Miracle-dz/UniFAR-FTUs huggingface dataset:
Ja n 20 07 Mixtures of Bose gases under rotation

One of the many interesting aspects of the field of cold atoms is that one may create mixtures of different species. The equilibrium density distribution of the atoms is an interesting problem by itself, since the different components may coexist, or separate, depending on the value of the coupling constants between the atoms of the same and of the different species. If this system rotates, the problem becomes even more interesting. In this case, the state of lowest energy may involve rotation of either one of the components, or rotation of all the components. Actually, the first vortex state in cold gases of atoms was observed experimentally in a two-component system [1], following the theoretical suggestion of Ref. [2]. More recently, vortices have also been created and observed in spinor Bose-Einstein condensates [3, 4]. Theoretically, there have been several studies of this problem [5, 6, 7], mostly in the case where the number of vortices is relatively large. Kasamatsu, Tsubota, and Ueda have also given a review of the work that has been done on this problem [8]. In this Letter, we consider a rotating two-component Bose gas in the limit of weak interactions and slow rotation, where the number of vortices is of order unity. Surprisingly, a number of exact analytical results exist for the energy of this system. The corresponding manybody wavefunction also has a relatively simple structure. We assume equal masses M for the two components, and a harmonic trapping potential Vt = M(ω ρ + ω zz )/2, with ρ = x + y. The trapping frequency ωz along the axis of rotation is assumed to be much higher than ω. In addition, we consider weak atomatom interactions, much smaller than the oscillator energy h̄ω, and work within the subspace of states of the lowest Landau level. The motion of the atoms is thus frozen along the axis of rotation and our problem becomes quasi-two-dimensional [9]. The relevant eigenstates are Φm(ρ, θ)φ0(z), where Φm(ρ, θ) are the lowestLandau-level eigenfunctions of the two-dimensional oscillator with angular momentum mh̄, and φ0(z) is the lowest harmonic oscillator eigenstate along the z axis. The assumption of weak interactions also excludes the possibility of phase separation in the absence of rotation [10], since the atoms of both species reside in the lowest state Φ0,0(r) = Φ0(ρ, θ)φ0(z), while the depletion of the condensate due to the interaction may be treated perturbatively. We label the two (distinguishable) components of the gas as A and B. In what follows the atomatom interaction is assumed to be a contact potential of equal scattering lengths for collisions between the same species and the different ones, aAA = aBB = aAB = a. The interaction energy is measured in units of v0 = U0 ∫
Detection of Malware using Machine Learning in Android Devices/Applications

Spreading malware through Android devices and applications became an important strategy of cyber attackers. Therefore, malware detection in Android applications has become an important area of research. In this context, it is important to answer the question that reads “how can we develop a model based on Machine Learning (ML) to detect malware in Android devices/applications? ” When malware is detected in real time from Android mobile applications, it can relive the users of Android phones from the risk of malware. It will also help stakeholders of Android devices to be safe from malicious software. The proposed system extracts feature from. APK files and training is given for supervised learning. Different ML models like Multinomial Naïve Bayes, Random Forest and SVM are used as prediction models. With these ML techniques a framework is realized to have provision for protection of malware in Android devices or applications. The proposed solution continues giving support with increased quality. The rationale behind this is that as the applications are protected and malware is detected, the training data gets increased. With increased training data, it will become much more accurate as time goes on. With some changes, it can be made to detect Android applications live when it is associated with a competing device.
Generalisations of Matrix Partitions : Complexity and Obstructions

A trigraph is a graph where each pair of vertices is labelled either 0 (a non-edge), 1 (an edge) or $\star$ (both an edge and a non-edge). In a series of papers, Hell et al. proposed to study the complexity of homomorphisms from graphs to trigraphs, called Matrix Partition Problems, where edges and non-edges can be both mapped to $\star$-edges, while a non-edge cannot be mapped to an edge, and vice-versa. Even though, Matrix Partition Problems are generalisations of CSPs, they share with them the property of being"intrinsically"combinatorial. So, the question of a possible dichotomy, i.e. P-time vs NP-complete, is a very natural one and raised in Hell et al.'s papers. We propose in this paper to study Matrix Partition Problems on relational structures, wrt a dichotomy question, and, in particular, homomorphisms between trigraphs. We first show that trigraph homomorphisms and Matrix Partition Problems are P-time equivalent, and then prove that one can also restrict (wrt dichotomy) to relational structures with one relation. Failing in proving that Matrix Partition Problems on directed graphs are not P-time equivalent to Matrix Partitions on relational structures, we give some evidence that it is unlikely by showing that reductions used in the case of CSPs cannot work. We turn then our attention to Matrix Partitions with finite sets of obstructions. We show that, for a fixed trigraph, the set of inclusion-wise minimal obstructions is finite for directed graphs if and only if it is finite for trigraphs. We also prove similar results for relational structures. We conclude by showing that on trees (seen as trigraphs) it is NP-complete to decide whether a given tree has a trigraph homomorphism to another input trigraph. The latter shows a notable difference on tractability between CSP and Matrix Partition as it is well-known that CSP is tractable on the class of trees.
Helical liquids in semiconductors

One-dimensional helical liquids can appear at boundaries of certain condensed matter systems. Two prime examples are the edge of a quantum spin Hall insulator and the hinge of a three-dimensional second-order topological insulator. For these materials, the presence of a helical state at the boundary serves as a signature of their nontrivial electronic bulk topology. Additionally, these boundary states are of interest themselves, as a novel class of strongly correlated low-dimensional systems with interesting potential applications. Here, we review existing results on such helical liquids in semiconductors. Our focus is on the theory, though we confront it with existing experiments. We discuss various aspects of the helical liquids, such as their realization, topological protection and stability, or possible experimental characterization. We lay emphasis on the hallmark of these states, being the prediction of a quantized electrical conductance. Since so far reaching a well-quantized conductance has remained challenging experimentally, a large part of the review is a discussion of various backscattering mechanisms which have been invoked to explain this discrepancy. Finally, we include topics related to proximity-induced topological superconductivity in helical states, as an exciting application toward topological quantum computation with the resulting Majorana bound states.
Can't See the Forest for the Trees: Navigating Metric Spaces by Bounded Hop-Diameter Spanners

Spanners for metric spaces have been extensively studied, perhaps most notably in low-dimensional Euclidean spaces - due to their numerous applications. Euclidean spanners can be viewed as means of compressing the (n2) pairwise distances of a d-dimensional Euclidean space into O(n) = O∈,d (n) spanner edges, so that the spanner distances preserve the original distances to within a factor of 1 + ε, for any ε > 0. Moreover, one can compute such spanners efficiently in the standard centralized and distributed settings. Once the spanner has been computed, it serves as a "proxy" overlay network, on which the computation can proceed, which gives rise to huge savings in space and other important quality measures. The original metric enables us to "navigate" optimally - a single
Superconducting triplet pairings and anisotropic tunneling-magnetoresistance effects in ferromagnet/superconductor/ferromagnet double-barrier junctions

Ferromagnetic spin valves offer the key building blocks to integrate giantand tunneling-magnetoresistance effects into spintronics devices. Starting from a generalized Blonder–Tinkham–Klapwijk approach, we theoretically investigate the impact of interfacial Rashba and Dresselhaus spin-orbit couplings on the tunneling conductance, and thereby the tunneling-magnetoresistance characteristics, of ferromagnet/superconductor/ferromagnet spin-valve junctions embedding thin superconducting spacers between the either parallel or antiparallel magnetized ferromagnets. We focus on the unique interplay between usual electron tunnelings—that fully determine the tunneling magnetoresistance in the normal-conducting state—and the peculiar Andreev reflections in the superconducting state. In the presence of interfacial spin-orbit couplings, special attention needs to be paid to the spin-flip (“unconventional”) Andreev-reflection process that is expected to induce superconducting triplet correlations in proximitized regions. As a transport signature of these triplet pairings, we detect conductance double-peaks around the singlet-gap energy, reflecting the competition between the singlet and the newly emerging triplet gap. We thoroughly analyze the Andreev reflections’ role in connection with superconducting tunneling-magnetoresistance phenomena, and eventually unravel huge conductance and tunneling-magnetoresistance magnetoanisotropies—easily exceeding their normal-state counterparts by several orders of magnitude—as another experimentally accessible fingerprint of unconventional Andreev reflections. Our results provide an important contribution to establish superconducting magnetic spin valves as an essential ingredient for future superconducting-spintronics concepts.
Downlink Analysis of LEO Multi-Beam Satellite Communication in Shadowed Rician Channels

The coming extension of cellular technology to base-stations in low-earth orbit (LEO) requires a fresh look at terrestrial 3GPP channel models. Relative to such models, sky-to-ground cellular channels will exhibit less diffraction, deeper shadowing, larger Doppler shifts, and possibly far stronger cross-cell interference: consequences of high elevation angles and extreme “sectorization” of LEO satellite transmissions into partially-overlapping spot beams. To permit forecasting of expected signal-to-noise ratio (SNR), interference-to-noise ratio (INR), and probability of outage, we characterize the powers of desired and interference signals as received by ground users from such a LEO satellite. In particular, building on the Shadowed Rician (SR) channel model, we observe that co-cell and cross-cell sky-to-ground signals travel along similar paths, whereas terrestrial co- and cross-cell signals travel along very different paths. We characterize SNR, signal-to-interference ratio (SIR), and INR using transmit beam profiles and linear relationships that we establish between certain SR random variables. These tools allow us to simplify certain density functions and moments, facilitating future analysis. Numerical results yield insight into the key question of whether emerging LEO systems should be viewed as interference- or noise-limited.
Limiting dynamics for Q-learning with memory one in two-player, two-action games

We develop a computational method to identify all pure strategy equilibrium points in the strategy space of the two-player, two-action repeated games played by Q-learners with one period memory. In order to approximate the dynamics of these Q-learners, we construct a graph of pure strategy mutual best-responses. We apply this method to the iterated prisoner’s dilemma and find that there are exactly three absorbing states. By analyzing the graph for various values of the discount factor, we find that, in addition to the absorbing states, limit cycles become possible. We confirm our results using numerical simulations.
Who Owns the Data? A Systematic Review at the Boundary of Information Systems and Marketing

This paper gives a systematic research review at the boundary of the information systems (IS) and marketing disciplines. First, a historical overview of these disciplines is given to put the review into context. This is followed by a bibliographic analysis to select articles at the boundary of IS and marketing. Text analysis is then performed on the selected articles to group them into homogeneous research clusters, which are refined by selecting “distinct” articles that best represent the clusters. The citation asymmetries between IS and marketing are noted and an overall conceptual model is created that describes the “areas of collaboration” between IS and marketing. Forward looking suggestions are made on how academic researchers can better interface with industry and how academic research at the boundary of IS and marketing can be further developed.
Using Perturbed Length-aware Positional Encoding for Non-autoregressive Neural Machine Translation

Non-autoregressive neural machine translation (NAT) usually employs sequence-level knowledge distillation using autoregressive neural machine translation (AT) as its teacher model. However, a NAT model often outputs shorter sentences than an AT model. In this work, we propose sequence-level knowledge distillation (SKD) using perturbed length-aware positional encoding and apply it to a student model, the Levenshtein Transformer. Our method outperformed a standard Levenshtein Transformer by 2.5 points in bilingual evaluation understudy (BLEU) at maximum in a WMT14 German to English translation. The NAT model output longer sentences than the baseline NAT models.
Day-to-day and seasonal regularity of network passenger delay for metro networks

In an effort to improve user satisfaction and transit image, transit service providers worldwide offer delay compensations. Smart card data enables the estimation of passenger delays throughout the network and aid in monitoring service performance. Notwithstanding, in order to prioritize measures for improving service reliability and hence reducing passenger delays, it is paramount to identify the system components - stations and track segments - where most passenger delay occurs. To this end, we propose a novel method for estimating network passenger delay from individual trajectories. We decompose the delay along a passenger trajectory into its corresponding track segment delay, initial waiting time and transfer delay. We distinguish between two different types of passenger delay in relation to the public transit network: average passenger delay and total passenger delay. We employ temporal clustering on these two quantities to reveal daily and seasonal regularity in delay patterns of the transit network. The estimation and clustering methods are demonstrated on one year of data from Washington metro network. The data consists of schedule information and smart card data which includes passenger-train assignment of the metro network for the months of August 2017 to August 2018. Our findings show that the average passenger delay is relatively stable throughout the day. The temporal clustering reveals pronounced and recurrent and thus predictable daily and weekly patterns with distinct characteristics for certain months.
Learning How to Listen: Automatically Finding Bug Patterns in Event-Driven JavaScript APIs

Event-driven programming is widely practiced in the JavaScript community, both on the client side to handle UI events and AJAX requests, and on the server side to accommodate long-running operations such as file or network I/O. Many popular event-based APIs allow event names to be specified as free-form strings without any validation, potentially leading to lost events for which no listener has been registered and dead listeners for events that are never emitted. In previous work, Madsen et al. presented a precise static analysis for detecting such problems, but their analysis does not scale because it may require a number of contexts that is exponential in the size of the program. Concentrating on the problem of detecting dead listeners, we present an approach to learn how to use event-based APIs by first mining a large corpus of JavaScript code using a simple static analysis to identify code snippets that register an event listener, and then applying statistical modeling to identify anomalous patterns, which often indicate incorrect API usage. In a large-scale evaluation on 127,531 open-source JavaScript code bases, our technique was able to detect 75 anomalous listener-registration patterns, while maintaining a precision of 90.9% and recall of 7.5% over a validation set, demonstrating that a learning-based approach to detecting event-handling bug patterns is feasible. In an additional experiment, we investigated instances of these patterns in 25 open-source projects, and reported 30 issues to the project maintainers, of which 7 have been confirmed as bugs.
United We Learn Better: Harvesting Learning Improvements From Class Hierarchies Across Tasks

Attempts of learning from hierarchical taxonomies in computer vision have been mostly focusing on image classification. Though ways of best harvesting learning improvements from hierarchies in classification are far from being solved, there is a need to target these problems in other vision tasks such as object detection. As progress on the classification side is often dependent on hierarchical cross-entropy losses, novel detection architectures using sigmoid as an output function instead of softmax cannot easily apply these advances, requiring novel methods in detection. In this work we establish a theoretical framework based on probability and set theory for extracting parent predictions and a hierarchical loss that can be used across tasks, showing results across classification and detection benchmarks and opening up the possibility of hierarchical learning for sigmoid-based detection architectures.
Pitch-Informed Instrument Assignment Using a Deep Convolutional Network with Multiple Kernel Shapes

This paper proposes a deep convolutional neural network for performing note-level instrument assignment. Given a polyphonic multi-instrumental music signal along with its ground truth or predicted notes, the objective is to assign an instrumental source for each note. This problem is addressed as a pitch-informed classification task where each note is analysed individually. We also propose to utilise several kernel shapes in the convolutional layers in order to facilitate learning of efficient timbre-discriminative feature maps. Experiments on the MusicNet dataset using 7 instrument classes show that our approach is able to achieve an average F-score of 0.904 when the original multi-pitch annotations are used as the pitch information for the system, and that it also excels if the note information is provided using third-party multi-pitch estimation algorithms. We also include ablation studies investigating the effects of the use of multiple kernel shapes and comparing different input representations for the audio and the note-related information.
Probabilistic and Geometric Depth: Detecting Objects in Perspective

3D object detection is an important capability needed in various practical applications such as driver assistance systems. Monocular 3D detection, as a representative general setting among image-based approaches, provides a more economical solution than conventional settings relying on LiDARs but still yields unsatisfactory results. This paper first presents a systematic study on this problem. We observe that the current monocular 3D detection can be simplified as an instance depth estimation problem: The inaccurate instance depth blocks all the other 3D attribute predictions from improving the overall detection performance. Moreover, recent methods directly estimate the depth based on isolated instances or pixels while ignoring the geometric relations across different objects. To this end, we construct geometric relation graphs across predicted objects and use the graph to facilitate depth estimation. As the preliminary depth estimation of each instance is usually inaccurate in this ill-posed setting, we incorporate a probabilistic representation to capture the uncertainty. It provides an important indicator to identify confident predictions and further guide the depth propagation. Despite the simplicity of the basic idea, our method, PGD, obtains significant improvements on KITTI and nuScenes benchmarks, achieving 1st place out of all monocular vision-only methods while still maintaining real-time efficiency. Code and models will be released at https://github.com/open-mmlab/mmdetection3d.
To Boost or not to Boost: On the Limits of Boosted Neural Networks

Boosting is a method for finding a highly accurate hypothesis by linearly combining many ``weak"hypotheses, each of which may be only moderately accurate. Thus, boosting is a method for learning an ensemble of classifiers. While boosting has been shown to be very effective for decision trees, its impact on neural networks has not been extensively studied. We prove one important difference between sums of decision trees compared to sums of convolutional neural networks (CNNs) which is that a sum of decision trees cannot be represented by a single decision tree with the same number of parameters while a sum of CNNs can be represented by a single CNN. Next, using standard object recognition datasets, we verify experimentally the well-known result that a boosted ensemble of decision trees usually generalizes much better on testing data than a single decision tree with the same number of parameters. In contrast, using the same datasets and boosting algorithms, our experiments show the opposite to be true when using neural networks (both CNNs and multilayer perceptrons (MLPs)). We find that a single neural network usually generalizes better than a boosted ensemble of smaller neural networks with the same total number of parameters.
Domain-matched Pre-training Tasks for Dense Retrieval

Pre-training on larger datasets with ever increasing model size is now a proven recipe for increased performance across almost all NLP tasks. A notable exception is information retrieval, where additional pre-training has so far failed to produce convincing results. We show that, with the right pre-training setup, this barrier can be overcome. We demonstrate this by pre-training large bi-encoder models on 1) a recently released set of 65 million synthetically generated questions, and 2) 200 million post-comment pairs from a preexisting dataset of Reddit conversations made available by pushshift.io. We evaluate on a set of information retrieval and dialogue retrieval benchmarks, showing substantial improvements over supervised baselines.