Miracle-dz/UniFAR-FTUs huggingface dataset:
Longitudinal Study of the Prevalence of Malware Evasive Techniques

By their very nature, malware samples employ a variety of techniques to conceal their malicious behavior and hide it from analysis tools. To mitigate the problem, a large number of different evasion techniques have been documented over the years, and PoC implementations have been collected in public frameworks, like the popular Al-Khaser. As malware authors tend to reuse existing approaches, it is common to observe the same evasive techniques in malware samples of different families. However, no measurement study has been conducted to date to assess the adoption and prevalence of evasion techniques. In this paper, we present a large-scale study, conducted by dynamically analyzing more than 180K Windows malware samples, on the evolution of evasive techniques over the years. To perform the experiments, we developed a custom Pin-based Evasive Program Profiler (Pepper), a tool capable of both detecting and circumventing 53 anti-dynamic-analysis techniques of different categories, ranging from anti-debug to virtual machine detection. To observe the phenomenon of evasion from different points of view, we employed four different datasets, including benign files, advanced persistent threat (APTs), malware samples collected over a period of five years, and a recent collection of different families submitted to VirusTotal over a one-month period.
Efficient Estimation of State-Space Mixed-Frequency VARs: A Precision-Based Approach

State-space mixed-frequency vector autoregressions are now widely used for nowcasting. Despite their popularity, estimating such models can be computationally intensive, especially for large systems with stochastic volatility. To tackle the computational challenges, we propose two novel precision-based samplers to draw the missing observations of the low-frequency variables in these models, building on recent advances in the band and sparse matrix algorithms for state-space models. We show via a simulation study that the proposed methods are more numerically accurate and computationally efficient compared to standard Kalman-filter based methods. We demonstrate how the proposed method can be applied in two empirical macroeconomic applications: estimating the monthly output gap and studying the response of GDP to a monetary policy shock at the monthly frequency. Results from these two empirical applications highlight the importance of incorporating high-frequency indicators in macroeconomic models. JEL classification: C11, C32, C51
Atmospheric Dynamics of Temperate Sub-Neptunes. I. Dry Dynamics

Sub-Neptunes (planets with radii between 2 and 4 R ⊕) are abundant around M-dwarf stars, yet the atmospheric dynamics of these planets is relatively unexplored. In this paper, we aim to provide a basic underpinning of the dry dynamics of general low-mean-molecular-weight, temperate sub-Neptune atmospheres. We use the ExoFMS general circulation model (GCM) with an idealized gray-gas radiation scheme to simulate planetary atmospheres with different levels of instellation and rotation rates, using the atmosphere of K2-18b as our control. We find that the atmospheres of tidally locked (TL), temperate sub-Neptunes have weak horizontal temperature gradients owing to their slow rotation rates and hydrogen-dominated composition. The zonal wind structure is dominated by high-latitude cyclostrophic jets driven by the conservation of angular momentum. At low pressures we observe superrotating equatorial jets, which we propose are driven by a Rossby–Kelvin instability similar to the type seen in simulations of idealized atmospheres with axisymmetric forcing. By viewing the flow in TL coordinates, we find the predominant overturning circulation to be between the day side and night side, and we derive scaling relations linking the TL stream function and vertical velocities to instellation. Comparing our results to the only other GCM study of K2-18b, we find significant qualitative differences in dynamics, highlighting the need for further collaboration and investigation into the effects of different dynamical cores and physical parameterizations. This paper provides a baseline for studying the dry dynamics of temperate sub-Neptunes, which will be built on in part II with the introduction of moist effects.
Coupled-mode analysis of ferrite microstrip lines

A coupled-mode analysis is presented for an axially magnetized ferrite microstrip, with a particular reference to the case of a coupled microstrip (FCL). On the basis of the gyromagnetic coupling between the basis propagating and the evanescent isotropic modes, we determine the propagation characteristics of normal modes in ferrite microstrips. The theory is also applied to define the scattering matrix of the FCL microstrip junction. The important for application behavior of FCL junction is discussed. Numerical results confirming the validity of the theory are included.
Geometry-Aware Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain in the presence of dataset shift. Most existing methods cannot address the domain alignment and class discrimination well, which may distort the intrinsic data structure for downstream tasks (e.g., classification). To this end, we propose a novel geometry-aware model to learn the transferability and discriminability simultaneously via nuclear norm optimization. We introduce the domain coherence and class orthogonality for UDA from the perspective of subspace geometry. The domain coherence will ensure the model has a larger capacity for learning separable representations, and class orthogonality will minimize the correlation between clusters to alleviate the misalignment. So, they are consistent and can benefit from each other. Besides, we provide a theoretical insight into the norm-based learning literature in UDA, which ensures the interpretability of our model. We show that the norms of domains and clusters are expected to be larger and smaller to enhance the transferability and discriminability, respectively. Extensive experimental results on standard UDA datasets demonstrate the effectiveness of our theory and model.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis of Transformer-based language model performance across a wide range of model scales -- from models with tens of millions of parameters up to a 280 billion parameter model called Gopher. These models are evaluated on 152 diverse tasks, achieving state-of-the-art performance across the majority. Gains from scale are largest in areas such as reading comprehension, fact-checking, and the identification of toxic language, but logical and mathematical reasoning see less benefit. We provide a holistic analysis of the training dataset and model's behaviour, covering the intersection of model scale with bias and toxicity. Finally we discuss the application of language models to AI safety and the mitigation of downstream harms.
Collaboration is not Evil: A Systematic Look at Security Research for Industrial Use

Following the recent Internet of Things-induced trends on digitization in general, industrial applications will further evolve as well. With a focus on the domains of manufacturing and production, the Internet of Production pursues the vision of a digitized, globally interconnected, yet secure environment by establishing a distributed knowledge base. Background. As part of our collaborative research of advancing the scope of industrial applications through cybersecurity and privacy, we identified a set of common challenges and pitfalls that surface in such applied interdisciplinary collaborations. Aim. Our goal with this paper is to support researchers in the emerging field of cybersecurity in industrial settings by formalizing our experiences as reference for other research efforts, in industry and academia alike. Method. Based on our experience, we derived a process cycle of performing such interdisciplinary research, from the initial idea to the eventual dissemination and paper writing. This presented methodology strives to successfully bootstrap further research and to encourage further work in this emerging area. Results. Apart from our newly proposed process cycle, we report on our experiences and conduct a case study applying this methodology, raising awareness for challenges in cybersecurity research for industrial applications. We further detail the interplay between our process cycle and the data lifecycle in applied research data management. Finally, we augment our discussion with an industrial as well as an academic view on this research area and highlight that both areas still have to overcome significant challenges to sustainably and securely advance industrial applications. Conclusions. With our proposed process cycle for interdisciplinary research in the intersection of cybersecurity and industrial application, we provide a foundation for further research.
A Theoretical View of Linear Backpropagation and Its Convergence

Backpropagation (BP) is widely used for calculating gradients in deep neural networks (DNNs). Applied often along with stochastic gradient descent (SGD) or its variants, BP is considered as a de-facto choice in a variety of machine learning tasks including DNN training and adversarial attack/defense. Recently, a linear variant of BP named LinBP was introduced for generating more transferable adversarial examples for performing black-box attacks, by Guo et al. [1]. Although it has been shown empirically effective in black-box attacks, theoretical studies and convergence analyses of such a method is lacking. This paper serves as a complement and somewhat an extension to Guo et al.'s paper, by providing theoretical analyses on LinBP in neural-network-involved learning tasks, including adversarial attack and model training. We demonstrate that, somewhat surprisingly, LinBP can lead to faster convergence in these tasks in the same hyper-parameter settings, compared to BP. We confirm our theoretical results with extensive experiments. Code for reproducing our experimental results will be publicly available.
Mapping Industrial Poultry Operations at Scale With Deep Learning and Aerial Imagery

Concentrated animal feeding operations (CAFOs) pose serious risks to air, water, and public health, but have proven to be challenging to regulate. The U.S. Government Accountability Office notes that a basic challenge is the lack of comprehensive location information on CAFOs. We use the U.S. Department of Agriculture's National Agricultural Imagery Program 1 m/pixel aerial imagery to detect poultry CAFOs across the continental USA. We train convolutional neural network models to identify individual poultry barns and apply the best-performing model to over 42 TB of imagery to create the first national open-source dataset of poultry CAFOs We validate the model predictions against held-out validation set on poultry CAFO facility locations from ten hand-labeled counties in California and demonstrate that this approach has significant potential to fill gaps in environmental monitoring.
Bidding in Multi-Unit Auctions under Limited Information

We study multi-unit auctions in which bidders have limited knowledge of opponent strategies and values. We characterize optimal prior-free bids; these bids minimize the maximal loss in expected utility resulting from uncertainty surrounding opponent behavior. Optimal bids are readily computable despite bidders having multi-dimensional private information, and in certain cases admit closed-form solutions. In the pay-as-bid auction the minimax-loss bid is unique; in the uniform-price auction the minimax-loss bid is unique if the bidder is allowed to determine the quantities for which they bid, as in many practical applications. We compare minimax-loss bids and auction outcomes across auction formats, and derive testable predictions.
The torelon spectrum and the world-sheet axion

Andreas Athenodoroua,b,∗ and Michael Teper Computation-based Science and Technology Research Center, The Cyprus Institute, Cyprus Dipartimento di Fisica, Universitá di Pisa and INFN, Sezione di Pisa, Largo Pontecorvo 3, 56127 Pisa, Italy Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Parks Road, Oxford OX1 3PU, UK All Souls College, University of Oxford, High Street, Oxford OX1 4AL, UK E-mail: a.athenodorou@cyi.ac. cy, mike.teper@physics.ox. ac.uk
Anomalous Residual Surface Conductivity in a Superconductor with Strong Spin-Orbit Coupling

Conventional BCS superconductors are expected to exhibit a conductivity with vanishing dissipation with decreasing temperature. While bulk physical properties measurements indicate PdPb$_{2}$ is a conventional superconductor with a $T_c$ of 3.0 K, measurements of surface impedance through the microwave cavity perturbation technique indicate a large, non-vanishing dissipative component below $T_c$ that is at odds with conventional superconductivity. We demonstrate PdPb$_2$ to be a possible topological superconductor with a fully gapped bulk and a dissipative Majorana fluid surface.
Agglomeration-based geometric multigrid schemes for the Virtual Element Method

In this paper we analyse the convergence properties of two-level, W-cycle and V-cycle agglomeration-based geometric multigrid schemes for the numerical solution of the linear system of equations stemming from the lowest order $C^0$-conforming Virtual Element discretization of two-dimensional second-order elliptic partial differential equations. The sequence of agglomerated tessellations are nested, but the corresponding multilevel virtual discrete spaces are generally non-nested thus resulting into non-nested multigrid algorithms. We prove the uniform convergence of the two-level method with respect to the mesh size and the uniform convergence of the W-cycle and the V-cycle multigrid algorithms with respect to the mesh size and the number of levels. Numerical experiments confirm the theoretical findings.
SDSS-IV MaNGA: Understanding Ionized Gas Turbulence Using Integral Field Spectroscopy of 4500 Star-forming Disk Galaxies

The Sloan Digital Sky Survey MaNGA program has now obtained integral field spectroscopy for over 10,000 galaxies in the nearby universe. We use the final MaNGA data release DR17 to study the correlation between ionized gas velocity dispersion and galactic star formation rate, finding a tight correlation in which σ Hα from galactic H ii regions increases significantly from ∼18–30 km s−1, broadly in keeping with previous studies. In contrast, σ Hα from diffuse ionized gas increases more rapidly from 20–60 km s−1. Using the statistical power of MaNGA, we investigate these correlations in greater detail using multiple emission lines and determine that the observed correlation of σ Hα with local star formation rate surface density is driven primarily by the global relation of increasing velocity dispersion at higher total star formation rate, as are apparent correlations with stellar mass. Assuming H ii region models consistent with our finding that σ [O III] < σ Hα < σ [O I], we estimate the velocity dispersion of the molecular gas in which the individual H ii regions are embedded, finding values σ Mol = 5–30 km s−1 consistent with ALMA observations in a similar mass range. Finally, we use variations in the relation with inclination and disk azimuthal angle to constrain the velocity dispersion ellipsoid of the ionized gas σ z /σ r = 0.84 ± 0.03 and σ ϕ /σ r = 0.91 ± 0.03, similar to that of young stars in the Galactic disk. Our results are most consistent with the theoretical models in which turbulence in modern galactic disks is driven primarily by star formation feedback.
Predicting Job Titles from Job Descriptions with Multi-label Text Classification

Finding a suitable job and hunting for eligible candidates are important to job seeking and human resource agencies. With the vast information about job descriptions, employees and employers need assistance to automatically detect job titles based on job description texts. In this paper, we propose the multi-label classification approach for predicting relevant job titles from job description texts, and implement the Bi-GRULSTM-CNN with different pre-trained language models to apply for the job titles prediction problem. The BERT with multilingual pre-trained model obtains the highest result by Fl-scores on both development and test sets, which are 62.20% on the development set, and 47.44% on the test set.
The Open Systems View

There is a deeply entrenched view in philosophy and physics, the closed systems view, according to which isolated systems are conceived of as fundamental. On this view, when a system is under the influence of its environment this is described in terms of a coupling between it and a separate system which taken together are isolated. We argue against this view, and in favor of the alternative open systems view, for which systems interacting with their environment are conceived of as fundamental, and the environment's influence is represented via the dynamical equations that govern the system's evolution. Taking quantum theories of closed and open systems as our case study, and considering three alternative notions of fundamentality: (i)~ontic fundamentality, (ii)~epistemic fundamentality, and (iii)~explanatory fundamentality, we argue that the open systems view is fundamental, and that this has important implications for the philosophy of physics, the philosophy of science, and for metaphysics.
Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning

Generalized few-shot semantic segmentation was introduced to move beyond only evaluating few-shot segmentation models on novel classes to include testing their ability to remember base classes. While the current state-of-the-art approach is based on meta-learning, it performs poorly and saturates in learning after observing only a few shots. We propose the first fine-tuning solution, and demonstrate that it addresses the saturation problem while achieving state-of-the-art results on two datasets, PASCAL-5i and COCO-20i. We also show that it outperforms existing methods, whether fine-tuning multiple final layers or only the final layer. Finally, we present a triplet loss regularization that shows how to redistribute the balance of performance between novel and base categories so that there is a smaller gap between them.
More is Less: Inducing Sparsity via Overparameterization

In deep learning, it is common to overparameterize neural networks, that is, to use more parameters than training samples. Quite surprisingly training the neural network via (stochastic) gradient descent leads to models that generalize very well, while classical statistics would suggest overfitting. In order to gain understanding of this implicit bias phenomenon, we study the special case of sparse recovery (compressed sensing) which is of interest on its own. More precisely, in order to reconstruct a vector from underdetermined linear measurements, we introduce a corresponding overparameterized square loss functional, where the vector to be reconstructed is deeply factorized into several vectors. We show that, if there exists an exact solution, vanilla gradient flow for the overparameterized loss functional converges to a good approximation of the solution of minimal $\ell _1$-norm. The latter is well-known to promote sparse solutions. As a by-product, our results significantly improve the sample complexity for compressed sensing via gradient flow/descent on overparameterized models derived in previous works. The theory accurately predicts the recovery rate in numerical experiments. Our proof relies on analyzing a certain Bregman divergence of the flow. This bypasses the obstacles caused by non-convexity and should be of independent interest.
Quintessence and the Swampland: The Parametrically Controlled Regime of Moduli Space

We provide evidence that slow roll is not possible in any parametrically controlled regime of the moduli space of string theory. This is proven in full generality in the asymptotic limit of the moduli space of type II and heterotic Calabi‐Yau compactifications for the dilaton and any number of Kähler moduli. Our results suggest that in order to build quintessence into string theory one must work in the interior of moduli space where numerical, even if not parametric, control could still be achieved.
Accelerating Clique Counting in Sparse Real-World Graphs via Communication-Reducing Optimizations

Counting instances of specific subgraphs in a larger graph is an important problem in graph mining. Finding cliques of size k (k-cliques) is one example of this NP-hard problem. Different algorithms for clique counting avoid counting the same clique multiple times by pivoting or ordering the graph. Ordering-based algorithms include an ordering step to direct the edges in the input graph, and a counting step, which is dominated by building node or edge-induced subgraphs. Of the ordering-based algorithms, kClist is the state-of-the art algorithm designed to work on sparse real-world graphs. Despite its leading overall performance, kClist's vertex-parallel implementation does not scale well in practice on graphs with a few million vertices. We present CITRON (Clique counting with Traffic Reducing Optimizations) to improve the parallel scalability and thus overall performance of clique counting. We accelerate the ordering phase by abandoning kClist's sequential core ordering and using a parallelized degree ordering. We accelerate the counting phase with our reorganized subgraph data structures that reduce memory traffic to improve scaling bottlenecks. Our sorted, compact neighbor lists improve locality and communication efficiency which results in near-linear parallel scaling. CITRON significantly outperforms kClist while counting moderately sized cliques, and thus increases the size of graph practical for clique counting. We have recently become aware of ArbCount (arXiv:2002.10047), which often outperforms us. However, we believe that the analysis included in this paper will be helpful for anyone who wishes to understand the performance characteristics of k-clique counting.
Unique Distributions Under Non-IID Assumption

Applications of Strongly Convergent M-Estimators are discussed. Given the ubiquity of distributions across the sciences, multiple applications in the Physical, Biomedical and Social Sciences are elaborated. In one particular implementation unique utilities are attained. Finally, the importance of the results and findings to model fit, inference and prediction are highlighted for broad applicability across the sciences.
Effective utilization of labeled data from related tasks using graph contrastive pretraining: application to disaster related text classification

Contrastive pretraining techniques for text classification has been largely studied in an unsupervised setting. However, oftentimes labeled data from related past datasets which share label semantics with current task is available. We hypothesize that using this labeled data effectively can lead to better generalization on current task. In this paper, we propose a novel way to effectively utilize labeled data from related tasks with a graph based supervised contrastive learning approach. We formulate a token-graph by extrapolating the supervised information from examples to tokens. Our experiments with 8 disaster datasets show our method outperforms baselines and also example-level contrastive learning based formulation. In addition, we show cross-domain effectiveness of our method in a zero-shot setting.