Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Lexical Borrowings from Dominant Languages in Multilingual Wordlists

Language contact is a pervasive phenomenon reflected in the borrowing of words from donor to recipient languages. Most computational approaches to borrowing detection treat all languages under study as equally important, even though dominant languages have a stronger impact on heritage languages than vice versa. We test new methods for lexical borrowing detection in contact situations where dominant languages play an important role, applying two classical sequence comparison methods and one machine learning method to a sample of seven Latin American languages which have all borrowed extensively from Spanish. All systems perform well, with the supervised machine learning system outperforming the classical systems. A review of detection errors shows that borrowing detection could be substantially improved by taking into account donor words with divergent meanings from recipient words.
Parameterized Algorithms for Colored Clustering

In the Colored Clustering problem, one is asked to cluster edge-colored (hyper-)graphs whose colors represent interaction types. More specifically, the goal is to select as many edges as possible without choosing two edges that share an endpoint and are colored differently. Equivalently, the goal can also be described as assigning colors to the vertices in a way that fits the edge-coloring as well as possible. As this problem is NP-hard, we build on previous work by studying its parameterized complexity. We give a 2ᴼ⁽ᵏ⁾·nᴼ⁽¹⁾-time algorithm where k is the number of edges to be selected and n the number of vertices. We also prove the existence of a problem kernel of size O(k⁵ᐟ²), resolving an open problem posed in the literature. We consider parameters that are smaller than k, the number of edges to be selected, and r, the number of edges that can be deleted. Such smaller parameters are obtained by considering the difference between k or r and some lower bound on these values. We give both algorithms and lower bounds for Colored Clustering with such parameterizations. Finally, we settle the parameterized complexity of Colored Clustering with respect to structural graph parameters by showing that it is W[1]-hard with respect to both vertex cover number and tree-cut width, but fixed-parameter tractable with respect to local feedback edge number.
Correlations of nuclear charge radii with other nuclear observables

Empirical correlations between nuclear charge radii and several other experimental ground and excited state observables of even–even nuclei are presented for different regions in the nuclear chart. The correlation among the isotonic development of these observables around the classical magic proton numbers is considered starting from Z = 82 to lower magic Z. Special attention is paid to the structural evolution along the isotonic chains around the ‘traditional’ magic proton numbers 8 and 20. The remarkable correlations recognized there indicate shell structure changes and the appearance of new non-traditional single or double magic-like numbers, as Z = 6, 14, 16. The results add new information to the properties of light nuclei. As the mechanisms responsible for the modification of nuclear shell closures are still under discussion, the experimental information obtained is of crucial importance to better constrain the theoretical models. The effect of nuclear deformation on radii and their global correlation over the full mass number region is also investigated.
The Power of External Memory in Increasing Predictive Model Capacity

One way of introducing sparsity into deep networks is by attaching an external table of parameters that is sparsely looked up at different layers of the network. By storing the bulk of the parameters in the external table, one can increase the capacity of the model without necessarily increasing the inference time. Two crucial questions in this setting are then: what is the lookup function for accessing the table and how are the contents of the table consumed? Prominent methods for accessing the table include 1) using words/wordpieces token-ids as table indices, 2) LSH hashing the token vector in each layer into a table of buckets, and 3) learnable softmax style routing to a table entry. The ways to consume the contents include adding/concatenating to input representation, and using the contents as expert networks that specialize to different inputs. In this work, we conduct rigorous experimental evaluations of existing ideas and their combinations. We also introduce a new method, alternating updates, that enables access to an increased token dimension without increasing the computation time, and demonstrate its effectiveness in language modeling.
Distillation Policy Optimization

While on-policy algorithms are known for their stability, they often demand a substantial number of samples. In contrast, off-policy algorithms, which leverage past experiences, are considered sample-efficient but tend to exhibit instability. Can we develop an algorithm that harnesses the benefits of off-policy data while maintaining stable learning? In this paper, we introduce an actor-critic learning framework that harmonizes two data sources for both evaluation and control, facilitating rapid learning and adaptable integration with on-policy algorithms. This framework incorporates variance reduction mechanisms, including a unified advantage estimator (UAE) and a residual baseline, improving the efficacy of both on- and off-policy learning. Our empirical results showcase substantial enhancements in sample efficiency for on-policy algorithms, effectively bridging the gap to the off-policy approaches. It demonstrates the promise of our approach as a novel learning paradigm.
Near-infrared phase cancellation instrument for fast and accurate localization of fluorescent heterogeneity

Near-infrared (NIR) diffuse optical imaging has become a promising method for noninvasive in vivo detection of breast cancer with intrinsic chromophores. Recent developments in molecular specific targeting fluorescent contrast agents offer high tumor to normal tissue contrast, and are capable of selectively labeling various precancer/cancer signatures, thus enhancing both the sensitivity and specificity of cancer detection. To detect a subsurface tumor labeled by fluorescent contrast agents, we have developed a phase cancellation imaging system for fast localization of fluorescent object embedded several centimeters deep inside the turbid media. The instrument is a frequency domain (50 MHz) phase modulation system with dual out-of-phase sources. The excitation wavelength is 780 nm and the fluorescence photons are collected through an 830±10 nm band-pass filter. Localization of fluorescent objects inside the scattering media is accurate using a phase cancellation device. The localization error for a 5 mm d.
The Spatial Kernel Predictor based on Huge Observation Sets

Spatial prediction in an arbitrary location, based on a spatial set of observations, is usually performed by Kriging, being the best linear unbiased predictor (BLUP) in a least-square sense. In order to predict a continuous surface over a spatial domain a grid representation is most often used. Kriging predictions and prediction variances are computed in the nodes of a grid covering the spatial domain, and the continuous surface is assessed from this grid representation. A precise representation usually requires the number of grid nodes to be considerably larger than the number of observations. For a Gaussian random field model the Kriging predictor coinsides with the conditional expectation of the spatial variable given the observation set. An alternative expression for this conditional expectation provides a spatial predictor on functional form which does not rely on a spatial grid discretization. This functional predictor, called the Kernel predictor, is identical to the asymptotic grid infill limit of the Kriging-based grid representation, and the computational demand is primarily dependent on the number of observations - not the dimension of the spatial reference domain nor any grid discretization. We explore the potential of this Kernel predictor with associated prediction variances. The predictor is valid for Gaussian random fields with any eligible spatial correlation function, and large computational savings can be obtained by using a finite-range spatial correlation function. For studies with a huge set of observations, localized predictors must be used, and the computational advantage relative to Kriging predictors can be very large. Moreover, model parameter inference based on a huge observation set can be efficiently made. The methodology is demonstrated in a couple of examples.
Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization

Image geolocalization is the challenging task of predicting the geographic coordinates of origin for a given photo. It is an unsolved problem relying on the ability to combine visual clues with general knowledge about the world to make accurate predictions across geographies. We present $\href{https://huggingface.co/geolocal/StreetCLIP}{\text{StreetCLIP}}$, a robust, publicly available foundation model not only achieving state-of-the-art performance on multiple open-domain image geolocalization benchmarks but also doing so in a zero-shot setting, outperforming supervised models trained on more than 4 million images. Our method introduces a meta-learning approach for generalized zero-shot learning by pretraining CLIP from synthetic captions, grounding CLIP in a domain of choice. We show that our method effectively transfers CLIP's generalized zero-shot capabilities to the domain of image geolocalization, improving in-domain generalized zero-shot performance without finetuning StreetCLIP on a fixed set of classes.
Criteria for Dynamical Timescale Mass Transfer of Metal-poor Intermediate-mass Stars

The stability criteria of rapid mass transfer and common-envelope evolution are fundamental in binary star evolution. They determine the mass, mass ratio, and orbital distribution of many important systems, such as X-ray binaries, type Ia supernovae, and merging gravitational-wave sources. We use our adiabatic mass-loss model to systematically survey intermediate-mass (IM) stars’ thresholds for dynamical timescale mass transfer. The impact of metallicity on the stellar responses and critical mass ratios is explored. Both tables (Z = 0.001) and fitting formulae (Z = 0.001 and Z = 0.02) of the critical mass ratios of IM stars are provided. An application of our results to intermediate-mass X-ray binaries (IMXBs) is discussed. We find that the predicted upper limit to mass ratios, as a function of orbital period, is consistent with the observed IMXBs that undergo thermal or nuclear timescale mass transfer. According to the observed peak X-ray luminosity, L X, we predict the range of L X for IMXBs as a function of the donor mass and the mass-transfer timescale.
Identification for passive robust fault detection using zonotope‐based set‐membership approaches

In this paper, the problem of identification for passive robust fault detection, when a bounded description of the modelling uncertainty is considered, is addressed. Two set‐membership identification methods are introduced to address this problem: the interval predictor and bounded error approaches. These two identification approaches naturally lead to two robust fault detection tests: the direct and inverse tests, respectively, which are also introduced and discussed. Implementation algorithms make use of a zonotope to approximate the parameter uncertainty set. Moreover, underlying hypothesis of both approaches is discussed and applicability conditions are stated. A case study based on a four‐tank system is used to illustrate the applicability and the properties of the two identification approaches as well as the corresponding fault detection. Copyright © 2011 John Wiley & Sons, Ltd.
BRAIN L: A book recommender system

Book sales in Spain have fallen progressively, which requires urgent changes to optimize the sales process as much as possible. This research proposes a new system, called Base of Reasoning in Artificial Intelligence with Natural Language (BRAIN L) focused exclusively on the publishing industry. The new field of knowledge of Artificial Intelligence (AI), Natural Language Processing (NLP), tecnolog\'ia del Machine Learning is combined with Case-Based Reasoning (CBR) techniques for book recommendations. A model is developed to retrieve similar cases/books supported by NLP techniques for decision making. In addition, policies are implemented to keep the model evaluated by expert reviews, where the system not only learns with new cases, but these cases are real.
TAP: Accelerating Large-Scale DNN Training Through Tensor Automatic Parallelisation

Model parallelism has become necessary to train large neural networks. However, finding a suitable model parallel schedule for an arbitrary neural network is a non-trivial task due to the exploding search space. In this work, we present a model parallelism framework TAP that automatically searches for the best data and tensor parallel schedules. Leveraging the key insight that a neural network can be represented as a directed acyclic graph, within which may only exist a limited set of frequent subgraphs, we design a graph pruning algorithm to fold the search space efficiently. TAP runs at sub-linear complexity concerning the neural network size. Experiments show that TAP is $20\times- 160\times$ faster than the state-of-the-art automatic parallelism framework, and the performance of its discovered schedules is competitive with the expert-engineered ones.
Diversity Awareness in Software Engineering Participant Research

Diversity and inclusion are necessary prerequisites for shaping technological innovation that benefits society as a whole. A common indicator of diversity consideration is the representation of different social groups among software engineering (SE) researchers, developers, and students. However, this does not necessarily entail that diversity is considered in the SE research itself.In our study, we examine how diversity is embedded in SE research, particularly research that involves participant studies. To this end, we have selected 79 research papers containing 105 participant studies spanning three years of ICSE technical tracks. Using a content analytical approach, we identified how SE researchers report the various diversity categories of their study participants and investigated: 1) the extent to which participants are described, 2) what diversity categories are commonly reported, and 3) the function diversity serves in the SE studies.We identified 12 different diversity categories reported in SE participant studies. Our results demonstrate that even though most SE studies report on the diversity of participants, SE research often emphasizes professional diversity data, such as occupation and work experience, over social diversity data, such as gender or location of the participants. Furthermore, our results show that participant diversity is seldom analyzed or reflected upon when SE researchers discuss their study results, outcome or limitations.To help researchers self-assess their study diversity awareness, we propose a diversity awareness model and guidelines that SE researchers can apply to their research. With this study, we hope to shed light on a new approach to tackling the diversity and inclusion crisis in the SE field.
The Parametric Stability of Well-separated Spherical Gaussian Mixtures

We quantify the parameter stability of a spherical Gaussian Mixture Model (sGMM) under small perturbations in distribution space. Namely, we derive the first explicit bound to show that for a mixture of spherical Gaussian $P$ (sGMM) in a pre-defined model class, all other sGMM close to $P$ in this model class in total variation distance has a small parameter distance to $P$. Further, this upper bound only depends on $P$. The motivation for this work lies in providing guarantees for fitting Gaussian mixtures; with this aim in mind, all the constants involved are well defined and distribution free conditions for fitting mixtures of spherical Gaussians. Our results tighten considerably the existing computable bounds, and asymptotically match the known sharp thresholds for this problem.
Design and Implementation of A Soccer Ball Detection System with Multiple Cameras

The detection of small and medium-sized objects in three dimensions has always been a frontier exploration problem. This technology has a very wide application in sports analysis, games, virtual reality, human animation and other fields. The traditional three-dimensional small target detection technology has the disadvantages of high cost, low precision and inconvenience, so it is difficult to apply in practice. With the development of machine learning and deep learning, the technology of computer vision algorithms is becoming more mature. Creating an immersive media experience is considered to be a very important research work in sports. The main work is to explore and solve the problem of football detection under the multiple cameras, aiming at the research and implementation of the live broadcast system of football matches. Using multi cameras detects a target ball and determines its position in three dimension with the occlusion, motion, low illumination of the target object. This paper designed and implemented football detection system under multiple cameras for the detection and capture of targets in real-time matches. The main work mainly consists of three parts, football detector, single camera detection, and multi-cameras detection. The system used bundle adjustment to obtain the three-dimensional position of the target, and the GPU to accelerates data pre-processing and achieve accurate real-time capture of the target. By testing the system, it shows that the system can accurately detect and capture the moving targets in 3D. In addition, the solution in this paper is reusable for large-scale competitions, like basketball and soccer. The system framework can be well transplanted into other similar engineering project systems. It has been put into the market.
HunSum-1: an Abstractive Summarization Dataset for Hungarian

We introduce HunSum-1: a dataset for Hungarian abstractive summarization, consisting of 1.14M news articles. The dataset is built by collecting, cleaning and deduplicating data from 9 major Hungarian news sites through CommonCrawl. Using this dataset, we build abstractive summarizer models based on huBERT and mT5. We demonstrate the value of the created dataset by performing a quantitative and qualitative analysis on the models' results. The HunSum-1 dataset, all models used in our experiments and our code are available open source.
D OUBLY R OBUST E STIMATION OF C AUSAL E FFECTS IN N ETWORK -B ASED O BSERVATIONAL S TUDIES

Causal inference on populations embedded in social networks poses technical challenges, since the typical no interference assumption may no longer hold. For instance, in the context of social research, the outcome of a study unit will likely be affected by an intervention or treatment received by close neighbors. While inverse probability-of-treatment weighted (IPW) estimators have been developed for this setting, they are often highly inefﬁcient. In this work, we assume that the network is a union of disjoint components and propose doubly robust (DR) estimators combining models for treatment and outcome that are consistent and asymptotically normal if either model is correctly speciﬁed. We present empirical results that illustrate the DR property and the efﬁciency gain of DR over IPW estimators when both the outcome and treatment models are correctly speciﬁed. Simulations are conducted for networks with equal and unequal component sizes and outcome data with and without a multilevel structure. We apply these methods in an illustrative analysis using the Add Health network, examining the impact of maternal college education on adolescent school performance, both direct and indirect.
Accelerated First-Order Optimization under Nonlinear Constraints

We exploit analogies between first-order algorithms for constrained optimization and non-smooth dynamical systems to design a new class of accelerated first-order algorithms for constrained optimization. Unlike Frank-Wolfe or projected gradients, these algorithms avoid optimization over the entire feasible set at each iteration. We prove convergence to stationary points even in a nonconvex setting and we derive accelerated rates for the convex setting both in continuous time, as well as in discrete time. An important property of these algorithms is that constraints are expressed in terms of velocities instead of positions, which naturally leads to sparse, local and convex approximations of the feasible set (even if the feasible set is nonconvex). Thus, the complexity tends to grow mildly in the number of decision variables and in the number of constraints, which makes the algorithms suitable for machine learning applications. We apply our algorithms to a compressed sensing and a sparse regression problem, showing that we can treat nonconvex $\ell^p$ constraints ($p<1$) efficiently, while recovering state-of-the-art performance for $p=1$.
Prioritizing Speech Test Cases

With the wide adoption of automated speech recognition (ASR) systems, it is increasingly important to test and improve ASR systems. However, collecting and executing speech test cases is usually expensive and time-consuming, motivating us to strategically prioritize speech test cases. A key question is: how to determine the ideal order of collecting and executing speech test cases to uncover more errors as early as possible? Each speech test case consists of a piece of audio and the corresponding reference text. In this work, we propose PROPHET (PRiOritizing sPeecH tEsT), a tool that predicts potential error-uncovering speech test cases only based on their reference texts. Thus, PROPHET analyzes test cases and prioritizes them without running the ASR system, which can analyze speech test cases at a large scale. We evaluate 6 different prioritization methods on 3 ASR systems and 12 datasets. Given the same testing budget, we find that our approach uncovers 12.63% more wrongly recognized words than the state-of-the-art method. We select test cases from the prioritized list to fine-tune ASR systems and analyze how our approach can improve the ASR system performance. Statistical tests show that our proposed method can bring significantly larger performance improvement to ASR systems than the existing baseline methods. Furthermore, we perform correlation analysis and confirm that fine-tuning an ASR system using a dataset, on which the model performs worse, tends to improve the performance more.