Miracle-dz/UniFAR-FTUs huggingface dataset:
Representation Learning and Recovery in the ReLU Model

Rectified linear units, or ReLUs, have become the preferred activation function for artificial neural networks. In this paper we consider two basic learning problems assuming that the underlying data follow a generative model based on a ReLU-network -- a neural network with ReLU activations. As a primarily theoretical study, we limit ourselves to a single-layer network. The first problem we study corresponds to dictionary-learning in the presence of nonlinearity (modeled by the ReLU functions). Given a set of observation vectors $\mathbf{y}^i \in \mathbb{R}^d, i =1, 2, \dots , n$, we aim to recover $d\times k$ matrix $A$ and the latent vectors $\{\mathbf{c}^i\} \subset \mathbb{R}^k$ under the model $\mathbf{y}^i = \mathrm{ReLU}(A\mathbf{c}^i +\mathbf{b})$, where $\mathbf{b}\in \mathbb{R}^d$ is a random bias. We show that it is possible to recover the column space of $A$ within an error of $O(d)$ (in Frobenius norm) under certain conditions on the probability distribution of $\mathbf{b}$. The second problem we consider is that of robust recovery of the signal in the presence of outliers, i.e., large but sparse noise. In this setting we are interested in recovering the latent vector $\mathbf{c}$ from its noisy nonlinear sketches of the form $\mathbf{v} = \mathrm{ReLU}(A\mathbf{c}) + \mathbf{e}+\mathbf{w}$, where $\mathbf{e} \in \mathbb{R}^d$ denotes the outliers with sparsity $s$ and $\mathbf{w} \in \mathbb{R}^d$ denote the dense but small noise. This line of work has recently been studied (Soltanolkotabi, 2017) without the presence of outliers. For this problem, we show that a generalized LASSO algorithm is able to recover the signal $\mathbf{c} \in \mathbb{R}^k$ within an $\ell_2$ error of $O(\sqrt{\frac{(k+s)\log d}{d}})$ when $A$ is a random Gaussian matrix.
Power management in a ST integrated medium voltage grid

High penetration of distributed generation (DG) sources result in bidirectional power flow and conventional power transformer (CPT) based microgrid experiences lack of controllability features in terms of real and reactive power flow between multiple feeders. This, in turn leads to voltage rise or fall depending upon different DG operating scenarios. This paper focus on the performance improvement in a smart transformer (ST) based medium voltage (MV) microgrid with DG connected multiple feeders. The ST capability for integrating battery energy storage system (BESS) is utilized to control the power flow in a multi-feeder distribution system.
Direction and gender classification using convolutional neural network for side-view images captured from a monitored trail

Prior work by a subset of authors led to a development of optical camera platform, which has a capability of distinguishing between human and animal movement in an outdoor environment. Once the image is classified as a human, an idea of providing additional information and insights led to the exploration of gender (men vs women) and direction (left to right vs right to left) classifications. The proposed method classifies the human gender, based on the full body image oriented in a side view manner. An additional feature is to classify the direction of the movement. In the current paper, the Convolutional Neural Networks (CNNs) are used to distinguish between men and women gender classes and to identify the direction of the movement. Furthermore, different aspects of CNN are visualized (for example, attention heat maps, t-Distributed Stochastic Neighbor Embedding plot, etc.) to provide useful insights corresponding to classifications and misclassifications. Additionally, different CNN architectures were tried to figure out the best possible choice. The classification accuracies for gender (men vs women) and direction classification (left to right vs right to left) on the test data are close to 93.3% and 94%, respectively.
Acute and late toxicity of bilateral orbital irradiation in the management of primary intraocular lymphoma

Abstract Primary intraocular lymphoma (PIOL) is a rare malignancy with poor outcomes. Concerns regarding toxicity lead some clinicians to exclude orbital radiation therapy (RT). We aimed to quantify the ocular toxicity of RT in 11 PIOL patients treated with chemoimmunotherapy and bilateral orbital RT (median 36 Gy). A multidisciplinary team, including an ocular oncologist, followed patients for a median of 42 months after RT. Common adverse events included dermatitis (100%), conjunctivitis (82%), xerophthalmia (64%), and keratopathy (45%). All phakic eyes developed cataracts (100%); correction resulted in good vision recovery. New, visually significant retinopathy was observed in only one eye (<5%) and affected a patient with preexisting diabetes. This report suggests that severe, vision-threatening complications following orbital RT are uncommon. In the absence of comorbidities, orbital RT should not be withheld due to fear of vision-threatening toxicity. The risk of toxicity may be augmented by comorbidities, so an individualized approach is recommended.
Pulmonary arterial pressure in patients with myelodysplastic syndromes

Heinz Sill*, Xhylsime Kqiku-Kryeziu*, Alexander Avian, Gabor Kovacs, Sonja Gaal, Armin Zebisch and Horst Olschewski Division of Hematology, Department of Internal Medicine, Medical University of Graz, Graz, Austria; Division of Pulmonology, Department of Internal Medicine, Medical University of Graz, Graz, Austria; Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria; Ludwig Boltzmann Institute for Lung Vascular Research, Graz, Austria
An Enhanced Multilevel Authentication Protocol for VANETs

Security and privacy are both involved in vehicular communications for successful adoption and deployment of the VANET. Generally, attacks cause anomalies to the network functionality. A secure Vehicular Ad Hoc Networks (VANET) system, while exchanging information should protect the system against unauthorized message injection, message alteration, eavesdropping.In this paper, we propose, an enhanced authentication protocol called MAVnet for Multilevel authentication protocol. The proposed protocol considers the different vehicle categories and guarantees authentication, where a third party is used, which is the Authentication, Authorization and Accounting (AAA) server. The MAVnet protocol is then carried out for tracking services by managing information using the DBMS (Database Management System). The main purpose of our work is to ensure the vehicle authentication, while respecting the constraints of vehicle categories by minimizing thing overhead of communications. Two ways of simulation are performed. The first one uses AVISPA framework, where simulation results of the protocol demonstrate that it is SAFE (according to AVISPA goals). And, the second simulation uses the Java Cryptography Extension (JCE) library. The simulation results show that the privacy can be maintained better by applying the proposed architecture and it's able to lessen the number of exchanging messages and time lag. The results show the superiority of MAVnet is significantly higher than that of the certificate-based authentication protocol and the identity-based authentication protocol.
Applying deep-learning to a top-down SSVEP BMI

Brain-machine interfaces (BMIs) enable humans to control devices by modulating their brain signals. As the current BMI technology has several obstacles to overcome, additional sources of brain activity need to be explored. It seems plausible that the brain activity associated with top-down cognitive functions could open a new prospect in the field of BMIs. As top-down cognitive BMIs could exploit neural signals from more diverse networks, a deep-learning approach with complex hidden layers may provide a more optimal decoding performance. In this study, using our top-down steady-state visual evoked potential (SSVEP) paradigm (N = 20), we observed that the decoding accuracy (48.42%) of a deep-learning algorithm with a sigmoid activation function was significantly higher than that of regularized linear discriminant analysis (rLDA) with shrinkage (42.52%; t(19) = −3.183, p < 0.01), used in our previous study. Therefore, a deep-learning approach seems to be more optimized for classification in the top-down cognitive BMI paradigm.
Static deformation modeling and analysis of flexure hinges made of a shape memory alloy

The flexure hinge is a key element in compliant mechanisms to achieve continuous motion; however the motion range of a flexure hinge is severely restricted by the material’s allowable strain. Due to the superelasticity effect, shape memory alloys (SMAs) can undergo much larger strain than other metals; this means that they are excellent candidates for the fabrication of flexure hinges with a large motion range. In this paper, a simple static deformation modeling approach is proposed for a flexure hinge made of a SMA. The superelastic behavior of the SMA is described by Brinson’s constitutive model. The flexure hinge is considered as a non-prismatic cantilever beam associated with geometrical and material nonlinearities. Govern equations of the flexure hinge are derived and solved numerically by applying the nonlinear bending theory of the Euler–Bernoulli beam. Experimental tests show that the proposed modeling approach can predict the deformation of the flexure hinge precisely; the maximum relative error is less than 6.5%. Based on the static deformation model, the motion capacity, the stiffness characteristic and the rotational error of the flexure hinge are also investigated. The results reveal that the flexure hinge made of a SMA has great potential to construct compliant mechanisms with a large motion range.
Changes of plasma levels of apolipoproteins A-I , AIl , and B and their isoforms in patients with intestinal failure receiving long-term parenteral nutrition 3

We have studied the plasma lipid and apolipoprotein profiles of 19 patients with intestinal failure who are receiving long-term total parenteral nutrition (TPN). These patients had significantly reduced levels of total and HDL cholesterol and normal levels of triglycerides. Radioimmunoassay determination of apolipoproteins showed a 30% and 50% reduction in apo A-I and AII levels, respectively. Apolipoprotein B was normal in all but three patients. Isoelectric focusing showed two major isoforms of apo A-I in patients as compared with four isoforms observed in normal subjects and one major isoform of apo A-Il compared with multiple isoforms. Recent epidemiologic studies indicate that an increased apo B:apo A-I ratio may be an important factor in atherogenesis. We suggest that patients with small-bowel syndrome who are currently on TPN may be at greater risk for atherosclerosis. Since TPN has restored a reasonably normal life expectancy for these patients, long-term follow-up will likely provide answers. Am J C/in Nuir 1987;45:414-22.
Nanophotonics with subwavelength apertures: theories and applications.

This dissertation presents subwavelength optics with focus on the theory and applications of subwavelength apertures in a metal film. Two main issues regarding the optics with subwavelength apertures are investigated. As the first issue, the extraordinary optical transmission (EOT) through a single hole in a metallic waveguide is presented. A total transmission through a single subwavelength aperture is theoretically predicted for a perfect electric conductor regardless of the aperture size, without relying on aperture arrays and surface corrugations as presented in previous works. The waveguide EOT is then applied to boost the optical throughput of an apertured near-field scanning optical microscope (NSOM) probe. Using a new structure for the apertured NSOM probe which allows for waveguide EOT, the optical throughput and the damage threshold are boosted by 100× and 40× as compared to a conventional structure, and the experimental findings are backed-up by comprehensive finite-difference time-domain (FDTD) simulations. Single fluorescent molecules are scanned using the EOT apertured NSOM probe, and a spatial resolution of 62 nm is achieved. As the second issue, subwavelength apertures are found useful for optical trapping. A small dielectric particle can significantly change the optical transmission through an aperture by dielectric loading, and subsequently, a large optical force is induced
Molecular Correlates of Social Dominance: A Novel Role for Ependymin in Aggression

Theoretical and empirical studies have sought to explain the formation and maintenance of social relationships within groups. The resulting dominance hierarchies have significant fitness and survival consequences dependent upon social status. We hypothesised that each position or rank within a group has a distinctive brain gene expression profile that correlates with behavioural phenotype. Furthermore, transitions in rank position should determine which genes shift in expression concurrent with the new dominance status. We used a custom cDNA microarray to profile brain transcript expression in a model species, the rainbow trout, which forms tractable linear hierarchies. Dominant, subdominant and submissive individuals had distinctive transcript profiles with 110 gene probes identified using conservative statistical analyses. By removing the dominant, we characterised the changes in transcript expression in sub-dominant individuals that became dominant demonstrating that the molecular transition occurred within 48 hours. A strong, novel candidate gene, ependymin, which was highly expressed in both the transcript and protein in subdominants relative to dominants, was tested further. Using antibody injection to inactivate ependymin in pairs of dominant and subdominant zebrafish, the subdominant fish exhibited a substantial increase in aggression in parallel with an enhanced competitive ability. This is the first study to characterise the molecular signatures of dominance status within groups and the first to implicate ependymin in control of aggressive behaviour. It also provides evidence for indirect genetic effect models in which genotype/phenotype of an individual is influenced by conspecific interactions within a group. The variation in the molecular profile of each individual within a group may offer a new explanation of intraspecific variation in gene expression within undefined groups of animals and provides new candidates for empirical study.
Large-Scale Analysis of Framework-Specific Exceptions in Android Apps

Mobile apps have become ubiquitous. For app developers, it is a key priority to ensure their apps' correctness and reliability. However, many apps still suffer from occasional to frequent crashes, weakening their competitive edge. Large-scale, deep analyses of the characteristics of real-world app crashes can provide useful insights to guide developers, or help improve testing and analysis tools. However, such studies do not exist — this paper fills this gap. Over a four-month long effort, we have collected 16,245 unique exception traces from 2,486 open-source Android apps, and observed that framework-specific exceptions account for the majority of these crashes. We then extensively investigated the 8,243 framework-specific exceptions (which took six person-months): (1) identifying their characteristics (e.g., manifestation locations, common fault categories), (2) evaluating their manifestation via state-of-the-art bug detection techniques, and (3) reviewing their fixes. Besides the insights they provide, these findings motivate and enable follow-up research on mobile apps, such as bug detection, fault localization and patch generation. In addition, to demonstrate the utility of our findings, we have optimized Stoat, a dynamic testing tool, and implemented ExLocator, an exception localization tool, for Android apps. Stoat is able to quickly uncover three previously-unknown, confirmed/fixed crashes in Gmail and Google+; ExLocator is capable of precisely locating the root causes of identified exceptions in real-world apps. Our substantial dataset is made publicly available to share with and benefit the community.
Combining Local and Global Shape Models for Deformable Object Matching

We describe a method for modelling and locating deformable objects using a combination of global and local shape models. An object is represented as a set of patches together with a geometric model of their relative positions. The geometry is modelled with a global pose and linear shape model, together with a Markov Random Field (MRF) model of local displacements from the global model. Matching to a new image involves an alternating scheme in which an MRF inference technique selects the best candidates for each point, which are then used to update the parameters of the global pose and shape model. A cascade of increasingly complex models is used to achieve robust matching to new images. We explore the effect of model parameters on system performance and show that the proposed method achieves better accuracy than other widely used methods on standard datasets.
Amaranth oil reduces accumulation of 4-hydroxynonenal-histidine adducts in gastric mucosa and improves heart rate variability in duodenal peptic ulcer patients undergoing Helicobacter pylori eradication

Abstract Helicobacter pylori-induced oxidative stress in gastric mucosa (GM) is a milieu for the development of chronic gastritis, duodenal peptic ulcer (DPU), gastric cancer, and a number of extragastric diseases. Because our previous study revealed the accumulation of the protein adducts of lipid peroxidation product 4-hydroxynonenal (HNE) in GM, which persists after eradication of H. pylori, the aim of the study was to test whether Amaranth oil supplementation in addition to standard anti-Helicobacter treatment could prevent such accumulation of HNE in GM in H. pylori-positive DPU patients. Seventy-five patients were randomly split into two groups: group 1 – standard treatment (n = 39) and group 2 – standard treatment with additional supplementation of 1 ml of concentrated oil from amaranth seeds (Amaranthus cruenthus L., n = 36). Clinical analysis, including endoscopy with biopsies from antrum and corpus of the stomach were performed before and after the treatment, as was heart rate variability (HRV) recorded, as parameter of systemic, extragastric pathophysiological alterations in DPU patients. Improvement of clinical, endoscopic and histologic manifestations, and successful ulcer healing were observed in both the groups. Moreover, supplementation of amaranth oil in addition to standard anti-H. pylori treatment significantly reduced accumulation of HNE-histidine adducts in GM and increased HRV in DPU patients (p < .05). Therefore, standard treatments of DPU require additional therapeutic approaches, in accordance with integrative medicine principles, aiming to reduce persistence of oxidative stress, as was successfully done in our study by the use of amaranth oil.
Preparing Bengali-English Code-Mixed Corpus for Sentiment Analysis of Indian Languages

Analysis of informative contents and sentiments of social users has been attempted quite intensively in the recent past. Most of the systems are usable only for monolingual data and fails or gives poor results when used on data with code-mixing property. To gather attention and encourage researchers to work on this crisis, we prepared gold standard Bengali-English code-mixed data with language and polarity tag for sentiment analysis purposes. In this paper, we discuss the systems we prepared to collect and filter raw Twitter data. In order to reduce manual work while annotation, hybrid systems combining rule based and supervised models were developed for both language and sentiment tagging. The final corpus was annotated by a group of annotators following a few guidelines. The gold standard corpus thus obtained has impressive inter-annotator agreement obtained in terms of Kappa values. Various metrics like Code-Mixed Index (CMI), Code-Mixed Factor (CF) along with various aspects (language and emotion) also qualitatively polled the code-mixed and sentiment properties of the corpus.
High-level synthesis for reduction of WCET in real-time systems

The increasing design complexity of systems-on-chip (SoCs) requires designers to work at higher levels of abstraction. High-level synthesis (HLS) is one approach towards this. It allows designers to synthesize hardware directly from code written in a high-level programming language and to more quickly explore alternative implementations by re-running the synthesis with different optimization parameters and pragmas. HLS is particularly interesting for FPGA circuits, where different hardware implementations can easily be loaded into the target device. Another perspective on HLS is performance. Compared to executing the high-level language code on a processor, HLS can be used to create hardware that accelerates critical parts of the code. When discussing performance in the context or real-time systems, it is the worst-case execution time (WCET) of a task that matters. WCET obviously benefits from hardware acceleration, but it may also benefit from a tighter bound on the WCET. This paper explores the use of and integration of accelerators generated using HLS into a time-predictable processor intended for real-time systems. The high-level design tool, Vivado HLS, is used to generate hardware accelerators from benchmark code, and the system using the generated hardware accelerators is evaluated against the WCET of the original code. The design evaluation is carried out using the Patmos processor from the open-source T-CREST platform and implemented on a Xilinx Artix 7 FPGA. The WCET speed-up achieved is between a factor of 5 and 70.
Deriving a cardiac ageing signature to reveal MMP-9-dependent inflammatory signalling in senescence.

AIMS Cardiac ageing involves the progressive development of cardiac fibrosis and diastolic dysfunction coordinated by MMP-9. Here, we report a cardiac ageing signature that encompasses macrophage pro-inflammatory signalling in the left ventricle (LV) and distinguishes biological from chronological ageing. METHODS AND RESULTS Young (6-9 months), middle-aged (12-15 months), old (18-24 months), and senescent (26-34 months) mice of both C57BL/6J wild type (WT) and MMP-9 null were evaluated. Using an identified inflammatory pattern, we were able to define individual mice based on their biological, rather than chronological, age. Bcl6, Ccl24, and Il4 were the strongest inflammatory markers of the cardiac ageing signature. The decline in early-to-late LV filling ratio was most strongly predicted by Bcl6, Il1r1, Ccl24, Crp, and Cxcl13 patterns, whereas LV wall thickness was most predicted by Abcf1, Tollip, Scye1, and Mif patterns. With age, there was a linear increase in cardiac M1 macrophages and a decrease in cardiac M2 macrophages in WT mice; of which, both were prevented by MMP-9 deletion. In vitro, MMP-9 directly activated young macrophage polarization to an M1/M2 mid-transition state. CONCLUSION Our results define the cardiac ageing inflammatory signature and assign MMP-9 roles in mediating the inflammaging profile by indirectly and directly modifying macrophage polarization. Our results explain early mechanisms that stimulate ageing-induced cardiac fibrosis and diastolic dysfunction.