Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Neuroevolution Using Island Extinction and Repopulation

Neuroevolution commonly uses speciation strategies to better explore the search space of neural network architectures. One such speciation strategy is through the use of islands, which are also popular in improving performance and convergence of distributed evolutionary algorithms. However, in this approach some islands can become stagnant and not find new best solutions. In this paper, we propose utilizing extinction events and island repopulation to avoid premature convergence. We explore this with the Evolutionary eXploration of Augmenting Memory Models (EXAMM) neuro-evolution algorithm. In this strategy, all members of the worst performing island are killed of periodically and repopulated with mutated versions of the global best genome. This island based strategy is additionally compared to NEAT's (NeuroEvolution of Augmenting Topologies) speciation strategy. Experiments were performed using two different real world time series datasets (coal-fired power plant and aviation flight data). The results show that with statistical significance, this island extinction and repopulation strategy evolves better global best genomes than both EXAMM's original island based strategy and NEAT's speciation strategy.
Resisting the Distracting-factors in Pedestrian Detection

Pedestrian detection has been heavily studied in the last decade due to its wide applications. Despite incremental progress, several distracting-factors in the aspect of geometry and appearance still remain. In this paper, we first analyze these impeding factors and their effect on the general region-based detection framework. We then present a novel model that is resistant to these factors by incorporating methods that are not solely restricted to pedestrian detection domain. Specifically, to address the geometry distraction, we design a novel coulomb loss as a regulator on bounding box regression, in which proposals are attracted by their target instance and repelled by the adjacent non-target instances. For appearance distraction, we propose an efficient semantic-driven strategy for selecting anchor locations, which can sample informative negative examples at training phase for classification refinement. Our detector can be trained in an end-to-end manner, and achieves consistently high performance on both the Caltech-USA and CityPersons benchmarks. Code will be publicly available upon publication.
A Reconstruction-Computation-Quantization (RCQ) Approach to Node Operations in LDPC Decoding

This paper proposes a finite-precision decoding method for low-density parity-check (LDPC) codes that features the three steps of Reconstruction, Computation, and Quantization (RCQ). Unlike Mutual-Information-Maximization Quantized Belief Propagation (MIM-QBP), RCQ can approximate either belief propagation or Min-Sum decoding. MIM-QBP decoders do not work well when the fraction of degree-2 variable nodes is large. However, sometimes a large fraction of degree-2 variable nodes is used to facilitate a fast encoding structure, as seen in the IEEE 802.11 standard and the DVB-S2 standard. In contrast to MIM-QBP, the proposed RCQ decoder may be applied to any off-the-shelf LDPC code, including those with a large fraction of degree-2 variable nodes. Simulations show that a 4-bit Min-Sum RCQ decoder delivers frame error rate (FER) performance within 0.1 dB of floating point belief propagation (BP) for the IEEE 802.11 standard LDPC code in the low SNR region. The RCQ decoder actually outperforms floating point BP and Min-Sum in the high SNR region were FER less than 10−5. This paper also introduces Hierarchical Dynamic Quantization (HDQ) to design the time-varying non-uniform quantizers required by RCQ decoders. HDQ is a low-complexity design technique that is slightly sub-optimal. Simulation results comparing HDQ and optimal quantization on the symmetric binary-input memoryless additive white Gaussian noise channel show a mutual information loss of less than 10−6 bits, which is negligible in practice.
Keystroke Biometrics in Response to Fake News Propagation in a Global Pandemic

This work proposes and analyzes the use of keystroke biometrics for content de-anonymization. Fake news have become a powerful tool to manipulate public opinion, especially during major events. In particular, the massive spread of fake news during the COVID-19 pandemic has forced governments and companies to fight against missinformation. In this context, the ability to link multiple accounts or profiles that spread such malicious content on the Internet while hiding in anonymity would enable proactive identification and blacklisting. Behavioral biometrics can be powerful tools in this fight. In this work, we have analyzed how the latest advances in keystroke biometric recognition can help to link behavioral typing patterns in experiments involving 100,000 users and more than 1 million typed sequences. Our proposed system is based on Recurrent Neural Networks adapted to the context of content de-anonymization. Assuming the challenge to link the typed content of a target user in a pool of candidate profiles, our results show that keystroke recognition can be used to reduce the list of candidate profiles by more than 90%. In addition, when keystroke is combined with auxiliary data (such as location), our system achieves a Rank-1 identification performance equal to 52.6% and 10.9% for a background candidate list composed of 1K and 100K profiles, respectively.
Helium Ion Microscopy for Reduced Spin Orbit Torque Switching Currents.

Spin orbit torque driven switching is a favourable way to manipulate nanoscale magnetic objects for both memory and wireless communication devices. The critical current required to switch from one magnetic state to another depends on the geometry and the intrinsic properties of the materials used, which are difficult to control locally. Here we demonstrate how focused helium ion beam irradiation can modulate the local magnetic anisotropy of a Co thin film at the microscopic scale. Real-time in-situ characterisation using the anomalous Hall effect showed up to an order of magnitude reduction of the magnetic anisotropy under irradiation, and using this, multi-level switching is demonstrated. The result is that spin-switching current densities, down to 800 kA cm-2, can be achieved on predetermined areas of the film, without the need for lithography. The ability to vary critical currents spatially has implications not only for storage elements, but also neuromorphic and probabilistic computing.
Static Pricing Problems under Mixed Multinomial Logit Demand

Price differentiation is a common strategy for many transport operators. In this paper, we study a static multiproduct price optimization problem with demand given by a continuous mixed multinomial logit model. To solve this new problem, we design an efficient iterative optimization algorithm that asymptotically converges to the optimal solution. To this end, a linear optimization (LO) problem is formulated, based on the trust-region approach, to find a "good" feasible solution and approximate the problem from below. Another LO problem is designed using piecewise linear relaxations to approximate the optimization problem from above. Then, we develop a new branching method to tighten the optimality gap. Numerical experiments show the effectiveness of our method on a published, non-trivial, parking choice model.
The Value of Progenitor Radius Measurements for Explosion Modeling of Type II-Plateau Supernovae

Using Modules for Experiments in Stellar Astrophysics (MESA)+STELLA, we show that very different physical models can adequately reproduce a specific observed Type II-Plateau supernova (SN). We consider SN2004A, SN2004et, SN2009ib, SN2017eaw, and SN2017gmr, nickel-rich ( ) events with bolometric lightcurves and a well-sampled decline from the plateau. These events also have constraints on the progenitor radius, via a progenitor image, or, in the case of SN2017gmr, a radius from fitting shock-cooling models. In general, many explosions spanning the parameter space of progenitors can yield excellent lightcurve and Fe-line velocity agreement, demonstrating the success of scaling laws in motivating models that match plateau properties for a given radius and highlighting the degeneracy between plateau luminosity and velocity in models and observed events, which can span over 50% in ejecta mass, radius, and explosion energy. This can help explain disagreements in explosion properties reported for the same event using different model calculations. Our calculations yield explosion properties when combined with pre-explosion progenitor radius measurements or a robust understanding of the outermost of material that quantifies the progenitor radius from SN observations a few days after explosion.
History for Visual Dialog: Do we really need it?

Visual Dialogue involves “understanding” the dialogue history (what has been discussed previously) and the current question (what is asked), in addition to grounding information in the image, to accurately generate the correct response. In this paper, we show that co-attention models which explicitly encode dialoh history outperform models that don’t, achieving state-of-the-art performance (72 % NDCG on val set). However, we also expose shortcomings of the crowdsourcing dataset collection procedure, by showing that dialogue history is indeed only required for a small amount of the data, and that the current evaluation metric encourages generic replies. To that end, we propose a challenging subset (VisdialConv) of the VisdialVal set and the benchmark NDCG of 63%.
A CRISPR-Cas-Inspired Mechanism for Detecting Hardware Trojans in FPGA Devices

Hardware security has risen in prominence in recent years with concerns stemming from a globalizing semiconductor supply chain and increased third-party IP (intellectual property) usage. Trojan detection is of paramount importance for ensuring systems with confidentiality, integrity, and availability. Existing methods for hardware Trojan detection in FPGA (field programmable gate array) devices include test-time methods, pre-implementation methods, and run-time methods. The first two methods provide effective ways of detecting some Trojans; however, Trojans may be specifically designed to avoid detection at test-time or before implementation making run-time detection a more attractive option. Run-time detection and removal of Trojans is highly desirable due to the wide range of critical systems which are deployed on FPGAs and may be difficult or costly to remove from operation. Many parallels can be drawn between hardware and natural systems, and one example creates an analogy between hardware attacks and biological attacks. We propose a CRISPR-Cas-inspired (clustered regularly interspaced palindromic repeats) method for detecting hardware Trojans in FPGAs. The fundamental concepts of the Type 1-E CRISPR-Cas mechanism are discussed and simulated to predict the flow of genetic information through this biological system. The basic structure of this system is utilized to propose a novel run-time Trojan detection method titled CADEFT (CRISPR-Cas-based Algorithm for DEtection of FPGA Trojans). Different levels of FPGA application design flow are explored, and CADEFT is proposed for realization at the bitstream level to monitor the configuration bitstream and the run-time properties of the FPGA. The flexibility of CADEFT originates in the CRISPR-Cas mechanism's ability to recognize similar albeit previously unseen patterns which may pose a threat to the system.
A Survey on Security and Privacy Issues in Modern Healthcare Systems

Recent advancements in computing systems and wireless communications have made healthcare systems more efficient than before. Modern healthcare devices can monitor and manage different health conditions of patients automatically without any manual intervention from medical professionals. Additionally, the use of implantable medical devices, body area networks, and Internet of Things technologies in healthcare systems improve the overall patient monitoring and treatment process. However, these systems are complex in software and hardware, and optimizing between security, privacy, and treatment is crucial for healthcare systems because any security or privacy violation can lead to severe effects on patients’ treatments and overall health conditions. Indeed, the healthcare domain is increasingly facing security challenges and threats due to numerous design flaws and the lack of proper security measures in healthcare devices and applications. In this article, we explore various security and privacy threats to healthcare systems and discuss the consequences of these threats. We present a detailed survey of different potential attacks and discuss their impacts. Furthermore, we review the existing security measures proposed for healthcare systems and discuss their limitations. Finally, we conclude the article with future research directions toward securing healthcare systems against common vulnerabilities.
Efficient Network Function Backup by Update Piggybacking

Network Function Virtualization (NFV) and Service Function Chaining (SFC) have been widely used to enable flexible and agile network management. To enhance reliability, some research has proposed to deploy backup function instances for prompt recovery when a primary instance fails. While most of the recent studies focus on speeding up recovery, less attention has been paid to the problem of minimizing the state update cost. In this work, we present PiggyBackup (Piggyback-based Backup), an efficient backup instance deployment and update protocol. Our key idea is to reuse the existing service chains traversing through servers in a network to help piggyback the update information. By doing this, we eliminate the header overhead and reduce the amount of update traffic significantly. To realize such a piggyback-based update more efficiently, we investigate the backup instance deployment and chain selection problems to enhance piggybacking opportunities and reduce the forwarding hop counts with explicit consideration of the distribution of service chains. Our simulation results show that PiggyBackup reduces the average overall update overhead by 47.65% and 39.56%, respectively, in a fat-tree topology as compared to random deployment and shortest path based deployment.
Accelerating drug repurposing for COVID-19 via modeling drug mechanism of action with large scale gene-expression profiles

The novel coronavirus disease, named COVID-19, emerged in China in December 2019, and has rapidly spread around the world. It is clearly urgent to fight COVID-19 at global scale. The development of methods for identifying drug uses based on phenotypic data can improve the efficiency of drug development. However, there are still many difficulties in identifying drug applications based on cell picture data. This work reported one state-of-the-art machine learning method to identify drug uses based on the cell image features of 1024 drugs generated in the LINCS program. Because the multi-dimensional features of the image are affected by non-experimental factors, the characteristics of similar drugs vary greatly, and the current sample number is not enough to use deep learning and other methods are used for learning optimization. As a consequence, this study is based on the supervised ITML algorithm to convert the characteristics of drugs. The results show that the characteristics of ITML conversion are more conducive to the recognition of drug functions. The analysis of feature conversion shows that different features play important roles in identifying different drug functions. For the current COVID-19, Chloroquine and Hydroxychloroquine achieve antiviral effects by inhibiting endocytosis, etc., and were classified to the same community. And Clomiphene in the same community inibited the entry of Ebola Virus, indicated a similar MoAs that could be reflected by cell image.
SUPER: A Novel Lane Detection System

AI-based lane detection algorithms were actively studied over the last few years. Many have demonstrated superior performance compared with traditional feature-based methods. However, most methods remain riddled with assumptions and limitations, still not good enough for safe and reliable driving in the real world. In this paper, we propose a novel lane detection system, called Scene Understanding Physics-Enhanced Real-time (SUPER) algorithm. The proposed method consists of two main modules: 1) a hierarchical semantic segmentation network as the scene feature extractor and 2) a physics enhanced multi-lane parameter optimization module for lane inference. We train the proposed system using heterogeneous data from Cityscapes, Vistas and Apollo, and evaluate the performance on four completely separate datasets (that were never seen before), including Tusimple, Caltech, URBAN KITTI-ROAD, and Mcity-3000. The proposed approach performs the same or better than lane detection models already trained on the same dataset, and performs well even on datasets that was never trained on. Real-world vehicle tests were also conducted. Preliminary test results show promising real-time lane-detection performance compared with the Mobileye.
Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey

Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems and epidemiology. Representing complex networks as structures changing over time allow network models to leverage not only structural but also temporal patterns. However, as dynamic network literature stems from diverse fields and makes use of inconsistent terminology, it is challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a lot of attention in recent years for their ability to perform well on a range of network science tasks, such as link prediction and node classification. Despite the popularity of graph neural networks and the proven benefits of dynamic network models, there has been little focus on graph neural networks for dynamic networks. To address the challenges resulting from the fact that this research crosses diverse fields as well as to survey dynamic graph neural networks, this work is split into two main parts. First, to address the ambiguity of the dynamic network terminology we establish a foundation of dynamic networks with consistent, detailed terminology and notation. Second, we present a comprehensive survey of dynamic graph neural network models using the proposed terminology.
Algorithm of detecting structural variations in DNA sequences

Whole genome sequencing enables to use the longest common subsequence algorithm to detect genetic structure variations. We propose to search position of short unique fragments, genetic markers, to achieve acceptable time and space complexity. The markers are generated by algorithms searching the genetic sequence or its Fourier transformation. The presented methods are checked on structural variations generated in silico on bacterial genomes giving the comparable or better results than other solutions.
ROBUSTNESS OF CLOSED CAPTURE-RECAPTURE METHODS TO VIOLATIONS OF THE CLOSURE ASSUMPTION

Closed-population capture-recapture methods have been used extensively in animal ecology, both by themselves and within the context of Pollock's robust design and multistate models, to estimate various parameters of population and community dynamics. The defining assumption of geographic and demographic closure (i.e., no births, deaths, immigration, or emigration) for the duration of sampling is restrictive and is likely to be violated in many field situations. I evaluated several types of violations of the closure assumption and found that completely random movement in and out of a study area does not introduce bias to estimators from closed-population methods, although it decreases precision. In addition, if capture probabilities vary only with time, the closed-population Lincoln-Petersen estimator is unbiased for the size of the superpopulation when there are only births/immigration or only deaths/emigration. However, for other cases of nonrandom movement, closed-population estimators were biased when movement was Markovian (de- pendent on the presence/absence of the animal in the previous time period), when an animal was allowed one entry to and one exit from the study area, or when there was trap response or heterogeneity among animals in capture probability. In addition, the probability that an animal is present and available for capture (e.g., breeding propensity) can be estimated using Pollock's robust design only when movement occurs at a broader temporal scale than that of sampling.
Modeling effects of prescribed fire on young-growth coast redwood trees

The study was designed to produce data for modeling effects of prescribed fire treatments on top killing and basal sprouting of coast redwood trees (Sequoiasempervirens (D. Don) Endl. ) at the plot and individual-tree level. Prescribed fire treatments included two levels of fireline intensity and surface fuel consumption crossed in randomized blocks. Burning was conducted on 32 plots each containing approximately 150 to 250 redwoods. Binary responses of individual trees were modeled using logistic regression analysis. Percent response of trees on each plot by 5-cm diameter classes was modeled using logistic and nonlinear regression analysis. Tree diameter, surface fuel consumption, flame length, and crown scorch were significant predictors in all models of top killing and basal sprouting. Models relating heights of crown scorch and bark charring to fire and tree characteristics were also developed. The ability of coast redwood to sprout along branches following foliar damage from fire is unusual among coni.
Folliculo-stellate Cells of the Pituitary GlandWhat role do these star-shaped cells play?

n the early 1950s, scientists studying the ultrastructure of the rat pituitary gland described starshaped cells whose arms, or processes, extend among their neighbors (Rinehart and Farquhar 1953, 1955). Soon after, Farquhar (1957) described cells that border an irregularly shaped space, or follicle, containing gelatinous material. There followed many electron microscopy studies describing either stellate or follicular cells in various areas-the pars intermedia (PI), pars distalis (PD), and even the enigmatic pars tuberalis (PT)-of the pituitary of amphibians, fish, reptiles, and mammals (Figure 1). However, scientists gradually came to realize that they were describing two views of the same cell type, now known as folliculo-stellate (FS) cells. Even though the roles of various other cells in the pituitary have been delineated, the FS cell remains, as described by Nunez and Gershom (1981), "a cell in search of a function. " This article will consider the ultrastructure of this cell and relate its morphology to possible suggested functions. Certainly the FS cell is not the only cell whose function is unclear. Scientists still debate the function of the