Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolutionary journey of the retroviral restriction gene Fv1

Significance We have charted the evolution of the capsid-binding retroviral restriction factor Fv1 through murid evolution, extending its age to ∼45 million years. Functionality can be found outside of the genus Mus, and shared signatures of positive selection are visible across species. Modeling suggests that maintenance for these extended periods can only be parsimoniously explained by repeated selection events—waves of retroviral infection throughout murid evolution. Our results complement and extend findings with TRIM5α and suggest that conserved features of retroviral capsid lattice assemblies may be common targets in convergent evolution of intrinsic defenses to retroviral infection. Functional constraints on capsid structure may prevent effective escape of host factors and result in cyclical coevolution, which is visible in the evolution of Fv1. Both exogenous and endogenous retroviruses have long been studied in mice, and some of the earliest mouse studies focused on the heritability of genetic factors influencing permissivity and resistance to infection. The prototypic retroviral restriction factor, Fv1, is now understood to exhibit a degree of control across multiple retroviral genera and is highly diverse within Mus. To better understand the age and evolutionary history of Fv1, a comprehensive survey of the Muroidea was conducted, allowing the progenitor integration to be dated to ∼45 million years. Intact coding potential is visible beyond Mus, and sequence analysis reveals strong signatures of positive selection also within field mice, Apodemus. Fv1’s survival for such a period implies a recurring and shifting retroviral burden imparting the necessary selective pressures—an influence likely also common to analogous factors. Regions of Fv1 adapt cooperatively, highlighting its preference for repeated structures and suggesting that this functionally constrained aspect of the retroviral capsid lattice presents a common target in the evolution of intrinsic immunity.
83-ps Timing Jitter With a Red-Enhanced SPAD and a Fully Integrated Front End Circuit

In this letter, we present the timing performance of an optimized red-enhanced single-photon avalanche diode (RE-SPAD) manufactured with a fully custom fabrication process. The detector has been characterized with an external CMOS front end circuit able to read out the RE-SPAD avalanche current and provide the timing information with state-of-the-art jitter. In particular, the careful minimization of the RE-SPAD capacitive parasitics, along with the transimpedance architecture and the gigahertz bandwidth of the front end circuit, allowed the achievement of a timing jitter as low as 83 ps full width at half maximum that increases only up to 103 ps for a detection threshold as high as 600 mV. This feature makes it possible to implement high-threshold detection systems insensitive to the electrical disturbances coming from other SPADs integrated in the same chip, demonstrating that the extension of this work to multichannel systems is feasible.
Robust Real-Time 3D Person Detection for Indoor and Outdoor Applications

Fast and robust person detection is one of the most important tasks for robotic applications involving human interaction. Particularly in mobile robotics this task is still challenging. Though there are already reliable and real-time capable approaches, they are usually computationally expensive. They either require GPUs or multiple CPU cores in order to work properly. Furthermore, some of the approaches are designed for special environments and sensor types, which reduces general applicability. In this work, we present a robust, generic and lightweight solution for real-time 3D person detection. Since our approach requires only a single CPU thread, it can be run as a background process and is suitable for smaller robotic systems. We demonstrate applicability to indoor and outdoor scenarios using different 3D sensor types separately. Moreover, we are able to show that the proposed method outperforms other state-of-the-art approaches, including a DCNN.
Efficacy of Multiuser Massive Miso Wireless Energy Transfer Under iq Imbalance and Channel Estimation Errors Over Rician Fading

We investigate the practical realization of energy beamforming gains in the downlink wireless power transfer from a massive antenna radio frequency (RF) source to multiple single antenna energy harvesting (EH) users. Assuming channel reciprocity for the uplink and downlink channels undergoing Rician fading, we first obtain the least-squares and linear-minimum-mean-square-error channel estimates using the energy-constrained pilot signal transmission from EH users. Due to the usage of low cost hardware at the users and for realizing massive antenna system at the RF source, these estimates are strongly influenced by the transmitter and receiver in-phase-and-quadrature-phase imbalance (IQI). Using these channel estimates, we next derive the harvested power at each user by applying the source transmit precoding that maximizes the sum harvested power among the users. Selected results generated considering practical RF EH system parameters show that IQI and channel estimation errors can lead to about 30% degradation in the sum EH performance.
MeshCNN: a network with an edge

Polygonal meshes provide an efficient representation for 3D shapes. They explicitly captureboth shape surface and topology, and leverage non-uniformity to represent large flat regions as well as sharp, intricate features. This non-uniformity and irregularity, however, inhibits mesh analysis efforts using neural networks that combine convolution and pooling operations. In this paper, we utilize the unique properties of the mesh for a direct analysis of 3D shapes using MeshCNN, a convolutional neural network designed specifically for triangular meshes. Analogous to classic CNNs, MeshCNN combines specialized convolution and pooling layers that operate on the mesh edges, by leveraging their intrinsic geodesic connections. Convolutions are applied on edges and the four edges of their incident triangles, and pooling is applied via an edge collapse operation that retains surface topology, thereby, generating new mesh connectivity for the subsequent convolutions. MeshCNN learns which edges to collapse, thus forming a task-driven process where the network exposes and expands the important features while discarding the redundant ones. We demonstrate the effectiveness of MeshCNN on various learning tasks applied to 3D meshes.
Iterative Deep Neural Networks for Speaker-Independent Binaural Blind Speech Separation

In this paper, we propose an iterative deep neural network (DNN)-based binaural source separation scheme, for recovering two concurrent speech signals in a room environment. Besides the commonly-used spectral features, the DNN also takes non-linearly wrapped binaural spatial features as input, which are refined iteratively using parameters estimated from the DNN output via a feedback loop. Different DNN structures have been tested, including a classic multilayer perception regression architecture as well as a new hybrid network with both convolutional and densely-connected layers. Objective evaluations in terms of PESQ and STOI showed consistent improvement over baseline methods using traditional binaural features, especially when the hybrid DNN architecture was employed. In addition, our proposed scheme is robust to mismatches between the training and testing data.
A Comparative Analysis of Depressive Symptomatology Among Adolescent Male and Female Students

Abstratct – The present investigation was intended to compare the adolescent male and female students on depression. Multistage random sampling technique was used to draw the sample from five districts of Kashmir valley. The sample for the present study comprised of 500 adolescent students (250 male and 250 female) studying in various high and higher secondary schools of Kashmir. Data was collected by using Reynolds Adolescent Depression Scale-Second Edition (2002). Data was analysed by using Mean, S.D. and t-test. The results of the study revealed that adolescent male and female students differ significantly on depression.
Increasing photoperiod stimulates the initiation of spring migratory behaviour and physiology in a facultative migrant, the pine siskin

The transition to a migratory state involves coordinated changes in physiology and behaviour. In species with regular, predictable (obligate) migrations, increasing day length triggers the expression of a spring migratory state and androgens play an important role in stimulating its development. By contrast, we know little about the environmental cues and endocrine mechanisms that regulate migration in species with less predictable (facultative) migrations. Here, we tested whether photoperiod stimulates a migratory state in a facultative nomadic migrant, the pine siskin (Spinus pinus). We exposed wintering birds to either a naturally increasing or short-day photoperiod and measured physiological and behavioural changes indicative of a migratory state. We also examined changes in circulating hormones that may play a role in the migratory transition. Natural-day, but not short-day, birds displayed physiological preparations for migration, including increases in fat deposition, and showed increased levels of migratory restlessness. We found no evidence for a role of corticosterone in the migratory transition, but testosterone may be important. This study is the first experimental test of the role of photoperiod in regulating facultative migration and demonstrates that the predictive cue used by many obligate migrants to time spring migration is also important in a facultative migrant.
Dietary Lipids Affect the Onset of Hibernation in the Garden Dormouse (Eliomys quercinus): Implications for Cardiac Function

Dietary lipids strongly influence patterns of hibernation in heterotherms. Increased dietary uptake of n-6 polyunsaturated fatty acids (PUFAs), particularly of linoleic acid (LA, C18:2 n-6), enables animals to reach lower body temperatures (Tb), lengthens torpor bout duration, and results in lower energy expenditure during hibernation. Conversely, dietary n-3 PUFA impacts negatively on hibernation performance. PUFA in surrounding phospholipids (PLs) presumably modulate the temperature-dependent activity of the sarcoplasmic reticulum (SR) Ca2+ ATPase 2 (SERCA2) and thus determine the threshold Tb still allowing proper heart function during torpor. We tested the effect of diets enriched with 10% of either corn oil (“CO,” high n-6 PUFA, e.g., LA) or menhaden oil [“MO,” long-chain n-3 PUFA, e.g., docosahexaenoic acid (DHA)] on hibernation performance and SERCA2 activity levels during torpor in garden dormice, an insectivorous, fat-storing hibernator. Prior to hibernation, individuals fed the MO diet showed an almost nine-times higher DHA levels and 30% lower LA proportions in white adipose tissue (WAT), reflecting the fatty acid composition of SR membranes, compared to CO-diet fed animals. When fed the MO diet, dormice significantly delayed their mean onset of hibernation by almost 4 days (range: 0–12 days), compared with CO-diet fed animals. Hibernation onset correlated positively with WAT-DHA levels and negatively with WAT-LA proportions prior to hibernation. Subsequently, hibernating patterns were similar between the two dietary groups, despite a significant difference in WAT-LA but not in WAT-DHA levels in mid-hibernation. SR-PL fatty acid composition and SERCA2 activity were identical in torpid individuals from the two dietary groups in mid-hibernation. In line with our previous findings on Syrian hamsters, a granivorous, food-storing hibernator, SERCA2 activity correlated positively with LA and negatively with DHA levels of SR-PL in torpid dormice, although SERCA2 activity was about three-times higher in garden dormice than in Syrian hamsters at similar PL-DHA proportions. Similarly, minimal Tb during torpor decreased as SERCA2 activity increased. We conclude that: (1) fatty acid composition of SR membranes modulates cardiac SERCA2 activity, hence determining the minimum Tb tolerated by hibernators, and (2) high DHA levels prevent hibernators from entering into torpor, but the critical levels differ substantially between species.
Squib: Reproducibility in Computational Linguistics: Are We Willing to Share?

This study focuses on an essential precondition for reproducibility in computational linguistics: the willingness of authors to share relevant source code and data. Ten years after Ted Pedersen’s influential “Last Words” contribution in Computational Linguistics, we investigate to what extent researchers in computational linguistics are willing and able to share their data and code. We surveyed all 395 full papers presented at the 2011 and 2016 ACL Annual Meetings, and identified whether links to data and code were provided. If working links were not provided, authors were requested to provide this information. Although data were often available, code was shared less often. When working links to code or data were not provided in the paper, authors provided the code in about one third of cases. For a selection of ten papers, we attempted to reproduce the results using the provided data and code. We were able to reproduce the results approximately for six papers. For only a single paper did we obtain the exact same results. Our findings show that even though the situation appears to have improved comparing 2016 to 2011, empiricism in computational linguistics still largely remains a matter of faith. Nevertheless, we are somewhat optimistic about the future. Ensuring reproducibility is not only important for the field as a whole, but also seems worthwhile for individual researchers: The median citation count for studies with working links to the source code is higher.
Feature Selection Mechanism in CNNs for Facial Expression Recognition

Facial Expression Recognition (FER) has been a challenging problem in computer vision for many decades, mainly due to the high-level variation of face geometry and facial appearance. In this paper, we propose a feature selection network (FSN) to automatically extract and ﬁlter facial features by embedding a feature selection mechanism inside the AlexNet. The designed feature selection mechanism effectively ﬁlters irrelevant features and emphasises correlated features according to learned feature maps. Experiment re-sults on several databases demonstrate that the FSN outperforms the AlexNet by a large margin and achieves comparable results with the state-of-the-art methods. Furthermore, the FSN also shows improved generalisation ability over the AlexNet in the cross validation experiment of different datasets.
Dietary Deoxynivalenol (DON) May Impair the Epithelial Barrier and Modulate the Cytokine Signaling in the Intestine of Atlantic Salmon (Salmo salar)

Impaired growth, immunity, and intestinal barrier in mammals, poultry, and carp have been attributed to the mycotoxin deoxynivalenol (DON). The increased use of plant ingredients in aquaculture feed implies a risk for contamination with mycotoxins. The effects of dietary DON were explored in 12-month-old Atlantic salmon (Salmo salar) (start weight of 58 g) that were offered a standard feed with non-detectable levels of mycotoxins (control group) or 5.5 mg DON/kg feed (DON group). Each group comprised two tanks with 25 fish per tank. Five fish from each tank were sampled eight weeks after the start of the feeding trial, when mean weights for the control and DON groups were 123.2 g and 80.2 g, respectively. The relative expression of markers for three tight junction proteins (claudin 25b, occludin, and tricellulin) were lower, whereas the relative expression of a marker for proliferating cell nuclear antigen was higher in both the mid-intestine and the distal intestine in fish fed DON compared with fish from the control group. The relative expression of markers for two suppressors of cytokine signaling (SOCS1 and SOCS2) were higher in the distal intestine in fish fed DON. There was no indication of inflammation attributed to the feed in any intestinal segments. Our findings suggest that dietary DON impaired the intestinal integrity, while an inflammatory response appeared to be mitigated by suppressors of cytokine signaling. A dysfunctional intestinal barrier may have contributed to the impaired production performance observed in the DON group.
Ensemble Clustering via Learning Representations from Auto-Encoder

Representation learning is central to clustering analysis and visualization. However, linear dimension reduction methods cannot model high dimensional and sparse feature spaces, thus may neglect some important information during feature representation. In this paper, we focus on developing effective models to capture non-linear and most salient features for clustering analysis, and propose an ensemble framework ECLRA to fuse different layer representations from auto-encoders. To further improve the clustering performance and fully realize the potential of ensemble learning, we also design a strategy to generate enough basic partitions, which turns out to be very effective. Experimental results demonstrate that ECLRA can significantly improve clustering performance compared with baseline methods, and are fairly robust on imbalanced datasets.
GM-PHD Filter Based Online Multiple Human Tracking Using Deep Discriminative Correlation Matching

In this paper, we propose deep discriminative correlation matching within the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter for online multiple human tracking. In this matching scheme, we mainly exploit the Convolutional Neural Network (CNN) based Discriminative Correlation Filter (DCF) as a target-specific classifier to discriminate the desired target from background and remaining targets. DCFs are learned through the extracted features obtained from the outputs of the last convolutional layers which are capable to encode the target appearances with better discriminativity and robustness to appearance changes. Moreover, we present a hybrid likelihood function that fuses the spatio-temporal relation and correlation matching score to collaboratively enhance the PHD association step. Experimental results on the MOT17 Challenge benchmark [1] confirm the improved performance of our proposed method as compared with other state-of-the-art techniques.
Enhancing the Description-to-Behavior Fidelity in Android Apps with Privacy Policy

Since more than 96 percent of mobile malware targets the Android platform, various techniques based on static code analysis or dynamic behavior analysis have been proposed to detect malicious apps. As malware is becoming more complicated and stealthy, recent research proposed a promising detection approach that looks for the inconsistency between an app's permissions and its description. In this paper, we first revisit this approach and reveal that using description and permission will lead to many false positives because descriptions often fail to declare all sensitive operations. Then, we propose exploiting an app's privacy policy and its bytecode to enhance the malware detection based on description and permissions. It is non-trivial to automatically analyze privacy policy and perform the cross-verification among these four kinds of software artifacts including, privacy policy, bytecode, description, and permissions. To address these challenging issues, we first propose a novel data flow model for analyzing privacy policy, and then develop a new system, named TAPVerifier, for carrying out investigation of individual software artifacts and conducting the cross-verification. The experimental results show that TAPVerifier can analyze privacy policy with a high accuracy and recall rate. More importantly, integrating privacy policy and bytecode level information can remove up to 59.4 percent false alerts of the state-of-the-art systems, such as AutoCog, CHABADA, etc.
Man-Made Object Recognition from Underwater Optical Images Using Deep Learning and Transfer Learning

With the development of underwater optical sensors, manmade object recognition from underwater optical images has attracted wide attention. Deep learning methods have demonstrated impressive performance in object recognition tasks from natural images. However, it is difficult to collect large-scale labeled underwater optical images for training such a model. Based on the assumption that it is possible to acquire sufficient labeled in-air images, the proposed work leverages a combination of deep learning and transfer learning to develop a novel recognition system for man-made object from underwater optical images. The extracted features from the proposed network have high representative power, and demonstrate robustness in both in-air and underwater imaging modalities. Therefore, our proposed framework has the ability to recognize underwater man-made objects using only labeled in -air images. The results of experiments on simulated data demonstrate that the proposed method outperforms traditional deep learning methods in the task of underwater man-made object recognition.
Utilizing AlexNet Deep Transfer Learning for Ear Recognition

Transfer Learning is an efficient approach of solving classification problem with little amount of data. In this paper, we applied Transfer Learning to the well-known AlexNet Convolution Neural Network (AlexNet CNN) for human recognition based on ear images. We adopted and fine-tuned AlexNet CNN to suit our problem domain. The last fully connected layer is replaced with another fully connected layer to recognize 10 classes instead of 1000 classes. Another Rectified Linear Unit (ReLU) layer is also added to improve the non-linear problem-solving ability of the network. To train the fine-tuned network, we allocate 250 ear images taken from 10 subjects for training, and 50 ear images are used for validation and testing. The proposed fine-tuned network works well in our application as we get 100% validation accuracy.
Synthesis of Images by Two-Stage Generative Adversarial Networks

In this paper, we propose a divide-and-conquer approach using two generative adversarial networks (GANs) to explore how a machine can draw colorful pictures (bird) using a small amount of training data. In our work, we simulate the procedure of an artist drawing a picture, where one begins with drawing objects' contours and edges and then paints them different colors. We adopt two GAN models to process basic visual features including shape, texture and color. We use the first GAN model to generate object shape, and then paint the black and white image based on the knowledge learned using the second GAN model. We run our experiments on 600 color images. The experimental results show that the use of our approach can generate good quality synthetic images, comparable to real ones.