Miracle-dz/UniFAR-FTUs huggingface dataset:
Entropy production and its large deviations in an active lattice gas

Active systems are characterized by a continuous production of entropy at steady state. We study the statistics of entropy production within a lattice-based model of interacting active particles that is capable of motility-induced phase separation. Exploiting a recent formulation of the exact fluctuating hydrodynamics for this model, we provide analytical results for its entropy production statistics in both typical and atypical (biased) regimes. This complements previous studies of the large deviation statistics of entropy production in off-lattice active particle models that could only be addressed numerically. Our analysis uncovers an unexpectedly intricate phase diagram, with five different phases arising (under bias) within the parameter regime where the unbiased system is in its homogeneous state. Notably, we find the concurrence of first order and second order nonequilibrium phase transition curves at a bias-induced tricritical point, a feature not yet reported in previous studies of active systems.
Spherical coordinates from persistent cohomology

We describe a method to obtain spherical parameterizations of arbitrary data through the use of persistent cohomology and variational optimization. We begin by computing the second-degree persistent cohomology of the filtered Vietoris-Rips (VR) complex of a data set X and extract a cocycle $$\alpha $$ α from any significant feature. From this cocycle, we define an associated map $$\alpha : VR(X) \rightarrow S^2$$ α : V R ( X ) → S 2 and use this map as an infeasible initialization for a variational model, which we show has a unique solution (up to rigid motion). We then employ an alternating gradient descent/Möbius transformation update method to solve the problem and generate a more suitable, i.e., smoother, representative of the homotopy class of $$\alpha $$ α , preserving the relevant topological feature. Finally, we conduct numerical experiments on both synthetic and real-world data sets to show the efficacy of our proposed approach.
Network Intrusion Detection with Limited Labeled Data

With the increasing dependency of daily life over computer networks, the importance of these networks security becomes prominent. Different intrusion attacks to networks have been designed and the attackers are working on improving them. Thus the ability to detect intrusion with limited number of labeled data is desirable to provide networks with higher level of security. In this paper we design an intrusion detection system based on a deep neural network. The proposed system is based on self-supervised contrastive learning where a huge amount of unlabeled data can be used to generate informative representation suitable for various downstream tasks with limited number of labeled data. Using different experiments, we have shown that the proposed system presents an accuracy of 94.05% over the UNSW-NB15 dataset, an improvement of 4.22% in comparison to previous method based on self-supervised learning. Our simulations have also shown impressive results when the size of labeled training data is limited. The performance of the resulting Encoder Block trained on UNSW-NB15 dataset has also been tested on other datasets for representation extraction which shows competitive results in downstream tasks.
Measurement of the Dark Matter Velocity Dispersion with Galaxy Stellar Masses, UV Luminosities, and Reionization

The root-mean-square of non-relativistic warm dark matter particle velocities scales as $v_{h\textrm{rms}}(a) = v_{h\textrm{rms}}(1)/a$, where $a$ is the expansion parameter of the universe. This velocity dispersion results in a cut-off of the power spectrum of density fluctuations due to dark matter free-streaming. Let $k_\textrm{fs}(t_\textrm{eq})$ be the free-streaming comoving cut-off wavenumber at the time of equal densities of radiation and matter. We obtain $v_{h\textrm{rms}}(1) = 0.41^{+0.14}_{-0.12} \textrm{ km/s}$, and $k_\textrm{fs}(t_\textrm{eq}) = 2.0^{+0.8}_{-0.5} \textrm{ Mpc}^{-1}$, at 68\% confidence, from the observed distributions of galaxy stellar masses and rest frame ultra-violet luminosities. This result is consistent with reionization. From the velocity dispersion cut-off we obtain the limits $v_{h\textrm{rms}}(1)<0.54 \textrm{ km/s}$, and $k_\textrm{fs}(t_\textrm{eq})>1.5 \textrm{ Mpc}^{-1}$. These results are in agreement with previous measurements based on spiral galaxy rotation curves, and on the formation of first galaxies and reionization. These measured parameters determine the temperature-to-mass ratio of warm dark matter. This ratio happens to be in agreement with the no freeze-in and no freeze-out warm dark matter scenario of spin 0 dark matter particles decoupling early on from the standard model sector. Spin 1/2 and spin 1 dark matter are disfavored if nature has chosen the no freeze-in and no freeze-out scenario. An extension of the standard model of quarks and leptons, with scalar dark matter that couples to the Higgs boson, that is in agreement with all current measurements, is briefly reviewed. Discrepancies with limits on dark matter particle mass that can be found in the literature, are addressed.
mmWave Beam Alignment using Hierarchical Codebooks and Successive Subtree Elimination

Treating the codewords in a beamforming codebook as the arms in a multi-armed bandit problem, we propose a best arm identification multi-armed bandit algorithm in the fixed-confidence setting for mmWave beam alignment initial access called Successive Subtree Elimination (SSE). The algorithm performance approaches that of state-of-the-art Bayesian algorithms at a fraction of the complexity and without requiring channel state information. The algorithm simultaneously exploits the benefits of hierarchical codebooks and the approximate unimodality of rewards to achieve fast beam steering, in a sense that we precisely define to provide fair comparison with existing algorithms. In particular, SSE outperforms other low-complexity algorithms in the low Signal-to-Noise Ratio (SNR) regime. The performance of SSE is demonstrated through the derivation of a closed-form sample complexity, which enables offline tuning of design parameters, as well as extensive simulations over slow fading channels.
Non-Gaussian Process Regression

Standard GPs offer a flexible modelling tool for well-behaved processes. However, deviations from Gaussianity are expected to appear in real world datasets, with structural outliers and shocks routinely observed. In these cases GPs can fail to model uncertainty adequately and may over-smooth inferences. Here we extend the GP framework into a new class of time-changed GPs that allow for straightforward modelling of heavy-tailed non-Gaussian behaviours, while retaining a tractable conditional GP structure through an infinite mixture of non-homogeneous GPs representation. The conditional GP structure is obtained by conditioning the observations on a latent transformed input space and the random evolution of the latent transformation is modelled using a L\'{e}vy process which allows Bayesian inference in both the posterior predictive density and the latent transformation function. We present Markov chain Monte Carlo inference procedures for this model and demonstrate the potential benefits compared to a standard GP.
Optical Conductivity as a Probe of the Interaction-Driven Metal in Rhombohedral Trilayer Graphene

Study of the strongly correlated states in van der Waals heterostructures is one of the central topics in modern condensed matter physics. Among these, the rhombohedral trilayer graphene (RTG) occupies a prominent place since it hosts a variety of interaction-driven phases, with the metallic ones yielding exotic superconducting orders upon doping. Motivated by these experimental findings, we show within the framework of the low-energy Dirac theory that the optical conductivity can distinguish different candidates for a paramagnetic metallic ground state in this system. In particular, this observable shows a single peak in the fully gapped valence-bond state. On the other hand, the bond-current state features two pronounced peaks in the optical conductivity as the probing frequency increases. Finally, the rotational symmetry breaking charge-density wave exhibits a minimal conductivity with the value independent of the amplitude of the order parameter, which corresponds precisely to the splitting of the two cubic nodal points at the two valleys into two triplets of the band touching points featuring linearly dispersing quasiparticles. These features represent the smoking gun signatures of different candidate order parameters for the paramagnetic metallic ground state, which should motivate further experimental studies of the RTG.
From [0,1]-Based Logic to Interval Logic (From Known Description of All Possible [0,1]-Based Logical Operations to a Description of All Possible Interval-Based Logical Operations)

Since early 1960s, we have a complete description of all possible [0 , 1]-based logical operations, namely of “and”-operations (t-norms) and of “or”-operations (t-conorms). In some real-life situations, intervals provide a more adequate way of describing uncertainty, so we need to describe interval-based logical operations (intuitionistic fuzzy logic can be viewed as an equivalent form of interval-valued fuzzy logic). Usually, researchers followed a pragmatic path and simply derived these operations from the [0 , 1]-based ones. From the foundational viewpoint, it is desirable not to a priori restrict ourselves to such derivative operations but, instead, to get a description of all interval-based operations which satisfy reasonable properties. Such description is presented in this paper. It turns out that all such operations can be described as the result of applying interval computations to the corresponding [0 , 1]-based ones. 75
Avast-CTU Public CAPE Dataset

Millions of new malicious software emerge every day worldwide 1 threatening to compromise consumer devices, stealing private data, and/or cause financial losses 2. These new threats put increasing pressure on malware-detection software to continuously improve detection capabilities in order to provide reliable protection to the users. There is a plethora of methods for detecting malicious samples (e.g., see surveys [19, 13]). Broadly speaking, we can distinguish two main categories: (1) detecting the samples based on their static features and (2) detecting the samples based on a behavioral analysis. The static features typically consist of considering the whole sample (e.g., as an image [9]) and/or properties of its most important parts (e.g., by examining in details header of a Windows portable executable (PE) file) [18]. The behavioral analysis consists of executing (or simulating the execution) of the sample and logging performed actions in order to determine whether these actions have characteristics of malicious behavior [13]. The main advantage of the first approach is the computational efficiency since extracting static features from the file itself can be much faster compared to the (simulated) execution. On the other hand, the main disadvantage of the static approach is the inability to reliably distinguish malicious samples from benign samples in case the sample is encrypted and/or the clean file is altered in a minor way to exhibit malicious behavior. The methods relying on behavioral analysis can discover malicious behavior even in encrypted samples, however, they require significantly more resources to run or simulate the instructions of the analyzed sample. In either case, the growing number of new, previously unseen samples makes the usage of automated decision/classification methods of artificial intelligence (AI) and machine learning (ML) inevitable in the malware-detection domain. Framing the problem of malware detection as an AI/ML problem reveals interesting and unique properties of the domain that are less prevalent in other domains. These properties, among others, include:
2022 Experimental and numerical identiﬁcation of the spiral wave in a wide-gap spherical Couette ﬂow

Spherical Couette ﬂow experiments were conducted according to the work of Egbers and Rath [Acta Mech. 111 pp.125–140 (1995)]. The ﬂow was visualized using aluminum ﬂakes drifted on a horizontal plane illuminated by a laser sheet. A comparison between the numerically calculated phase velocity and that calculated from the visualized ﬂow images indicates the robust formation of a spiral wave with azimuthal wavenumber m = 3. By solving the equation of motion for the inﬁnitesimal surface elements advecting in the ﬂow ﬁeld obtained numerically, this study obtained a visual distribution of reﬂected light, which is in good agreement with the picture obtained experimentally.
Increasing adverse drug events extraction robustness on social media: Case study on negation and speculation

In the last decade, an increasing number of users have started reporting adverse drug events (ADEs) on social media platforms, blogs, and health forums. Given the large volume of reports, pharmacovigilance has focused on ways to use natural language processing (NLP) techniques to rapidly examine these large collections of text, detecting mentions of drug-related adverse reactions to trigger medical investigations. However, despite the growing interest in the task and the advances in NLP, the robustness of these models in face of linguistic phenomena such as negations and speculations is an open research question. Negations and speculations are pervasive phenomena in natural language and can severely hamper the ability of an automated system to discriminate between factual and non-factual statements in text. In this article, we take into consideration four state-of-the-art systems for ADE detection on social media texts. We introduce SNAX, a benchmark to test their performance against samples containing negated and speculated ADEs, showing their fragility against these phenomena. We then introduce two possible strategies to increase the robustness of these models, showing that both of them bring significant increases in performance, lowering the number of spurious entities predicted by the models by 60% for negation and 80% for speculations.
Effects of Popular Science Writing Instruction on General Education Student Attitudes Towards Science: A Case Study in Astronomy

For many students, introductory college science courses are often the only opportunity in their formal higher education to be exposed to science, shaping their view of the subject, their scientific literacy, and their attitudes towards their own ability in STEM. While science writing instruction has been demonstrated to impact attitudes and outlooks of STEM majors in their coursework, this instructional strategy has yet to be explored for non-majors. In this work, we investigate student attitudes towards STEM before and after taking a writing-intensive introductory astronomy course. We find that students cite writing about science as beneficial to their learning, deepening their understanding of science topics and their perspective on science as a field and finding writing to be a ``bridge'' between STEM content and their focus on humanities in their majors. Students also report increased perceptions of their own ability and confidence in engaging with STEM across multiple metrics, leaving the course more prepared to be informed, engaged, and science literate citizens.
A class of self-affine tiles in $\mathbb{R}^d$ that are $d$-dimensional tame balls

We study a family of self-affine tiles in R (d ≥ 2) with noncollinear digit sets, which naturally generalizes a class studied originally by Deng and Lau in R and its extension to R by the authors. By using Brouwer’s invariance of domain theorem, along with a tool which we call horizontal distance, we obtain necessary and sufficient conditions for the tiles to be d-dimensional tame balls. This answers positively the conjecture in an earlier paper by the authors stating that a member in a certain class of self-affine tiles is homeomorphic to a d-dimensional ball if and only if its interior is connected. E-mail addresses: hilltower@163.com, lct984@163.com, smngai@georgiasouthern.edu . 2010 Mathematics Subject Classification. Primary 28A80, 52C22; Secondary 05B45, 51M20.
Measurement of the Usage of Web Clips in Underground Economy

In this paper, we study the ecosystem of the abused Web Clips in underground economy. Through this study, we find the Web Clips is wildly used by perpetrators to penetrate iOS devices to gain profit. This work starts with 1,800 user complaint documents about cyber crimes over Web Clips. We firstly look into the ecosystem of abused Web Clips and point out the main participants and workflow. In addition, what is the Web Clips used for is demystified. Then the main participants, including creators, distributors, and operators are deeply studied based on our dataset. We try to reveal the prominent features of the illicit Web Clips and give some mitigation measures. Analysis reveals that 1) SSL certificate is overwhelmingly preferred for signing Web Clips instances compared with certificate issued by Apple. The wildly used SSL certificates can be aggregated into a limited group. 2) The content of the abused Web Clips falls into a few categories, ‘Gambling’, ‘Fraud’, and ‘Pornography’ are among the top categories. 3) Instant messenger (IM) and live streaming platform are the most popular medium to trick victims into deploying the Web Clips. 4) The Web Clips are operated by a small amount of perpetrators, and the perpetrators tend to evade detection by taking technical approach, such as registering domain names through oversea domain name service provider, preferring easy-to-acquire new gTLD (global Top Level Domain), and deploying anti-crawler tricks. Our study gives hints on investigation of cyber crime over Web Clips, we hope that this work can help stakeholders to stay ahead of the threat.
General Formulation of Topos Many-Node Theory

We consider the created entities (events) in the ﬁrst moments of universe creation. It is assumed that there exists a causal energetic relationship between all events (nodes) such that all nodes are placed on a world line and each node occupies a region (instead of a point) in space-time, called locale, in mathematical terms. The set of locale nodes form a topos many-node system. Using some basic assumptions, we introduce two kinds of Hamiltonians. By attributing a general structural Hamiltonian to the system, it is shown that the system has an optimized critical dimension with a probable Raman and infrared spectrums. Also, we consider a general nonstructural Hamiltonian which includes a set of commutative self-adjoint operators and an interaction terms due to the spin, charge, or other kinds of probable degrees of freedoms for each n th -optimized graph. For ﬁnding the state-space, truth values and quantity valued objects of the many-node system, a general procedure is introduced. The set of these values is a classical snapshot of the n th optimized graph which forms its kinematic. We show that the dynamic of the system can be explained by deﬁning a combined map between the n th - state-space belongs to the n th -graph and the ( n + 1) th -state-space belong to ( n + 1) th -graph. Finally, by providing an interpretation of the general formulation of many-node theory, we discuss and explain how one can use the data of the cosmic background radiations and cosmic rays for ﬁnding a detailed model of both general structural and nonstructural introduced Hamiltonian. Here, time is no more than the change in truth value during comparison between n th and ( n + 1) th -graph.
A spectral least-squares-type method for heavy-tailed corrupted regression with unknown covariance \&heterogeneous noise

We revisit heavy-tailed corrupted least-squares linear regression assuming to have a corrupted n-sized label-feature sample of at most ǫn arbitrary outliers. We wish to estimate b∗ ∈ Rp given such sample of a label-feature pair (y, x) ∈ R×Rp satisfying y = 〈x, b∗〉 + ξ, with heavy-tailed (x, ξ). We only assume x is L4 − L2 hypercontractive with constant L > 0 and has covariance matrix Σ with minimum eigenvalue 1/μ(B2) > 0 and bounded condition number κ > 0. The noise ξ ∈ R can be arbitrarily dependent on x and nonsymmetric as long as ξx has finite covariance matrix Ξ. We propose a near-optimal computationally tractable estimator, based on the power method, assuming no knowledge on (Σ, Ξ) nor the operator norm of Ξ. With probability at least 1 − δ, our proposed estimator attains the statistical rate μ(B2)‖Ξ‖( p n + log(1/δ) n + ǫ) 1/2 and breakdown-point ǫ . 1 L4κ2 , both optimal in the l2-norm, assuming the near-optimalminimum sample size L4κ2(p log p + log(1/δ)) . n, up to a log factor. To the best of our knowledge, this is the first computationally tractable algorithm satisfying simultaneously all the mentioned properties. Our estimator is based on a two-stage Multiplicative Weight Update algorithm. The first stage estimates a descent direction v̂ with respect to the (unknown) pre-conditioned inner product 〈Σ(·), ·〉. The second stage estimate the descent direction Σv̂ with respect to the (known) inner product 〈·, ·〉, without knowing nor estimating Σ.
Multigrained Angle Representation for Remote-Sensing Object Detection

Arbitrary-oriented object detection (AOOD) plays a significant role in image understanding in remote-sensing scenarios. The existing AOOD methods face the challenges of ambiguity and high costs in angle representation. To this end, a multigrained angle representation (MGAR) method, consisting of coarse-grained angle classification (CAC) and fine-grained angle regression (FAR), is proposed. Specifically, the designed CAC avoids the ambiguity of angle prediction by discrete angular encoding (DAE) and reduces complexity by coarsening the granularity of DAE. Based on CAC, FAR is developed to refine the angle prediction with much lower costs than narrowing the granularity of DAE. Furthermore, an Intersection over Union (IoU)-aware FAR-Loss (IFL) is designed to improve the accuracy of angle prediction using an adaptive reweighting mechanism guided by IoU. Extensive experiments are performed on several public remote-sensing datasets, which demonstrate the effectiveness of the proposed MGAR. Moreover, experiments on embedded devices demonstrate that the proposed MGAR is also friendly for lightweight deployments.
Architecture-Algorithmic Trade-offs in Multi-path Channel Estimation for mmWAVE Systems

5G mmWave massive MIMO systems are likely to be deployed in dense urban scenarios, where increasing network capacity is the primary objective. A key component in mmWave transceiver design is channel estimation which is challenging due to the very large signal bandwidths (order of GHz) implying significant resolved spatial multipath, coupled with large # of Tx/Rx antennas for large-scale MIMO. This results in significantly increased training overhead that in turn leads to unacceptably high computational complexity and power cost. Our work thus highlights the interplay of transceiver architecture and receiver signal processing algorithm choices that fundamentally address (mobile) handset power consumption, with minimal degradation in performance. We investigate trade-offs enabled by conjunction of hybrid beamforming mmWave receiver and channel estimation algorithms that exploit available sparsity in such wideband scenarios. A compressive sensing (CS) framework for sparse channel estimation -- Binary Iterative Hard Thresholding (BIHT) \cite{jacques2013robust} followed by linear reconstruction method with varying quantization (ADC) levels -- is explored to compare the trade-offs between bit-depth and sampling rate for a given ADC power budget. Performance analysis of the BIHT+ linear reconstruction method is conducted via simulation studies for 5G specified multi-path channel models and compared to oracle-assisted bounds for validation.