Miracle-dz/UniFAR-FTUs huggingface dataset:
Prabhupadavani: A Code-mixed Speech Translation Data for 25 Languages

Nowadays, the interest in code-mixing has become ubiquitous in Natural Language Processing (NLP); however, not much attention has been given to address this phenomenon for Speech Translation (ST) task. This can be solely attributed to the lack of code-mixed ST task labelled data. Thus, we introduce Prabhupadavani, which is a multilingual code-mixed ST dataset for 25 languages. It is multi-domain, covers ten language families, containing 94 hours of speech by 130+ speakers, manually aligned with corresponding text in the target language. The Prabhupadavani is about Vedic culture and heritage from Indic literature, where code-switching in the case of quotation from literature is important in the context of humanities teaching. To the best of our knowledge, Prabhupadvani is the first multi-lingual code-mixed ST dataset available in the ST literature. This data also can be used for a code-mixed machine translation task. All the dataset can be accessed at: https://github.com/frozentoad9/CMST.
Two New Black Widow Millisecond Pulsars in M28

We report the discovery of two Black Widow millisecond pulsars in the globular cluster (GC) M28 with the MeerKAT telescope. PSR J1824−2452M (M28M) is a 4.78 ms pulsar in a 5.82 hr orbit, and PSR J1824−2452N (M28N) is a 3.35 ms pulsar in a 4.76 hr orbit. Both pulsars have dispersion measures near 119.30 pc cm−3 and have low-mass companion stars (∼0.01–0.03 M ⊙) that do not cause strong radio eclipses or orbital variations. Including these systems, there are now five known black widow pulsars in M28. The pulsar searches were conducted as a part of an initial phase of MeerKAT’s GC census (within the TRAPUM Large Survey Project). These faint discoveries demonstrate the advantages of MeerKAT’s survey sensitivity over previous searches, and we expect to find additional pulsars in continued searches of this cluster.
Stock2Vec: An Embedding to Improve Predictive Models for Companies

Building predictive models for companies often relies on inference using historical data of companies in the same industry sector. However, companies are similar across a variety of dimensions that should be leveraged in relevant prediction problems. This is particularly true for large, complex organizations which may not be well defined by a single industry and have no clear peers. To enable prediction using company information across a variety of dimensions, we create an embedding of company stocks, Stock2Vec, which can be easily added to any prediction model that applies to companies with associated stock prices. We describe the process of creating this rich vector representation from stock price fluctuations, and characterize what the dimensions represent. We then conduct comprehensive experiments to evaluate this embedding in applied machine learning problems in various business contexts. Our experiment results demonstrate that the four features in the Stock2Vec embedding can readily augment existing cross-company models and enhance cross-company predictions.
Robust Augmentation for Multivariate Time Series Classification

Neural networks are capable of learning powerful representations of data, but they are susceptible to overfitting due to the number of parameters. This is particularly challenging in the domain of time series classification, where datasets may contain fewer than 100 training examples. In this paper, we show that the simple methods of cutout, cutmix, mixup, and window warp improve the robustness and overall performance in a statistically significant way for convolutional, recurrent, and self-attention based architectures for time series classification. We evaluate these methods on 26 datasets from the University of East Anglia Multivariate Time Series Classification (UEA MTSC) archive and analyze how these methods perform on different types of time series data. We show that the InceptionTime network with augmentation improves accuracy by 1% to 45% in 18 different datasets compared to without augmentation. We also show that augmentation improves accuracy for recurrent and self attention based architectures.
Exact Optimal Accelerated Complexity for Fixed-Point Iterations

Despite the broad use of ﬁxed-point iterations throughout applied mathematics, the optimal convergence rate of general ﬁxed-point problems with nonexpansive nonlinear operators has not been established. This work presents an acceleration mechanism for ﬁxed-point iterations with nonexpansive operators, contractive operators, and nonexpansive operators satisfying a H ¨ older-type growth condition. We then provide matching complexity lower bounds to establish the exact optimality of the acceleration mechanisms in the nonexpansive and contractive setups. Finally, we provide experiments with CT imaging, optimal transport, and decentralized optimization to demonstrate the practical effectiveness of the acceleration mechanism.
CacheFX: A Framework for Evaluating Cache Security

Over the last two decades, the danger of sharing resources between programs has been repeatedly highlighted. Multiple side-channel attacks, which seek to exploit shared components for leaking information, have been devised, mostly targeting shared caching components. In response, the research community has proposed multiple cache designs that aim at curbing the source of side channels. With multiple competing designs, there is a need for assessing the level of security against side-channel attacks that each design offers. Several metrics have been suggested for performing such evaluations. However, these tend to be limited both in terms of the potential adversaries they consider and in the applicability of the metric to real-world attacks, as opposed to attack techniques. Moreover, all existing metrics implicitly assume that a single metric can encompass the nuances of side-channel security. In this work we propose CacheFX, a flexible framework for assessing and evaluating the resilience of cache designs to side-channel attacks. CacheFX allows the evaluator to implement various cache designs, victims, and attackers, as well as to exercise them for assessing the leakage of information via the cache. To demonstrate the power of CacheFX, we implement multiple cache designs and replacement algorithms, and devise three evaluation metrics that measure different aspects of the caches: (1) the entropy induced by a memory access; (2) the complexity of building an eviction set; (3) protection against cryptographic attacks; Our experiments highlight that different security metrics give different insights to designs, making a comprehensive analysis mandatory. For instance, while eviction-set building was fastest for randomized skewed caches, these caches featured lower eviction entropy and higher practical attack complexity. Our experiments show that all non-partitioned designs allow for effective cryptographic attacks. However, in state-of-the-art secure caches, eviction-based attacks are more difficult to mount than occupancy-based attacks, highlighting the need to consider the latter in cache design.
Geo-indistinguishable Mechanisms for Spatial Crowdsourcing via Multi-Objective Evolutionary Optimization

—Industrial Internet of Things (IIoT) has exploded key revolutions in several leading industries, such as energy, agriculture, mining, transportation, and healthcare. Due to the nature of high capacity and fast transmission speed, 5G plays a pivot role in enhancing the industrial procedures, practices and guidelines, such as crowdsourcing, cloud outsourcing and platform subcontracting. Spatial crowdsourcing (SC)-servers (such as offered by DiDi, MeiTuan and Uber) assign different tasks based on workers’ location information. However, SC-servers are often untrustworthy and have the threat of revealing workers’ privacy. In this paper, we introduce a framework Geo-MOEA (Multi-Objective Evolutionary Algorithm) to protect location privacy of workers involved on SC platform in 5G environment. We propose an adaptive regionalized obfuscation mechanism with inference error bounds based on geo-indistinguishability (a strong notion of differential privacy), which is suitable for the context of large-scale location data and task allocations. This offers locally generated pseudo-locations of workers to be reported instead of their actual locations. Further, to optimize the trade-off between SC service availability and privacy protection, we utilize MOEA to improve the global applicability of the mechanism in 5G environment. Finally, by simulating the location scenario, the visual results on experiments show that the mechanism can not only protect location privacy, but also achieve high availability of services as desired.
Rapid Mass Parameter Estimation of Binary Black Hole Coalescences Using Deep Learning

Deep learning can be used to drastically decrease the processing time of parameter estimation for coalescing binaries of compact objects including black holes and neutron stars detected in gravitational waves (GWs). As a first step, we present two neural network models trained to rapidly estimate the posterior distributions of the chirp mass and mass ratio of a detected binary black hole system from the GW strain data of LIGO Hanford and Livingston Observatories. Using these parameters the component masses can be predicted, which has implications for the prediction of the likelihood that a merger contains a neutron star. The results are compared to the ‘gold standard’ of parameter estimation of gravitational waves used by the LIGO-Virgo Collaboration (LVC), LALInference. Our models predict posterior distributions consistent with that from LALInference while using orders of magnitude less processing time once the models are trained. The median predictions are within the 90% credible intervals of LALInference for all predicted parameters when tested on real binary black hole events detected during the LVC’s first and second observing runs. We argue that deep learning has strong potential for low-latency high-accuracy parameter estimation suitable for real-time GW search pipelines.
Head and Eye Egocentric Gesture Recognition for Human-Robot Interaction Using Eyewear Cameras

Non-verbal communication plays a particularly important role in a wide range of scenarios in Human-Robot Interaction (HRI). Accordingly, this work addresses the problem of human gesture recognition. In particular, we focus on head and eye gestures, and adopt an egocentric (first-person) perspective using eyewear cameras. We argue that this egocentric view may offer a number of conceptual and technical benefits over scene- or robot-centric perspectives. A motion-based recognition approach is proposed, which operates at two temporal granularities. Locally, frame-to-frame homographies are estimated with a convolutional neural network (CNN). The output of this CNN is input to a long short-term memory (LSTM) to capture longer-term temporal visual relationships, which are relevant to characterize gestures. Regarding the configuration of the network architecture, one particularly interesting finding is that using the output of aninternal layer of the homography CNN increases the recognition rate with respect to using the homography matrix itself. While this work focuses on action recognition, and no robot or user study has been conducted yet, the system has been designed to meet real-time constraints. The encouraging results suggest that the proposed egocentric perspective is viable, and this proofof-concept work provides novel and useful contributions to the exciting area of HRI.
Rotational Modulation of Spectroscopic Zeeman Signatures in Low-mass Stars

Accurate tracers of the stellar magnetic field and rotation are cornerstones for the study of M dwarfs and for reliable detection and characterization of their exoplanetary companions. Such measurements are particularly challenging for old, slowly rotating, fully convective M dwarfs. To explore the use of new activity and rotation tracers, we examined multiyear near-infrared (NIR) spectroscopic monitoring of two such stars—GJ 699 (Barnard’s Star) and Teegarden’s Star—carried out with the Habitable-zone Planet Finder spectrograph. We detected periodic variations in absorption line widths across the stellar spectrum, with higher amplitudes toward longer wavelengths. We also detected similar variations in the strength and width of the 12435.67 Å neutral potassium (K i) line, a known tracer of the photospheric magnetic field. Attributing these variations to rotational modulation, we confirm the known 145 ± 15 day rotation period of GJ 699, and measure the rotation period of Teegarden’s Star to be 99.6 ± 1.4 days. Based on simulations of the K i line and the wavelength dependence of the line-width signal, we argue that the observed signals are consistent with varying photospheric magnetic fields and the associated Zeeman effect. These results highlight the value of detailed line profile measurements in the NIR for diagnosing stellar magnetic field variability. Such measurements may be pivotal for disentangling activity and exoplanet-related signals in spectroscopic monitoring of old, low-mass stars.
Compact-object Formation, Retention, and Growth through Accretion onto Gas-embedded White Dwarfs/Neutron Stars in Gas-enriched Globular Clusters

Observations of pulsars in globular clusters (GCs) give evidence that more than >10%–20% of neutron stars (NSs) that ever formed in GCs were retained there. However, the velocity distribution of field pulsars peaks at 5–10 times the escape velocities of GCs. Consequently, only a small fraction of GC NSs should have been retained, which is potentially difficult to explain even accounting for low-velocity NSs formed through electron-capture supernovae (SNe). Thus, too few low-velocity NSs should have been retained, giving rise to the NS retention problem in GCs. Here we suggest a novel solution, in which the progenitors of most GC NSs were ONe white dwarfs (WDs) that accreted ambient intracluster gas and formed low-velocity NSs through accretion-induced collapse (AIC). The existence of an early gas-enriched environment in GCs is supported by observations of multiple stellar populations in GCs. It is thought that 10–100s of megayears after the formation of the first generation of stars, and after ONe WDs were already formed, GCs were replenished with gas, which formed a second generation of stars. Accretion of such replenished gas onto the ONe WDs catalyzed the AIC processes. The number of AIC-formed NSs is then sufficient to explain the large number of NSs retained in GCs. Similar processes might also drive CO WDs to produce Type Ia SNe or to merge and form NSs and similarly drive NSs to AIC and mergers producing BHs. Moreover, the wide variety of gas-catalyzed binary mergers and explosive transients suggested to occur in the gas-rich environments of an active galactic nucleus disk could similarly, and even more efficiently, occur in second-generation gas in GCs.
Gap Minimization for Knowledge Sharing and Transfer

Learning from multiple related tasks by knowledge sharing and transfer has become increasingly relevant over the last two decades. In order to successfully transfer information from one task to another, it is critical to understand the similarities and differences between the domains. In this paper, we introduce the notion of \emph{performance gap}, an intuitive and novel measure of the distance between learning tasks. Unlike existing measures which are used as tools to bound the difference of expected risks between tasks (e.g., $\mathcal{H}$-divergence or discrepancy distance), we theoretically show that the performance gap can be viewed as a data- and algorithm-dependent regularizer, which controls the model complexity and leads to finer guarantees. More importantly, it also provides new insights and motivates a novel principle for designing strategies for knowledge sharing and transfer: gap minimization. We instantiate this principle with two algorithms: 1. gapBoost, a novel and principled boosting algorithm that explicitly minimizes the performance gap between source and target domains for transfer learning; and 2. gapMTNN, a representation learning algorithm that reformulates gap minimization as semantic conditional matching for multitask learning. Our extensive evaluation on both transfer learning and multitask learning benchmark data sets shows that our methods outperform existing baselines.
High-order Line Graphs of Non-uniform Hypergraphs: Algorithms, Applications, and Experimental Analysis

Hypergraphs offer flexible and robust data representations for many applications, but methods that work directly on hypergraphs are not readily available and tend to be prohibitively expensive. Much of the current analysis of hypergraphs relies on first performing a graph expansion – either based on the nodes (clique expansion), or on the hyperedges (line graph) − and then running standard graph analytics on the resulting representative graph. However, this approach suffers from massive space complexity and high computational cost with increasing hypergraph size. Here, we present efficient, parallel algorithms to accelerate and reduce the memory footprint of higher-order graph expansions of hypergraphs. Our results focus on the hyperedge-based s-line graph expansion, but the methods we develop work for higher-order clique expansions as well. To the best of our knowledge, ours is the first framework to enable hypergraph spectral analysis of a large dataset on a single shared-memory machine. Our methods enable the analysis of datasets from many domains that previous graph-expansion-based models are unable to provide. The proposed s-line graph computation algorithms are orders of magnitude faster than state-of-the-art sparse general matrix-matrix multiplication methods, and obtain approximately 2–31× speedup over a prior state-of-the-art heuristic-based algorithm for $s$-line graph computation.
Dissecting the impact of different loss functions with gradient surgery

Pair-wise loss is an approach to metric learning that learns a semantic embedding by optimizing a loss function that encourages images from the same semantic class to be mapped closer than images from different classes. The literature reports a large and growing set of variations of the pair-wise loss strategies. Here we decompose the gradient of these loss functions into components that relate to how they push the relative feature positions of the anchor-positive and anchor-negative pairs. This decomposition allows the unification of a large collection of current pair-wise loss functions. Additionally, explicitly constructing pair-wise gradient updates to separate out these effects gives insights into which have the biggest impact, and leads to a simple algorithm that beats the state of the art for image retrieval on the CAR, CUB and Stanford Online products datasets.
Multi-Access Cache-Aided Multi-User Private Information Retrieval

We consider the problem of multi-access cache-aided multi-user Private Information Retrieval (MuPIR). In this problem, several files are replicated across multiple servers. There are $K$ users and $C$ cache nodes. Each user can access $L$ cache nodes, and every cache node can be accessed by several users. Each user wants to retrieve one file from the servers, but the users do not want the servers to know their demands. Before the users decide their respective demands, servers will fill the cache nodes from the content of the files. Users will then request their desired files from the servers. Servers will perform coded transmissions, and all the users should get their desired files from these transmissions and the content placed in the caches they are accessing. It is required that any individual server should not get any information about the demands of the users. This problem is an extension of the dedicated cache-aided MuPIR problem, which itself generalizes the widely studied single user PIR setup. In this paper, we propose a MuPIR scheme which utilizes a multi-access setup of the coded caching problem. The presented scheme is order optimal when $K=\binom{C}{L}$ users. We also characterize the rate of the scheme for the special case of cyclic wraparound multi-access setup, where $C=K$ and each user access $L$ consecutive cache nodes in cyclic wraparound fashion.
Nonlinearity and wavelength control in ultrashort-pulse subsurface material processing

The pronounced dependence of the nonlinear parameters of both dielectric and semiconductor materials on the wavelength, and the nonlinear interaction between the ultra-short laser pulse and the material requires precise control of the wavelength of the pulse, in addition to the precise control of the pulse energy, pulse duration and focusing optics. This becomes particularly important for fine sub-wavelength single pulse sub-surface processing. Based on two different numerical models and taking Si as example material, we investigate the spatio-temporal behavior of a pulse propagating through the material while covering a broad range of parameters. The wavelength-dependence of material processing depends on the different contributions of twoand tree-photon absorption in combination with the Kerr effect which results in a particularly sharp nonlinear peak at ≈ 2100 nm. We could show that in silicon this makes processing preferable close to this wavelength. The impact of the nonlinear nonparaxial propagation effects on spatio-temporal beam structure is also investigated. It could be shown that with increasing wavelength and large focusing angles the aberrations at the focal spot can be reduced, and thereby cleaner and more precise processing can be achieved. Finally, we could show that the optimum energy transfer from the pulse to the material is within a narrow window of pulse durations between 600 to 900 fs.
Reasoning Like Program Executors

Reasoning over natural language is a long-standing goal for the research community. However, studies have shown that existing language models are inadequate in reasoning. To address the issue, we present POET, a novel reasoning pre-training paradigm. Through pre-training language models with programs and their execution results, POET empowers language models to harvest the reasoning knowledge possessed by program executors via a data-driven approach. POET is conceptually simple and can be instantiated by different kinds of program executors. In this paper, we showcase two simple instances POET-Math and POET-Logic, in addition to a complex instance, POET-SQL. Experimental results on six benchmarks demonstrate that POET can significantly boost model performance in natural language reasoning, such as numerical reasoning, logical reasoning, and multi-hop reasoning. POET opens a new gate on reasoning-enhancement pre-training, and we hope our analysis would shed light on the future research of reasoning like program executors.
Learning Constrained Adaptive Differentiable Predictive Control Policies With Guarantees

We present differentiable predictive control (DPC), a method for learning constrained neural control policies for linear systems with probabilistic performance guarantees. We employ automatic differentiation to obtain direct policy gradients by backpropagating the model predictive control (MPC) loss function and constraints penalties through a differentiable closed-loop system dynamics model. We demonstrate that the proposed method can learn parametric constrained control policies to stabilize systems with unstable dynamics, track time-varying references, and satisfy nonlinear state and input constraints. In contrast with imitation learning-based approaches, our method does not depend on a supervisory controller. Most importantly, we demonstrate that, without losing performance, our method is scalable and computationally more efficient than implicit, explicit, and approximate MPC.