes can be computed as the distances in this embedding space. To better encode a 3D shape, we propose a new feature aggregation method, Cross-View Convolution (CVC), which models a 3D shape as a sequence of rendered views. For bridging the gaps between images and 3D shapes, we propose a Cross-Domain Triplet Neural Network (CDTNN) that incorporates an adaptation layer to match the features from different domains better and can be trained end-to-end. In addition, we speed up the triplet training process by presenting a new fast cross-domain triplet neural network architecture. We evaluate our method on a new image to 3D shape dataset for category-level retrieval and ObjectNet3D for instance-level retrieval. Experimental results demonstrate that our method outperforms the state-of-the-art approaches in terms of retrieval performance. We also provide in-depth analysis of various design choices to further reduce the memory storage and computational cost.
Active Learning With Realistic Data - A Case Study

Machine learning systems learn from data. It is not rare that those data are grouped into categories (also called classes). Thus, one of the goals of a learning algorithm is to find out and understand how to assign new, previously unknown data to the correct class. But, in many cases data is available or can be gathered at low costs, whereas acquiring the corresponding category (or class) involves high costs. This is precisely where active learning (AL) comes into its own: In order to reduce the annotation costs, it allows the learning system to deliberately select the samples which should be annotated. Subsequently, the selected samples are presented to an entity (e.g., humans, simulation systems, etc.), generally addressed under the term oracle, that provides the corresponding classes. Afterwards, the knowledge base of the learner is updated and, depending on a stopping criterion, new labels are queried or not. Such a system is self-aware of its own imperfection, thus, it uses a selection strategy to determine the next most informative sample. Hitherto, it has been shown that AL is a powerful paradigm that evinces the desired results, provided that the oracles are omniscient. But, human oracles are prone to error, so for that reason we can ask ourself: Does AL still work with error prone and uncertain human annotators? In this article we present the results of an active learning case study conducted on 30000 images labeled by two humans and propose a new type of labeling for better solving AL problems with error-prone annotators.
Spotted Fever Group Rickettsiae in Inner Mongolia, China, 2015–2016

We found Rickettsia raoultii infection in 6/261 brucellosis-negative patients with fever of unknown origin in brucellosis-endemic Inner Mongolia, China. We further identified Hyalomma asiaticum ticks associated with R. raoultii, H. marginatum ticks associated with R. aeschlimannii, and Dermacentor nuttalli ticks associated with both rickettsiae species in the autonomous region.
A Network-Based High Level Data Classification Technique

In machine learning, traditional data classification techniques analyze only physical features of the input data (e.g., distance or distribution) in order to identify the main differences among them. This type of approach is referred to as low level classification. However, the human (animal) brain is able to perform not only low orders of learning, but it is also able to identify patterns according to the semantic meaning of the input data. Data classification that considers both physical attributes and also the pattern formation, is referred to as high level classification. Previous high level classification techniques require a low level technique to work together. Such an approach does not fully highlight the ability of feature extraction embedded in high-level schemes. In this paper, we propose a pure network- based high level classification technique which aims to identify the classes of new instances by detecting and comparing the impact that each one of them has on the topological structure of the network components, which represent each class of the input data set. Eight artificially generated data sets, along with other nine different real classification data sets, were used in order to test this technique, as well as to compare its performance with those obtained by nine traditional and well-known classification models. The results of these tests are very stimulating, indicating that the novel technique proposed in this work may have great potential for further development and application. Moreover, the peculiarity of the concept of pattern based classification provides a new general approach for raw data feature extraction.
Anxious Learning in Real-Time Heuristic Search

Real-time heuristic search methods are used when planning time available per agent’s move is severely limited (e.g., while pathfinding in a video game). Such agents interleave planning and plan execution. As the agent has to move before a complete plan is computed, it is prone to be misguided by inaccuracies in its heuristic. To get out of heuristic depressions, such agents update their heuristic over time. The usual update process requires multiple state revisits which can make the agent appear irrational to the player. To alleviate such map "scrubbing" we propose a new learning mechanism inspired by the psychological notion of anxiety. Our agent maintains a level of anxiety which increases due to state revisits and decays naturally over time. Agent’s anxiety causes it to update the heuristic more aggressively thus filling heuristic depressions quicker. Such anxiety-accelerated learning can be used on top of other real-time heuristic search techniques. Empirical evaluation on video-game pathfinding benchmarks demonstrates benefits for the average solution quality when the new mechanism is used by itself or in combination with expendable state marking.