Miracle-dz/UniFAR-FTUs huggingface dataset:
An iterative scheme for the generalized Peierls–Nabarro model based on the inverse Hilbert transform

A new semi-analytical iterative scheme is proposed in this work for solving the generalized Peierls-Nabarro model. The numerical method developed here exploits certain basic properties of the Hilbert transform to achieve the desired reduction of the non-local and non-linear equations characterizing the generalized Peierls-Nabarro model to a local fixed point iteration scheme. The method is validated with simple examples involving the 1D Peierls-Nabarro model corresponding to a sinusoidal stacking fault energy, and with calculations of the core structure of both edge and screw dislocations on the close-packed $\{111\}$ planes in Aluminium. An approximate technique to incorporate external stresses within the framework of the proposed iterative scheme is also discussed with applications to the equilibration of a dislocation dipole. Finally, the advantages, limitations and avenues for future extension of the proposed method are discussed.
Ground states and their characterization of spin-F Bose-Einstein condensates

The computation of the ground states of spin-$F$ Bose-Einstein condensates (BECs) can be formulated as an energy minimization problem with two quadratic constraints. We discretize the energy functional and constraints using the Fourier pseudospectral schemes and view the discretized problem as an optimization problem on manifold. Three different types of retractions to the manifold are designed. They enable us to apply various optimization methods on manifold to solve the problem. Specifically, an adaptive regularized Newton method is used together with a cascadic multigrid technique to accelerate the convergence. According to our limited knowledege, our method is the first applicable algorithm for BECs with an arbitrary integer spin, including the complicated spin-3 BECs. Extensive numerical results on ground states of spin-1, spin-2 and spin-3 BECs with diverse interaction and optical lattice potential in one/two/three dimensions are reported to show the efficiency of our method and to demonstrate some interesting physical phenomena.
On the VC-dimension of convex sets and half-spaces

A family $S$ of convex sets in the plane defines a hypergraph $H = (S,E)$ as follows. Every subfamily $S'\subset S$ defines a hyperedge of $H$ if and only if there exists a halfspace $h$ that fully contains $S'$, and no other set of $S$ is fully contained in $h$. In this case, we say that $h$ realizes $S'$. We say a set $S$ is shattered, if all its subsets are realized. The VC-dimension of a hypergraph $H$ is the size of the largest shattered set. We show that the VC-dimension for \emph{pairwise disjoint} convex sets in the plane is bounded by $3$, and this is tight. In contrast, we show the VC-dimension of convex sets in the plane (not necessarily disjoint) is unbounded. We also show that the VC-dimension is unbounded for pairwise disjoint convex sets in $\mathbb{R}^d$, for $d\geq 3$. We focus on, possibly intersecting, segments in the plane and determine that the VC-dimension is always at most $5$. And this is tight, as we construct a set of five segments that can be shattered. We give two exemplary applications. One for a geometric set cover problem and one for a range-query data structure problem, to motivate our findings.
WHAM!: Extending Speech Separation to Noisy Environments

Recent progress in separating the speech signals from multiple overlapping speakers using a single audio channel has brought us closer to solving the cocktail party problem. However, most studies in this area use a constrained problem setup, comparing performance when speakers overlap almost completely, at artificially low sampling rates, and with no external background noise. In this paper, we strive to move the field towards more realistic and challenging scenarios. To that end, we created the WSJ0 Hipster Ambient Mixtures (WHAM! ) dataset, consisting of two speaker mixtures from the wsj0-2mix dataset combined with real ambient noise samples. The samples were collected in coffee shops, restaurants, and bars in the San Francisco Bay Area, and are made publicly available. We benchmark various speech separation architectures and objective functions to evaluate their robustness to noise. While separation performance decreases as a result of noise, we still observe substantial gains relative to the noisy signals for most approaches.
Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight

In this letter, we propose a robust and efficient quadrotor motion planning system for fast flight in three-dimensional complex environments. We adopt a kinodynamic path searching method to find a safe, kinodynamic feasible, and minimum-time initial trajectory in the discretized control space. We improve the smoothness and clearance of the trajectory by a B-spline optimization, which incorporates gradient information from a Euclidean distance field and dynamic constraints efficiently utilizing the convex hull property of B-spline. Finally, by representing the final trajectory as a non-uniform B-spline, an iterative time adjustment method is adopted to guarantee dynamically feasible and non-conservative trajectories. We validate our proposed method in various complex simulational environments. The competence of the method is also validated in challenging real-world tasks. We release our code as an open-source package.11Open-source implementation is available at https://github.com/HKUST-Aerial-Robotics/Fast-Planner.
Persistent Multi-UAV Surveillance with Data Latency Constraints

We discuss surveillance with multiple unmanned aerial vehicles (UAV) that minimize idleness (the time between consecutive visits of sensing locations) and constrain latency (the time between capturing data at a sensing location and its arrival at the base station). This is important in persistent surveillance scenarios where sensing locations should not only be visited periodically, but the captured data also should reach the base station in due time even if the area is larger than the communication range. Our approach employs the concept of minimum-latency paths (MLP) to guarantee that the data reaches the base station within a predefined latency bound. To reach the bound, multiple UAVs cooperatively transport the data in a store-and-forward fashion. Additionally, MLPs specify a lower bound for any latency minimization problem where multiple mobile agents transport data in a store-and-forward fashion. We introduce three variations of a heuristic employing MLPs and compare their performance in a simulation study. The results show that extensions of the simplest of our approaches, where data is transported after each visit of a sensing location, show improved performance and the tradeoff between latency and idleness.
Domain Adaptation via Low-Rank Basis Approximation

Transfer learning focuses on the reuse of supervised learning models in a new context. Prominent applications can be found in robotics, image processing or web mining. In these areas, learning scenarios change by nature, but often remain related and motivate the reuse of existing supervised models. While the majority of symmetric and asymmetric domain adaptation algorithms utilize all available source and target domain data, we show that domain adaptation requires only a substantial smaller subset. This makes it more suitable for real-world scenarios where target domain data is rare. The presented approach finds a target subspace representation for source and target data to address domain differences by orthogonal basis transfer. We employ Nystrom techniques and show the reliability of this approximation without a particular landmark matrix by applying post-transfer normalization. It is evaluated on typical domain adaptation tasks with standard benchmark data.
Quiver invariants from Jeffrey-Kirwan residues

In quiver quantum mechanics with 4 supercharges, supersymmetric ground states are known to be in one-to-one correspondence with Dolbeault cohomology classes on the moduli space of stable quiver representations. Using supersymmetric localization, the refined Witten index can be expressed as a residue integral with a specific contour prescription, originally due to Jeffrey and Kirwan, depending on the stability parameters. On the other hand, the physical picture of quiver quantum mechanics describing interactions of BPS black holes predicts that the refined Witten index of a non-Abelian quiver can be expressed as a sum of indices for Abelian quivers, weighted by `single-centered invariants'. In the case of quivers without oriented loops, we show that this decomposition naturally arises from the residue formula, as a consequence of applying the Cauchy-Bose identity to the vector multiplet contributions. For quivers with loops, the same procedure produces a natural decomposition of the single-centered invariants, which remains to be elucidated. In the process, we clarify some under-appreciated aspects of the localization formula. Part of the results reported herein have been obtained by implementing the Jeffrey-Kirwan residue formula in a public Mathematica code.
A Gradient Sampling method based on Ideal direction for solving nonsmooth nonconvex optimization problems: convergence analysis and numerical experiments

In this paper, a modification to the Gradient Sampling (GS) method for minimizing nonsmooth nonconvex functions is presented. One drawback in GS method is the need of solving a Quadratic optimization Problem (QP) at each iteration, which is time-consuming especially for large scale objectives. To resolve this difficulty, we propose a new descent direction, namely Ideal direction, for which there is no need to consider any quadratic or linear optimization subproblem. It is shown that, this direction satisfies Armijo step size condition and can be used to make a substantial reduction in the objective function. Furthermore, we prove that using Ideal directions preserves the global convergence of the GS method. Moreover, under some moderate assumptions, we present an upper bound for the number of serious iterations. Using this upper bound, we develop a different strategy to study the convergence of the method. We also demonstrate the efficiency of the proposed method using small, medium and large scale problems in our numerical experiments.
Tight Sensitivity Bounds For Smaller Coresets

An ε-coreset to the dimensionality reduction problem for a (possibly very large) matrix A ∈ Rn x d is a small scaled subset of its n rows that approximates their sum of squared distances to every affine k-dimensional subspace of Rd, up to a factor of 1±ε. Such a coreset is useful for boosting the running time of computing a low-rank approximation (k-SVD/k-PCA) while using small memory. Coresets are also useful for handling streaming, dynamic and distributed data in parallel. With high probability, non-uniform sampling based on the so called leverage score or sensitivity of each row in A yields a coreset. The size of the (sampled) coreset is then near-linear in the total sum of these sensitivity bounds. We provide algorithms that compute provably tight bounds for the sensitivity of each input row. It is based on two ingredients: (i) iterative algorithm that computes the exact sensitivity of each row up to arbitrary small precision for (non-affine) k-subspaces, and (ii) a general reduction for computing a coreset for affine subspaces, given a coreset for (non-affine) subspaces in Rd. Experimental results on real-world datasets, including the English Wikipedia documents-term matrix, show that our bounds provide significantly smaller and data-dependent coresets also in practice. Full open source code is also provided.
Methodology for the Automated Metadata-Based Classification of Incriminating Digital Forensic Artefacts

The ever increasing volume of data in digital forensic investigation is one of the most discussed challenges in the field. Usually, most of the file artefacts on seized devices are not pertinent to the investigation. Manually retrieving suspicious files relevant to the investigation is akin to finding a needle in a haystack. In this paper, a methodology for the automatic prioritisation of suspicious file artefacts (i.e., file artefacts that are pertinent to the investigation) is proposed to reduce the manual analysis effort required. This methodology is designed to work in a human-in-the-loop fashion. In other words, it predicts/recommends that an artefact is likely to be suspicious rather than giving the final analysis result. A supervised machine learning approach is employed, which leverages the recorded results of previously processed cases. The process of features extraction, dataset generation, training and evaluation are presented in this paper. In addition, a toolkit for data extraction from disk images is outlined, which enables this method to be integrated with the conventional investigation process and work in an automated fashion.
Predicting the Topical Stance of Media and Popular Twitter Users

Controversial social and political issues of the day spur people to express their opinion on social networks, often sharing links to online media articles and reposting statements from prominent members of the platforms. Discovering the stances of people and entire media on current, debatable topics is important for social statisticians and policy makers. Many supervised solutions exist for determining viewpoints, but manually annotating training data is costly. In this paper, we propose a method that uses unsupervised learning and is able to characterize both the general political leaning of online media and popular Twitter users, as well as their stances with respect to controversial topics, by leveraging on the retweet behavior of users. We evaluate the model by comparing its bias predictions to gold labels from the Media Bias/Fact Check website, and we further perform manual analysis.
Job Scheduling Based on Virtual Abstractions in Cloud

--Cloud computing is a provisioning of services in a timely, on-demand manner, to allow the scaling up and down of resources. Job scheduling is one of the major issues in the public cloud which concerns availability of resources in the datacenter. Data center need to achieve certain level of utilization of its nodes while maintaining level of responsiveness of parallel jobs. Existing scheduling schemes make use of backfilling strategies which pre-empt shortest jobs to execute when jobs at head of the queue have unavailable of resources. This results in starvation of larger jobs, reduced throughput and underutilization of resources. In this paper, job scheduling based on virtual abstraction scheme is proposed for efficient scheduling of jobs in k-cloud data center with multiple computing capacities which solves large-scale static scheduling problem in cloud.
Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy

There is a growing desire in the field of reinforcement learning (and machine learning in general) to move from black-box models toward more "interpretable AI. " We improve interpretability of reinforcement learning by increasing the utility of decision tree policies learned via reinforcement learning. These policies consist of a decision tree over the state space, which requires fewer parameters to express than traditional policy representations. Existing methods for creating decision tree policies via reinforcement learning focus on accurately representing an action-value function during training, but this leads to much larger trees than would otherwise be required. To address this shortcoming, we propose a novel algorithm which only increases tree size when the estimated discounted future reward of the overall policy would increase by a sufficient amount. Through evaluation in a simulated environment, we show that its performance is comparable or superior to traditional tree-based approaches and that it yields a more succinct policy. Additionally, we discuss tuning parameters to control the tradeoff between optimizing for smaller tree size or for overall reward.
The ecodist Package for Dissimilarity-based Analysis of Ecological Data

Ecologists are concerned with the relationships between species composition and environmental framework incorporating space explicitly is an extremely flexible tool for answering these questions. The R package ecodist brings together methods for working with dissimilarities, including some not available in other R packages. We present some of the features of ecodist, particularly simple and partial Mantel tests, and make recommendations for their effective use. Although the partial Mantel test is often used to account for the effects of space, the assumption of linearity greatly reduces its effectiveness for complex spatial patterns. We introduce a modification of the Mantel correlogram designed to overcome this restriction and allow consideration of complex nonlinear structures. This extension of the method allows the use of partial multivariate correlograms and tests of relationship between variables at different spatial scales. Some of the possibilities are demonstrated using both artificial data and data from an ongoing study of plant community composition in grazinglands of the northeastern United States.
Good News for People Who Love Bad News: Centralization, Privacy, and Transparency on US News Sites

The democratic role of the press relies on maintaining independence, ensuring citizens can access controversial materials without fear of persecution, and promoting transparency. However, as news has moved to the web, reliance on third-parties has centralized revenue and hosting infrastructure, fostered an environment of pervasive surveillance, and lead to widespread adoption of opaque and poorly-disclosed tracking practices. In this study, 4,000 US-based news sites, 4,000 non-news sites, and privacy policies for 1,892 news sites and 2,194 non-news sites are examined. We find news sites are more reliant on third-parties than non-news sites, user privacy is compromised to a greater degree on news sites, and privacy policies lack transparency in regards to observed tracking behaviors. Overall, findings indicate the democratic role of the press is being undermined by reliance on the "surveillance capitalism" funding model.
Exploring the Smartphone Manipulation Skills in a Coffee Farming Community Using Clustering Algorithm

Majority of the general public in the Philippines who lives in rural areas maintains their living through agriculture and the importance of coffee in the country cannot be underrated. However, the coffee plantations are commonly confronted with diverse insect diseases. This pushes authorities to continue to look for solutions through scientific technological applications for coffee farming. Nowadays, smartphone is becoming a functional tool in agriculture because its mobility served as an advantage to agriculture. However, the level of skills on smartphone technology usage among the rural community is low due to limited knowledge and skills. Thus, this study has the main objective of applying data mining to the smartphone manipulation skills dataset gathered in the province of Quirino, Philippines as step for risk analysis leading to the implementation of a smartphone-based coffee disease management project. Specifically, it sought to determine the most appropriate algorithm that can produce the different types of potential users and to identify the different types of possible users and their skills that emerged from the clustering. The result shows that k-means where k=4 is the best choice for the dataset. The four clusters formed to represent the four groups of possible users are the good users with 25 instances, skilled users with 35 instances, the users with limited skills with 83 instances and finally, the expert users with 32 instances. Each of the groups possesses their distinct skills which came out from the clustering technique implemented.
Live It Up: Analyzing Emotions and Language Use in Tweets during the Soccer World Cup Finals

In this paper, we present a study to identify similarities and differences in how users express themselves on Twitter during two editions of the most watched sports events in the world, the finals of the FIFA Soccer World Cup of 2014 and 2018. Our findings suggest in 2014 users tended to post more negative content than in 2018, while less hateful and offensive messages were posted in 2018 than in 2014. This study also showcases the challenges of performing analysis of emotional reactions on sports-related posts due to the specificity of the colorful language employed by the fans.
On the Endurance of the d-Choices Garbage Collection Algorithm for Flash-Based SSDs

Garbage collection (GC) algorithms for flash-based solid-state drives (SSDs) have a profound impact on its performance and many studies have focused on assessing the so-called write amplification of various GC algorithms. In this article, we consider the family of d-choices GC algorithms and study (a) the extent in which these algorithms induce unequal wear and (b) the manner in which they affect the lifetime of the drive. For this purpose, we introduce two performance measures: PE fairness and SSD endurance. We study the impact of the d-choices GC algorithm on both these measures under different workloads (uniform, synthetic and trace-based) when combined with two different write modes. Numerical results show that the more complex of the two write modes, which requires hot/cold data identification, may not necessarily give rise to a significantly better SSD endurance. Further, the d-choices GC algorithm is often shown to strike a good balance between garbage collection and wear leveling for small d values (e.g., d = 10), yielding high endurance.
An Iterative Post-processing Approach for Speech Enhancement

Speech enhancement has been widely used in speech recognition, multimedia systems and hearing aids etc. In this study, we explore a new post-processing strategy for speech enhancement. The main goal of proposed post-processing method is to reduce speech distortion and improve speech quality and intelligibility after enhancement. First, a masking-based speech enhancement system based on deep neural network is implemented. Then, an iterative global variance equalization post-processing is proposed to adopt on estimated masks. We evaluate the intelligibility and quality of enhanced speech and observe that the proposed post-processing method achieves higher speech intelligibility and less speech distortion at low signal-to-noise ratios (SNRs) comparing to the baseline system without post-processing or previous post-processing methods. The experiments under unseen noises also show that the proposed post-processing strategy can improve the model generalization at multiple noise types.
Intra-Class Recognition of Fruits Using DCNN for Commercial Trace Back-System

Intra-class recognition of fruit using image processing and computer vision techniques, is considered challenging task due to similarities between various type of fruits and external environmental changes such as lighting. Mainly sub-type of the same fruit shows a much similarities between each other so, it's more difficult task to distinguish than when different types with same color of fruits are involved. It creates mismatches between training and test set. The problem become more difficult when lighting changes which tend to change the actual characteristic of the fruits like contour shape. To solve the problem of intra-class recognition, we proposed a deep neural network model with only few layers which learn optimal features from an input image adaptively. Our proposed method has been tested on 2 different fruit types and 2 sub classes of each fruit. To show the robustness of the classifier on sub classes of fruits. Extensive experiments have been done on dataset of 5602 apple fruit images & 4292 kiwi fruit images. The proposed model shows satisfactory performance across different fruit types and sub-types.