Miracle-dz/UniFAR-FTUs huggingface dataset:
HAPSSA: Holistic Approach to PDF malware detection using Signal and Statistical Analysis

Malicious PDF documents present a serious threat to various security organizations that require modern threat intelligence platforms to effectively analyze and characterize the identity and behavior of PDF malware. State-of-the-art approaches use machine learning (ML) to learn features that characterize PDF malware. However, ML models are often susceptible to evasion attacks, in which an adversary obfuscates the malware code to avoid being detected by an Antivirus. In this paper, we derive a simple yet effective holistic approach to PDF malware detection that leverages signal and statistical analysis of malware binaries. This includes combining orthogonal feature space models from various static and dynamic malware detection methods to enable generalized robustness when faced with code obfuscations. Using a dataset of nearly 30,000 PDF files containing both malware and benign samples, we show that our holistic approach maintains a high detection rate (99.92%) of PDF malware and even detects new malicious files created by simple methods that remove the obfuscation conducted by malware authors to hide their malware, which are undetected by most antiviruses.
Lattice gauge symmetry in neural networks

We review a novel neural network architecture called lattice gauge equivariant convolutional neural networks (L-CNNs), which can be applied to generic machine learning problems in lattice gauge theory while exactly preserving gauge symmetry. We discuss the concept of gauge equivariance which we use to explicitly construct a gauge equivariant convolutional layer and a bilinear layer. The performance of L-CNNs and non-equivariant CNNs is compared using seemingly simple non-linear regression tasks, where L-CNNs demonstrate generalizability and achieve a high degree of accuracy in their predictions compared to their non-equivariant counterparts.
GASKAP Pilot Survey Science. II. ASKAP Zoom Observations of Galactic 21 cm Absorption

Using the Australian Square Kilometre Array Pathfinder to measure 21 cm absorption spectra toward continuum background sources, we study the cool phase of the neutral atomic gas in the far outer disk, and in the inner Galaxy near the end of the Galactic bar at longitude 340°. In the inner Galaxy, the cool atomic gas has a smaller scale height than in the solar neighborhood, similar to the molecular gas and the super-thin stellar population in the bar. In the outer Galaxy, the cool atomic gas is mixed with the warm, neutral medium, with the cool fraction staying roughly constant with the Galactic radius. The ratio of the emission brightness temperature to the absorption, i.e., 1 − e −τ , is roughly constant for velocities corresponding to Galactic radius greater than about twice the solar circle radius. The ratio has a value of about 300 K, but this does not correspond to a physical temperature in the gas. If the gas causing the absorption has kinetic temperature of about 100 K, as in the solar neighborhood, then the value 300 K indicates that the fraction of the gas mass in this phase is one-third of the total H i mass.
Flying Trapeze Act Motion Planning Algorithm for Two-Link Free-Flying Acrobatic Robot

Most existing pieces of research on flying trapeze robot motion planning are verified on highly abstracted models, which are susceptible to physical uncertainties and physical limitations. To make the control algorithm more versatile and physically realizable, the proposed research was designed and verified to work with a simulation of a realizable two-link free-flying acrobatic robot that mimics real-world circumstances. The resulting algorithm can adjust the posture to reduce the effect of uncertainties in the robot’s physical properties and mishaps while being airborne, by quickly recomputing its posture trajectory using the isolated posture dynamics of itself considering its drifted center-of-mass trajectory on the sagittal plane while concurrently stabilizing the posture with a time-varying LQR. The simulation results show a high success rate in catching a target when the physical parameters are uncertain.
Kernel Methods for Multistage Causal Inference: Mediation Analysis and Dynamic Treatment Effects

Funding information Jerry Hausman Dissertation Fellowship, Department of Economics, Massachusetts Institute of Technology. We propose kernel ridge regression estimators for mediation analysis and dynamic treatment effects over short horizons. We allow treatments, covariates, and mediators to be discrete or continuous, and low, high, or infinite dimensional. We propose estimators of means, increments, and distributions of counterfactual outcomes with closed form solutions in terms of kernel matrix operations. For the continuous treatment case, we prove uniform consistencywith finite sample rates. For the discrete treatment case, we prove √ n consistency, Gaussian approximation, and semiparametric efficiency. We conduct simulations then estimate mediated and dynamic treatment effects of the US Job Corps program for disadvantaged youth.
A Composite Centrality Measure for Improved Identification of Influential Users

In recent years, the problem of identifying the spreading ability and ranking social network users according to their influence has attracted a lot of attention; different approaches have been proposed for this purpose. Most of these approaches rely on the topological location of nodes and their neighbours in the graph to provide a measure that estimates the spreading ability of users. One of the most well-known measures is k-shell; additional measures have been proposed based on it. However, as the same k-shell index may be assigned to nodes with different degrees, this measure suffers from low accuracy. This paper is trying to improve this by proposing a composite centrality measure in that it combines both the degree and k-shell index of nodes. Experimental results and evaluations of the proposed measure on various real and artificial networks show that the proposed measure outperforms other state-of-the-art measures regarding monotonicity and accuracy.
Residual-Guided Learning Representation for Self-Supervised Monocular Depth Estimation

Photometric consistency loss is one of the representative objective functions commonly used for self-supervised monocular depth estimation. However, this loss often causes unstable depth predictions in textureless or occluded regions due to incorrect guidance. Recent self-supervised learning approaches tackle this issue by utilizing feature representations explicitly learned from auto-encoders, expecting better discriminability than the input image. Despite the use of auto-encoded features, we observe that the method does not embed features as discriminative as auto-encoded features. In this paper, we propose residual guidance loss that enables the depth estimation network to embed the discriminative feature by transferring the discriminability of auto-encoded features. We conducted experiments on the KITTI benchmark and verified our method's superiority and orthogonality on other state-of-the-art methods.
Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits

We study the problem of dynamic regret minimization in K -armed Dueling Bandits under non-stationary or time-varying preferences. This is an online learning setup where the agent chooses a pair of items at each round and observes only a relative binary ‘win-loss’ feedback for this pair sampled from an underlying preference matrix at that round. We ﬁrst study the problem of static-regret minimization for adversarial preference sequences and design an efﬁcient algorithm with ˜ O ( √ KT ) regret bound. We next use similar algorithmic ideas to propose an efﬁcient and provably optimal algorithm for dynamic-regret minimization under two notions of non-stationarities. In particular, we show ˜ O ( √ SKT ) and ˜ O ( V 1 / 3 T K 1 / 3 T 2 / 3 ) dynamic-regret guarantees, respectively, with S being the total number of ‘effective-switches’ in the underlying preference relations and V T being a measure of ‘continuous-variation’ non-stationarity. These rates are provably optimal as justiﬁed with matching lower bound guarantees. Moreover, our proposed algorithms are ﬂexible as they can be easily ‘blackboxed’ to yield dynamic regret guarantees for other notions of dueling bandits regret, including condorcet regret, best-response bounds, and Borda regret. The complexity of these problems have not been studied prior to this work despite the practicality of non-stationary environments. Extensive simulations corroborate our results.
High Voltage Generation by Fiber-Coupled Pulsed Laser for a Simple Receiver Circuit Structure

Almost all high-voltage dc generation for lowpower applications is done by either electrostatic machines or voltage multipliers. Electrostatic machines use mechanically moving parts to transfer charge and energy from the lowvoltage side to the high-voltage side. Voltage multipliers use capacitive and inductive networks to achieve the same purpose of energy transfer. Considering the pros and cons inherent in those mechanical, capacitive, and inductive energy transfer, a new means of energy transfer may provide a superior design of a high voltage dc generator. Here we investigate the design of high voltage generators based on optical power transfer. Optical power delivered via fiber-optic cable allows extensive input-tooutput dc insulation and spatial separation. These characteristics lead to advantages with respect to ease of insulation, ease of electromagnetic shielding, and scalability. We experimentally validate the idea by building and testing a 5.5 kV dc generator module solely powered by a 20 kHz pulsed laser, and cascading three of those modules to obtain 14.7 kV dc output voltage. We then discuss possible improvements to the circuit design to make it useful for real-world applications. Finally, we demonstrate an optically powered electroadhesion gripper to show the practicality of the proposed high voltage generator.
Character Vectors of Strongly Regular Vertex Operator Algebras

We summarize interactions between vertex operator algebras and number theory through the lens of Zhu theory. The paper begins by recalling basic facts on vertex operator algebras (VOAs) and modular forms, and then explains Zhu's theorem on characters of VOAs in a slightly new form. We then axiomatize the desirable properties of modular forms that have played a role in Zhu's theorem and related classification results of VOAs. After this we summarize known classification results in rank two, emphasizing the geometric theory of vector-valued modular forms as a means for simplifying the discussion. We conclude by summarizing some known examples, and by providing some new examples, in higher ranks. In particular, the paper contains a number of potential character vectors that could plausibly correspond to a VOA, but such that the existence of a corresponding hypothetical VOA is presently unknown.
Resonance distribution in the quantum random Lorentz gas

The multiple scattering model of a quantum particle in a random Lorentz gas consisting of ﬁxed point scatterers is considered in arbitrary dimension. An eﬃcient method is developed to numerically compute the map of the density of scattering resonances in the complex plane of the wavenumber without ﬁnding them one by one. The method is applied to two collision models for the individual scatterers, namely a resonant model, and a non-resonant hard-sphere model. The results obtained with the former are compared to the literature. In particular, the spiral arms surrounding the single-scatterer resonance are identiﬁed as proximity resonances. Moreover, the hard-sphere model is used to reveal previously unknown structures in the resonance density. Finally, it is shown how Anderson localization aﬀects the distribution of resonance widths, especially in the one-dimensional case.
Crowdsourcing with Meta-Workers: A New Way to Save the Budget

Due to the unreliability of Internet workers, it's difficult to complete a crowdsourcing project satisfactorily, especially when the tasks are multiple and the budget is limited. Recently, meta learning has brought new vitality to few-shot learning, making it possible to obtain a classifier with a fair performance using only a few training samples. Here we introduce the concept of \emph{meta-worker}, a machine annotator trained by meta learning for types of tasks (i.e., image classification) that are well-fit for AI. Unlike regular crowd workers, meta-workers can be reliable, stable, and more importantly, tireless and free. We first cluster unlabeled data and ask crowd workers to repeatedly annotate the instances nearby the cluster centers; we then leverage the annotated data and meta-training datasets to build a cluster of meta-workers using different meta learning algorithms. Subsequently, meta-workers are asked to annotate the remaining crowdsourced tasks. The Jensen-Shannon divergence is used to measure the disagreement among the annotations provided by the meta-workers, which determines whether or not crowd workers should be invited for further annotation of the same task. Finally, we model meta-workers' preferences and compute the consensus annotation by weighted majority voting. Our empirical study confirms that, by combining machine and human intelligence, we can accomplish a crowdsourcing project with a lower budget than state-of-the-art task assignment methods, while achieving a superior or comparable quality.
Bayesian modelling of statistical region- and family-level clustered ordinal outcome data from Turkey

This study is concerned with the analysis of three-level ordinal outcome data with polytomous logistic regression in the presence of random-effects. It is assumed that the random-effects follow a Bridge distribution for the logit link, which allows one to obtain marginal interpretations of the regression coefficients. The data are obtained from the Turkish Income and Living Conditions Study, where the outcome variable is self-rated health (SRH), which is ordinal in nature. The analysis of these data is to compare covariate sub-groups and draw regionand family-level inferences in terms of SRH. Parameters and random-effects are sampled from the joint posterior densities following a Bayesian paradigm. Three criteria are used for model selection: Watenable information criterion, log pseudo marginal likelihood, and deviance information criterion. All three suggest that we need to account for both regionand family-level variabilities in order to model SRH. The extent to which the models replicate the observed data is examined by posterior predictive checks. Differences in SRH are found between levels of economic and demographic variables, regions of Turkey, and families who participated in the survey. Some of the interesting findings are that unemployed people are 19% more likely to report poorer health than employed people, and rural Aegean is the region that has the least probability of reporting poorer health.
A Comparison of Sleep and Performance of Sailors on an Operationally Deployed U.S. Navy Warship

Abstract : The crew s mission on a deployed warship is inherently dangerous. The nature of the job means navigating restricted waters, conducting underway replenishments with less than 200 feet of lateral separation from another ship, and various other operations all of which require a high level of training, alertness, and attention to detail. Performing these tasks when sailors are sleep deprived creates the potential for catastrophic incidents that can cost millions of dollars and possibly result in injury or loss of life. This study compared the sleep and performance of sailors standing either the 3/9 or 6/6 watch rotation on a deployed warship. Results showed that not only did sailors standing the 6/6 rotation receive less sleep, but their response speeds were significantly slower than their 3/9 counterparts. Although the 3/9 participants stood half as much watch, with twice as much time off watch, they still received only 391 minutes of sleep per night, on average. Even more concerning was that the 6/6 participants received only 330 minutes of sleep per night (less than six hours per day), on average, accruing over 2.5 hours of sleep debt per night. Sleep provides a combat edge to today s warfighters. Leaders neglect it at their peril.
ExtremeBB: Enabling Large-Scale Research into Extremism, the Manosphere and Their Correlation by Online Forum Data

Online extremism is a growing and pernicious problem, and increas-ingly linked to real-world violence. We introduce a new resource to help research and understand it: ExtremeBB is a structured textual dataset containing nearly 44M posts made by more than 300K registered members on 12 different online extremist forums, enabling both qualitative and quantitative large-scale analyses of historical trends going back two decades. It enables us to trace the evolution of different strands of extremist ideology; to measure levels of toxicity while exploring and developing the tools to do so better; to track the relationships between online subcultures and external political movements such as MAGA (the importance of which was seen in the attack on the US Capitol in January 2021); and to explore links with misogyny and violence, including radicalisation and recruitment. To illustrate a few potential uses, we apply statistical and data-mining techniques to analyse the online extremist landscape in a variety of ways, from posting patterns through topic modelling to toxicity and the membership overlap across different communities. A pic-ture emerges of communities working as support networks, with complex discussions over a wide variety of topics. The discussions of many topics show a level of disagreement which challenges the perception of homogeneity among these groups. These two features of mutual support and a wide range of attitudes lead us to suggest a more nuanced policy approach than simply shutting down these websites. Censorship might remove the support that lonely and troubled individuals are receiving, and fuel paranoid perceptions that the world is against them, though this must be balanced with other benefits of de-platforming. ExtremeBB can help develop a better understanding of these sub-cultures which may lead to more effective interventions; it also opens up the prospect of research to develop better tools to identify hate speech, and to monitor the effectiveness of any interventions that are undertaken.
Designing Strategies for Optimal Spatial Distribution of Wind Power

One of the common problems with the production of wind energy is the increased uncertainty surrounding the delivered output. Wind power producers adopt various strategies to reduce production risk, one of the most popular being the dispersion of wind farms across distant locations. The purpose of this paper is to examine the effectiveness of different methodologies for deriving optimal capacity allocation schemes. We collect a large dataset of wind-speed measurements for various locations across the Netherlands and use copula techniques to capture seasonal variations and cross-site dependencies in wind profiles. We exploit the information provided by statistical models to determine optimal resource allocation plans assuming a variety of operational and investment objectives. Experimental results designate the importance of accurately modelling site dependencies when deciding how to optimally allocate the overall wind capacity. Carefully chosen subgroups of sites can result in a more reliable energy supply as opposed to other allocation schemes that ignore spatial correlations.
Temperature characteristics of langanite bulk wave vibrations

Langanite (NGL) is the promising material for FC applications. In present work we measured NGL piezoelectric constants at temperature range -196/spl deg/C-+120/spl deg/C. This results and previous data on temperature dependencies (TD) other constants enabled to make the complete theoretical analysis of thickness vibration parameters of NGL single and double rotation cuts in wide temperature range. Thermally compensated xybl//spl alpha///spl beta/ cuts were theoretically found for several modes. For slow shear mode first and second order temperature coefficients (TC) could be zero simultaneously. From our calculations resonator frequency shift at temperature range could be 2-4 times better than SGL one. High quality NGL single crystals were grown by Czochralski method NGL plane, plano-convex and double-convex plates were prepared and tested at temperature range 15/spl deg/C-120/spl deg/C on fundamental and 3, 5, 7 overtone frequencies. It experimentally suggested zero cuts existence. From above data and possibility to grow high quality single crystals NGL seems to be more promising compare with SGL.
High-speed soft x-ray techniques

The construction and the characteristics of recent high- speed soft x-ray generators designed by the authors are described. The flash x-ray generators having cold-cathode radiation tubes are three types as follows: (1) soft generator utilizing an ignitron, (2) plasma generator for producing high-intensity characteristic x rays, and (3) water-window generator having a high-durability fermite capillary. In general, when we employed the flash x-ray generators with diodes, the pulse widths had values of less than 200 ns. Next, the x-ray duration was almost equivalent to the durations of the tube voltage and current during their damped oscillations when the water-window generator was employed. The maximum tube voltage was increased up to 100 kV, and the tube currents achieved with high-intensity generators were more than 10 kA. In order to obtain kilohertz-range repetition rates, we have developed two types of stroboscopic x-ray generators having hot-cathode tubes as follows: (4) low-photon-energy generator utilizing and triode and (5) high-photon-energy generator with a diode. As the duration was controlled in a microsecond range by using the low-photon-energy generator, sufficient x-ray intensifier for the normal radiography were obtained. The maximum photon energy could be increased up to about 200 keV by the high-photon-energy generator having a double transformer. Using these generation, we performed high-speed soft radiography.
Mixed tenure estates: Implications for asset management in the registered social landlord sector

Purpose – The purpose of this paper is to define mixed tenure estates as comprising any mix of social housing tenants with: private renting tenants (who therefore have private landlords); shared owners (i.e. those who buy a part share in their home, the remaining share being typically retained by a social landlord); owner occupiers (bought outright, or those paying a mortgage on the whole value of the property). Design/methodology/approach – A literature review and interviews with Registered Social Landlord (RSL) personnel are used to inform the discussion contained in this paper. The term “Asset Management” is used to describe the management of estates, including maintenance, repair and other physical investment. Does mixed tenure really have different asset management needs from mono tenure estates? The research methodology is based on a case study of a social housing provider, supported by semi‐structured interviews.Findings – The main conclusion of this paper is that, along with a number of other issue.
A self-learning neural-network LSI using neuron MOSFETs

A functional MOS transistor called a neuron MOSFET (vMOS) which simulates the function of biological neurons is discussed. A method of constructing neural network LSIs that have a self-learning capability using the neuron MOSFET is given. The key is the implementation of a synaptic connection which changes its weight according to various learning algorithms. In addition, the synapse must be free from standby power dissipation and be as small as possible. <<ETX>>