Miracle-dz/UniFAR-FTUs huggingface dataset:
Transfer learning with generative models for object detection on limited datasets

The availability of data is limited in some fields, especially for object detection tasks, where it is necessary to have correctly labeled bounding boxes around each object. A notable example of such data scarcity is found in the domain of marine biology, where it is useful to develop methods to automatically detect submarine species for environmental monitoring. To address this data limitation, the state-of-the-art machine learning strategies employ two main approaches. The first involves pretraining models on existing datasets before generalizing to the specific domain of interest. The second strategy is to create synthetic datasets specifically tailored to the target domain using methods like copy-paste techniques or ad-hoc simulators. The first strategy often faces a significant domain shift, while the second demands custom solutions crafted for the specific task. In response to these challenges, here we propose a transfer learning framework that is valid for a generic scenario. In this framework, generated images help to improve the performances of an object detector in a few-real data regime. This is achieved through a diffusion-based generative model that was pretrained on large generic datasets, and is not trained on the task-specific domain. We validate our approach on object detection tasks, specifically focusing on fishes in an underwater environment, and on the more common domain of cars in an urban setting. Our method achieves detection performance comparable to models trained on thousands of images, using only a few hundreds of input data. Our results pave the way for new generative AI-based protocols for machine learning applications in various domains, for instance ranging from geophysics to biology and medicine.
A Rational Analysis of the Speech-to-Song Illusion

The speech-to-song illusion is a robust psychological phenomenon whereby a spoken sentence sounds increasingly more musical as it is repeated. Despite decades of research, a complete formal account of this transformation is still lacking, and some of its nuanced characteristics, namely, that certain phrases appear to transform while others do not, is not well understood. Here we provide a formal account of this phenomenon, by recasting it as a statistical inference whereby a rational agent attempts to decide whether a sequence of utterances is more likely to have been produced in a song or speech. Using this approach and analyzing song and speech corpora, we further introduce a novel prose-to-lyrics illusion that is purely text-based. In this illusion, simply duplicating written sentences makes them appear more like song lyrics. We provide robust evidence for this new illusion in both human participants and large language models.
PBADet: A One-Stage Anchor-Free Approach for Part-Body Association

The detection of human parts (e.g., hands, face) and their correct association with individuals is an essential task, e.g., for ubiquitous human-machine interfaces and action recognition. Traditional methods often employ multi-stage processes, rely on cumbersome anchor-based systems, or do not scale well to larger part sets. This paper presents PBADet, a novel one-stage, anchor-free approach for part-body association detection. Building upon the anchor-free object representation across multi-scale feature maps, we introduce a singular part-to-body center offset that effectively encapsulates the relationship between parts and their parent bodies. Our design is inherently versatile and capable of managing multiple parts-to-body associations without compromising on detection accuracy or robustness. Comprehensive experiments on various datasets underscore the efficacy of our approach, which not only outperforms existing state-of-the-art techniques but also offers a more streamlined and efficient solution to the part-body association challenge.
One-loop analysis of $\beta$ decays in SMEFT

We perform a loop-level analysis of charged-current (CC) processes involving light leptons and quarks within the Standard Model Effective Field Theory (SMEFT). This work is motivated by the high precision reached in experiment and Standard Model calculations for CC decays of mesons, neutron, and nuclei, and by a lingering tension in the Cabibbo universality test. We identify the SMEFT operators that induce the largest loop-level contributions to CC processes. These include four-quark and four-fermion semileptonic operators involving two third-generation quarks. We discuss the available constraints on the relevant effective couplings and along the way we derive new loop-level bounds from $K \to \pi \nu \bar \nu$ on four-quark operators involving two top quarks. We find that low-energy CC processes are quite competitive with other probes, set constraints that do not depend on flavor-symmetry assumptions, and probe operators involving third-generation quarks up to effective scales of $\Lambda\simeq 8$ TeV. Finally, we briefly discuss single-field ultraviolet completions that could induce the relevant operators.
Accelerating molecular vibrational spectra simulations with a physically informed deep learning model

In recent years, machine learning (ML) surrogate models have emerged as an indispensable tool to accelerate simulations of physical and chemical processes. However, there is still a lack of ML models that can accurately predict molecular vibrational spectra. Here, we present a highly efficient high-dimensional neural network potentials (HD-NNP) architecture to accurately calculate infrared (IR) and Raman spectra based on dipole moments and polarizabilities obtained on-the-fly via ML-molecular dynamics (MD) simulations. The methodology is applied to pyrazine, a prototypical polyatomic chromophore. The HD-NNP predicted energies are well within the chemical accuracy (1 kcal/mol), and the errors for HD-NNP predicted forces are only one-half of those obtained from a popular high-performance ML model. Compared to the ab initio reference, the HD-NNP predicted frequencies of IR and Raman spectra differ only by less than 8.3 cm^(-1), and the intensities of IR spectra and the depolarizaiton ratios of Raman spectra are well reproduced. The HD-NNP architecture developed in this work highlights importance of constructing highly accurate NNPs for predicting molecular vibrational spectra.
Passive detection of a random signal common to multi-sensor reference and surveillance arrays

This paper addresses the passive detection of a common rank-one subspace signal received in two multi-sensor arrays. We consider the case of a one-antenna transmitter sending a common Gaussian signal, independent Gaussian noises with arbitrary spatial covariance, and known channel subspaces. The detector derived in this paper is a generalized likelihood ratio (GLR) test. For all but one of the unknown parameters, it is possible to find closed-form maximum likelihood (ML) estimator functions. We can further compress the likelihood to only an unknown vector whose ML estimate requires maximizing a product of ratios in quadratic forms, which is carried out using a trust-region algorithm. We propose two approximations of the GLR that do not require any numerical optimization: one based on a sample-based estimator of the unknown parameter whose ML estimate cannot be obtained in closed-form, and one derived under low-SNR conditions. Notably, all the detectors are scale-invariant, and the approximations are functions of beamformed data. However, they are not GLRTs for data that has been pre-processed with a beamformer, a point that is elaborated in the paper. These detectors outperform previously published correlation detectors on simulated data, in many cases quite significantly. Moreover, performance results quantify the performance gains over detectors that assume only the dimension of the subspace to be known.
Population projection by age, sex, and educational attainment in rural and urban regions of 35 provinces of India, 2011-2101: Technical report on projecting the regionally explicit socioeconomic heterogeneity in India

This working paper documents and explains our methodological approaches and technical details about how we conducted subnational population projections for India. This research is motivated by two research questions: (1) How does the accounting of socioeconomic heterogeneity, measured by educational attainment, improve population projections for India? , and (2) How will changing patterns in urbanization affect the population projections, depending on the spatial scale (national vs. subnational) considered in the projections? Projections at national and subnational level can provide essential information for planning and implementing government policies, including the allocation of budget and resources. In a country like India national projections ignoring spatial and socioeconomic heterogeneity would be too short-sighted considering its sheer population size of 1.2 billion in 2011. It was surprising to see that our population projections for India with baseline scenario were consistent with the UN medium variant and Wittgenstein Centre SSP2 until 2070. We found that while our fertility assumptions are lower, our mortality assumptions were also lower and compensated for the lower number of births (and no international migration) with higher number of survivors. The results show that the overall fertility for India is lower than estimated/assumed by UN and Wittgenstein Centre due to lower starting values in our projection as well as due to explicit consideration of education in the model. This results in a rapid TFR decline to about 1.85 children per woman in the next two decades and stabilization for the rest of the century. The projection resulted in slower rate of urbanization in India from 31% in 2011 to 40% in 2051, compared to the UN urbanization projection and we presented several explanations for that.
Towards Robust Car Following Dynamics Modeling via Blackbox Models: Methodology, Analysis, and Recommendations

The selection of the target variable is important while learning parameters of the classical car following models like GIPPS, IDM, etc. There is a vast body of literature on which target variable is optimal for classical car following models, but there is no study that empirically evaluates the selection of optimal target variables for black-box models, such as LSTM, etc. The black-box models, like LSTM and Gaussian Process (GP) are increasingly being used to model car following behavior without wise selection of target variables. The current work tests different target variables, like acceleration, velocity, and headway, for three black-box models, i.e., GP, LSTM, and Kernel Ridge Regression. These models have different objective functions and work in different vector spaces, e.g., GP works in function space, and LSTM works in parameter space. The experiments show that the optimal target variable recommendations for black-box models differ from classical car following models depending on the objective function and the vector space. It is worth mentioning that models and datasets used during evaluation are diverse in nature: the datasets contained both automated and human-driven vehicle trajectories; the black-box models belong to both parametric and non-parametric classes of models. This diversity is important during the analysis of variance, wherein we try to find the interaction between datasets, models, and target variables. It is shown that the models and target variables interact and recommended target variables don't depend on the dataset under consideration.
Cooperating Cracks in Two-Dimensional Crystals

The pattern development of multiple cracks in extremely anisotropic solids such as bilayer or multilayer two-dimensional (2D) crystals contains rich physics, which, however, remains largely unexplored. We studied crack interaction across neighboring 2D layers by transmission electron microscopy and molecular dynamics simulations. Parallel and anti-parallel ('En-Passant') cracks attract and repel each other in bilayer 2D crystals, respectively, in stark contrast to the behaviors of co-planar cracks. We show that the misfit between in-plane displacement fields around the crack tips results in non-uniform interlayer shear, which modifies the crack driving forces by creating an antisymmetric component of the stress intensity factor. The cross-layer interaction between cracks directly leads to material toughening, the strength of which increases with the shear stiffness and decreases with the crack spacings. Backed by the experimental findings and simulation results, a theory that marries the theory of linear elastic fracture mechanics and the shear-lag model is presented, which guides the unconventional approach to engineer fracture patterns and enhance material resistance to cracking.
TeMPO: Efficient Time-Multiplexed Dynamic Photonic Tensor Core for Edge AI with Compact Slow-Light Electro-Optic Modulator

Electronic-photonic computing systems offer immense potential in energy-efficient artificial intelligence (AI) acceleration tasks due to the superior computing speed and efficiency of optics, especially for real-time, low-energy deep neural network (DNN) inference tasks on resource-restricted edge platforms. However, current optical neural accelerators based on foundry-available devices and conventional system architecture still encounter a performance gap compared to highly customized electronic counterparts. To bridge the performance gap due to lack of domain specialization, we present a time-multiplexed dynamic photonic tensor accelerator, dubbed TeMPO, with cross-layer device/circuit/architecture customization. At the device level, we present foundry-compatible, customized photonic devices, including a slow-light electro-optic modulator with experimental demonstration, optical splitters, and phase shifters that significantly reduce the footprint and power in input encoding and dot-product calculation. At the circuit level, partial products are hierarchically accumulated via parallel photocurrent aggregation, lightweight capacitive temporal integration, and sequential digital summation, considerably relieving the analog-to-digital conversion bottleneck. We also employ a multi-tile, multi-core architecture to maximize hardware sharing for higher efficiency. Across diverse edge AI workloads, TeMPO delivers digital-comparable task accuracy with superior quantization/noise tolerance. We achieve a 368.6 TOPS peak performance, 22.3 TOPS/W energy efficiency, and 1.2 TOPS/mm$^2$ compute density, pushing the Pareto frontier in edge AI hardware. This work signifies the power of cross-layer co-design and domain-specific customization, paving the way for future electronic-photonic accelerators with even greater performance and efficiency.
Insights into (k, ρ)-shortcutting algorithms

A graph is called a $(k,\rho)$-graph iff every node can reach $\rho$ of its nearest neighbors in at most k hops. This property proved useful in the analysis and design of parallel shortest-path algorithms. Any graph can be transformed into a $(k,\rho)$-graph by adding shortcuts. Formally, the $(k,\rho)$-Minimum-Shortcut problem asks to find an appropriate shortcut set of minimal cardinality. We show that the $(k,\rho)$-Minimum-Shortcut problem is NP-complete in the practical regime of $k \ge 3$ and $\rho = \Theta(n^\epsilon)$ for $\epsilon>0$. With a related construction, we bound the approximation factor of known $(k,\rho)$-Minimum-Shortcut problem heuristics from below and propose algorithmic countermeasures improving the approximation quality. Further, we describe an integer linear problem (ILP) solving the $(k,\rho)$-Minimum-Shortcut problem optimally. Finally, we compare the practical performance and quality of all algorithms in an empirical campaign.
Designing NLP-based solutions for requirements variability management: experiences from a design science study at Visma

Context and motivation: In this industry-academia collaborative project, a team of researchers, supported by a software architect, business analyst, and test engineer explored the challenges of requirement variability in a large business software development company. Question/problem: Following the design science paradigm, we studied the problem of requirements analysis and tracing in the context of contractual documents, with a specific focus on managing requirements variability. This paper reports on the lessons learned from that experience, highlighting the strategies and insights gained in the realm of requirements variability management. Principal ideas/results: This experience report outlines the insights gained from applying design science in requirements engineering research in industry. We show and evaluate various strategies to tackle the issue of requirement variability. Contribution: We report on the iterations and how the solution development evolved in parallel with problem understanding. From this process, we derive five key lessons learned to highlight the effectiveness of design science in exploring solutions for requirement variability in contract-based environments.
AraSpider: Democratizing Arabic-to-SQL

This study presents AraSpider, the first Arabic version of the Spider dataset, aimed at improving natural language processing (NLP) in the Arabic-speaking community. Four multilingual translation models were tested for their effectiveness in translating English to Arabic. Additionally, two models were assessed for their ability to generate SQL queries from Arabic text. The results showed that using back translation significantly improved the performance of both ChatGPT 3.5 and SQLCoder models, which are considered top performers on the Spider dataset. Notably, ChatGPT 3.5 demonstrated high-quality translation, while SQLCoder excelled in text-to-SQL tasks. The study underscores the importance of incorporating contextual schema and employing back translation strategies to enhance model performance in Arabic NLP tasks. Moreover, the provision of detailed methodologies for reproducibility and translation of the dataset into other languages highlights the research's commitment to promoting transparency and collaborative knowledge sharing in the field. Overall, these contributions advance NLP research, empower Arabic-speaking researchers, and enrich the global discourse on language comprehension and database interrogation.
Squidgets: Sketch-based Widget Design and Direct Manipulation of 3D Scene

Squidgets or 'sketch-widgets' is a novel stroke-based UI framework for direct scene manipulation. Squidgets is motivated by the observation that sketch strokes comprising visual abstractions of scene elements implicitly provide natural handles for the direct manipulation of scene parameters. Configurations of such strokes can further be explicitly drawn by users to author custom widgets associated with scene attributes. Users manipulate a scene by simply drawing strokes: a squidget is selected by partially matching the drawn stroke against both implicit scene contours and explicitly authored curves, and used in-situ to interactively control scene parameters associated with the squidget. We present an implementation of squidgets within the 3D modeling animation system Maya, and report on an evaluation of squidget creation and manipulation, by both casual users and professional artists.
Zepyros: A webserver to evaluate the shape complementarity of protein-protein interfaces

Shape complementarity of molecular surfaces at the interfaces is a well-known characteristic of protein-protein binding regions, and it is critical in influencing the stability of the complex. Measuring such complementarity is at the basis of methods for both the prediction of possible interactions and for the design/optimization of speficic ones. However, only a limited number of tools are currently available to efficiently and rapidly assess it. Here, we introduce Zepyros, a webserver for fast measuring of the shape complementarity between two molecular interfaces of a given protein-protein complex using structural information. Zepyros is implemented as a publicly available tool with a user-friendly interface. Our server can be found at the following link (all major browser supported): https://zepyros.bio-groups.com
Solving Deep Reinforcement Learning Benchmarks with Linear Policy Networks

Although Deep Reinforcement Learning (DRL) methods can learn effective policies for challenging problems such as Atari games and robotics tasks, algorithms are complex and training times are often long. This study investigates how evolution strategies (ES) perform compared to gradient-based deep reinforcement learning methods. We use ES to optimize the weights of a neural network via neuroevolution, performing direct policy search. We benchmark both regular networks and policy networks consisting of a single linear layer from observations to actions; for three classical ES methods and for three gradient-based methods such as PPO. Our results reveal that ES can find effective linear policies for many RL benchmark tasks, in contrast to DRL methods that can only find successful policies using much larger networks, suggesting that current benchmarks are easier to solve than previously assumed. Interestingly, also for higher complexity tasks, ES achieves results comparable to gradient-based DRL algorithms. Furthermore, we find that by directly accessing the memory state of the game, ES are able to find successful policies in Atari, outperforming DQN. While gradient-based methods have dominated the field in recent years, ES offers an alternative that is easy to implement, parallelize, understand, and tune.
Surge-Type Glaciers in the Tien Shan (Central Asia)

ABSTRACT Surge-type glaciers have been observed in several mountain ranges of the world. Though Karakoram and Pamir are the hot spots for the occurrence of surge-type glaciers in High Mountain Asia, few surge-type glaciers also exist in Tien Shan. These have not been studied or reported in detail in the recent literature. We have identified 39 surge-type glaciers and five tributary surges in Tien Shan either from available literature or by visual interpretation using available images from the period 1960 until 2014. Out of the 39 glaciers, 9 are confirmed as surge-type, 13 are very probably surge-type, and the remaining are possibly of surge-type. Most of the surge-type glaciers are located in Ak-Shiirak and Central Tien Shan. Compared with the normal glaciers of Tien Shan, the surge-type glaciers are larger, cover higher ranges of elevations, and have shallower slopes. There is no significant difference in aspect. The largest surge events were observed in Central Tien Shan: North Inylchek Glacier (years 1996/1997) and Samoilowich Glacier (years 1992 until 2006) advanced several kilometers. The surge cycle was around 50 years for both of these glaciers. The advance was less pronounced for all other surge-type glaciers during the period ca. 1960–2014. Some of the tributary glaciers behaved differently than the main glaciers in the sense that they continuously advanced during the entire period of our study, whereas the main glaciers have remained stable or retreated.