Miracle-dz/UniFAR-FTUs huggingface dataset:
End-to-End Chess Recognition

Chess recognition is the task of extracting the chess piece configuration from a chessboard image. Current approaches use a pipeline of separate, independent, modules such as chessboard detection, square localization, and piece classification. Instead, we follow the deep learning philosophy and explore an end-to-end approach to directly predict the configuration from the image, thus avoiding the error accumulation of the sequential approaches and eliminating the need for intermediate annotations. Furthermore, we introduce a new dataset, Chess Recognition Dataset (ChessReD), that consists of 10,800 real photographs and their corresponding annotations. In contrast to existing datasets that are synthetically rendered and have only limited angles, ChessReD has photographs captured from various angles using smartphone cameras; a sensor choice made to ensure real-world applicability. Our approach in chess recognition on the introduced challenging benchmark dataset outperforms related approaches, successfully recognizing the chess pieces' configuration in 15.26% of ChessReD's test images. This accuracy may seem low, but it is ~7x better than the current state-of-the-art and reflects the difficulty of the problem. The code and data are available through: https://github.com/ThanosM97/end-to-end-chess-recognition.
How (not) to ensemble LVLMs for VQA

This paper studies ensembling in the era of Large Vision-Language Models (LVLMs). Ensembling is a classical method to combine different models to get increased performance. In the recent work on Encyclopedic-VQA the authors examine a wide variety of models to solve their task: from vanilla LVLMs, to models including the caption as extra context, to models augmented with Lens-based retrieval of Wikipedia pages. Intuitively these models are highly complementary, which should make them ideal for ensembling. Indeed, an oracle experiment shows potential gains from 48.8% accuracy (the best single model) all the way up to 67% (best possible ensemble). So it is a trivial exercise to create an ensemble with substantial real gains. Or is it?
Refining Decompiled C Code with Large Language Models

A C decompiler converts an executable into source code. The recovered C source code, once re-compiled, is expected to produce an executable with the same functionality as the original executable. With over twenty years of development, C decompilers have been widely used in production to support reverse engineering applications. Despite the prosperous development of C decompilers, it is widely acknowledged that decompiler outputs are mainly used for human consumption, and are not suitable for automatic recompilation. Often, a substantial amount of manual effort is required to fix the decompiler outputs before they can be recompiled and executed properly. This paper is motived by the recent success of large language models (LLMs) in comprehending dense corpus of natural language. To alleviate the tedious, costly and often error-prone manual effort in fixing decompiler outputs, we investigate the feasibility of using LLMs to augment decompiler outputs, thus delivering recompilable decompilation. Note that different from previous efforts that focus on augmenting decompiler outputs with higher readability (e.g., recovering type/variable names), we focus on augmenting decompiler outputs with recompilability, meaning to generate code that can be recompiled into an executable with the same functionality as the original executable. We conduct a pilot study to characterize the obstacles in recompiling the outputs of the de facto commercial C decompiler -- IDA-Pro. We then propose a two-step, hybrid approach to augmenting decompiler outputs with LLMs. We evaluate our approach on a set of popular C test cases, and show that our approach can deliver a high recompilation success rate to over 75% with moderate effort, whereas none of the IDA-Pro's original outputs can be recompiled. We conclude with a discussion on the limitations of our approach and promising future research directions.
Acoustic Lateral Recoil Force and Stable Lift of Anisotropic Particles

Acoustic forces and torques are of immense importance for manipulation of particles, in particular in biomedical applications. While such forces and torques are well understood for small spherical particles with lowest-order monopole and dipole responses, the higher-order effects for larger anisotropic particles have not been properly investigated. Here we examine the acoustic force and torque on an anisotropic (ellipsoid) particle and reveal two novel phenomena. First, we describe the lateral recoil force, orthogonal to the direction of the incident wave and determined by the tilted orientation of the particle. Second, we find conditions for the stable acoustic lift, where the balanced torque and force produce a stable lateral drift of the tilted particle. We argue that these phenomena can bring about new functionalities in acoustic manipulation and sorting of anisotropic particles including biological objects such as blood cells.
GIS Based Analysis of Landslide Effecting Factors in the Pyeongchang Area

Most areas in Gangwon-do are mountainous regions where causes heavy damages due to landslides. It is important to analyze basic factors influencing the cause of landslides in order to prevent such landslides. For this study, a landslide occurring site is extracted from aerial images taken after the landslide caused by typhoon "Ewiniar" in Pyeongchang area 2006. Also, the overlay analysis with the topographic, forest, and soil maps in this area is performed using GIS based methods. In addition, the topographic, forest, and soil characteristics relating to the landslide factors are analyzed. As a result, large numbers of landslides occurred at a slope angle of 20°- 40°. In the case of the forest factors, there are close relationships between the artificial pine and larch forests and the frequency of landslides. The low forest density represents a weakness in landslides. In the case of the soil factors, a higher level in the surface soil with a type of sandy loam soil, a higher gravel content in subsoil, and a higher degree of acid rocks in soil parent materials cause higher frequencies in landslides
Sine-Gordon model from coupled condensates: A generalized hydrodynamics viewpoint

The sine-Gordon model captures the low-energy effective dynamics of a wealth of one-dimensional quantum systems, stimulating the experimental efforts in building a versatile quantum simulator of this field theory and fueling the parallel development of new theoretical toolkits able to capture far-from-equilibrium settings. In this work, we analyze the realization of sine-Gordon from the interference pattern of two one-dimensional quasicondensates: we argue the emergent field theory is well described by its classical limit and develop its large-scale description based on Generalized Hydrodynamics. We show how, despite sine-Gordon being an integrable field theory, trap-induced inhomogeneities cause instabilities of excitations and provide exact analytical results to capture this effect.
UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model

Text is ubiquitous in our visual world, conveying crucial information, such as in documents, websites, and everyday photographs. In this work, we propose UReader, a first exploration of universal OCR-free visually-situated language understanding based on the Multimodal Large Language Model (MLLM). By leveraging the shallow text recognition ability of the MLLM, we only finetuned 1.2% parameters and the training cost is much lower than previous work following domain-specific pretraining and finetuning paradigms. Concretely, UReader is jointly finetuned on a wide range of Visually-situated Language Understanding tasks via a unified instruction format. To enhance the visual text and semantic understanding, we further apply two auxiliary tasks with the same format, namely text reading and key points generation tasks. We design a shape-adaptive cropping module before the encoder-decoder architecture of MLLM to leverage the frozen low-resolution vision encoder for processing high-resolution images. Without downstream finetuning, our single model achieves state-of-the-art ocr-free performance in 8 out of 10 visually-situated language understanding tasks, across 5 domains: documents, tables, charts, natural images, and webpage screenshots. Codes and instruction-tuning datasets will be released.
Sparse topic modeling via spectral decomposition and thresholding

The probabilistic Latent Semantic Indexing model assumes that the expectation of the corpus matrix is low-rank and can be written as the product of a topic-word matrix and a word-document matrix. In this paper, we study the estimation of the topic-word matrix under the additional assumption that the ordered entries of its columns rapidly decay to zero. This sparsity assumption is motivated by the empirical observation that the word frequencies in a text often adhere to Zipf's law. We introduce a new spectral procedure for estimating the topic-word matrix that thresholds words based on their corpus frequencies, and show that its $\ell_1$-error rate under our sparsity assumption depends on the vocabulary size $p$ only via a logarithmic term. Our error bound is valid for all parameter regimes and in particular for the setting where $p$ is extremely large; this high-dimensional setting is commonly encountered but has not been adequately addressed in prior literature. Furthermore, our procedure also accommodates datasets that violate the separability assumption, which is necessary for most prior approaches in topic modeling. Experiments with synthetic data confirm that our procedure is computationally fast and allows for consistent estimation of the topic-word matrix in a wide variety of parameter regimes. Our procedure also performs well relative to well-established methods when applied to a large corpus of research paper abstracts, as well as the analysis of single-cell and microbiome data where the same statistical model is relevant but the parameter regimes are vastly different.
Well/Ill-posedness of the Boltzmann Equation with Soft Potential

We consider the Boltzmann equation with the soft potential and angular cutoff. Inspired by the methods from dispersive PDEs, we establish its sharp local well-posedness and ill-posedness in $H^{s}$ Sobolev space. We find the well/ill-posedness separation at regularity $s=\frac{d-1}{2}$, strictly $\frac{1}{2}$-derivative higher than the scaling-invariant index $s=\frac{d-2}{2}$, the usually expected separation point.
Unlearning with Fisher Masking

Machine unlearning aims to revoke some training data after learning in response to requests from users, model developers, and administrators. Most previous methods are based on direct fine-tuning, which may neither remove data completely nor retain full performances on the remain data. In this work, we find that, by first masking some important parameters before fine-tuning, the performances of unlearning could be significantly improved. We propose a new masking strategy tailored to unlearning based on Fisher information. Experiments on various datasets and network structures show the effectiveness of the method: without any fine-tuning, the proposed Fisher masking could unlearn almost completely while maintaining most of the performance on the remain data. It also exhibits stronger stability compared to other unlearning baselines
In situ strength characterisation of peat and organic soil using full-flow penetrometers

Full-flow penetrometers have been shown to overcome problems experienced with the cone penetrometer when measuring resistance in very soft peat and organic soil, and give a much more uniform measure of resistance than the cone in fibrous peat. However, at present there is no guidance on the interpretation of strength parameters in these soils using the T-bar and ball. This paper examines the results of tests using these devices at two research sites in the Netherlands in con- junction with high-quality Sherbrooke sampling for laboratory testing. In fibrous peat, the T-bar and ball provided a more uniform measure of resistance with a lower degree of scatter than the cone. The in situ testing results have been compared with the laboratory tests to assess the range of resistance factors relating penetration resistance to the undrained shear strength (su) and have been shown to occupy a lower range of values than the cone penetrometer. However, penetration tests in these soils are likely to be influenced by partial drainage effects and this should be considered during testing and the sub- sequent interpretation of results. Recommendations are made for the use of full-flow penetrometers to obtain strength pa- rameters in these soils.
Controllable Chest X-Ray Report Generation from Longitudinal Representations

Radiology reports are detailed text descriptions of the content of medical scans. Each report describes the presence/absence and location of relevant clinical findings, commonly including comparison with prior exams of the same patient to describe how they evolved. Radiology reporting is a time-consuming process, and scan results are often subject to delays. One strategy to speed up reporting is to integrate automated reporting systems, however clinical deployment requires high accuracy and interpretability. Previous approaches to automated radiology reporting generally do not provide the prior study as input, precluding comparison which is required for clinical accuracy in some types of scans, and offer only unreliable methods of interpretability. Therefore, leveraging an existing visual input format of anatomical tokens, we introduce two novel aspects: (1) longitudinal representation learning -- we input the prior scan as an additional input, proposing a method to align, concatenate and fuse the current and prior visual information into a joint longitudinal representation which can be provided to the multimodal report generation model; (2) sentence-anatomy dropout -- a training strategy for controllability in which the report generator model is trained to predict only sentences from the original report which correspond to the subset of anatomical regions given as input. We show through in-depth experiments on the MIMIC-CXR dataset how the proposed approach achieves state-of-the-art results while enabling anatomy-wise controllable report generation.
Problem-tailored Simulation of Energy Transport on Noisy Quantum Computers

The transport of conserved quantities like spin and charge is fundamental to characterizing the behavior of quantum many-body systems. Numerically simulating such dynamics is generically challenging, which motivates the consideration of quantum computing strategies. However, the relatively high gate errors and limited coherence times of today's quantum computers pose their own challenge, highlighting the need to be frugal with quantum resources. In this work we report simulations on quantum hardware of infinite-temperature energy transport in the mixed-field Ising chain, a paradigmatic many-body system that can exhibit a range of transport behaviors at intermediate times. We consider a chain with $L=12$ sites and find results broadly consistent with those from ideal circuit simulators over 90 Trotter steps, containing up to 990 entangling gates. To obtain these results, we use two key problem-tailored insights. First, we identify a convenient basis$\unicode{x2013}$the Pauli $Y$ basis$\unicode{x2013}$in which to sample the infinite-temperature trace and provide theoretical and numerical justifications for its efficiency relative to, e.g., the computational basis. Second, in addition to a variety of problem-agnostic error mitigation strategies, we employ a renormalization strategy that compensates for global nonconservation of energy due to device noise. We expect that these techniques will prove useful beyond the specific application considered here.
Penalty shootouts are tough, but the alternating order is fair

We compare conversion rates of association football (soccer) penalties during regulation or extra time with those during shootouts. Our data consists of roughly 50,000 penalties from the eleven~most recent seasons in European men's football competitions. About one third of the penalties are from more than 1,500 penalty shootouts. We find that shootout conversion rates are significantly lower, and attribute this to worse performance of shooters rather than better performance of goalkeepers. We also find that, statistically, there is no advantage for either team in the usual alternating shooting order. These main findings are complemented by a number of more detailed analyses.
The Benefits and Costs of Highly Expansionary Monetary Policy

How far to go – and to remain – in the direction of highly expansionary monetary policy hinges on the balance of marginal benefits and costs of additional monetary easing and its expected evolution over time. This paper sketches a framework for assessing this balance and applies it to four OECD economic areas: the euro area, Japan, the United Kingdom and the United States. The effectiveness of further stimulus via quantitative easing or forward guidance in affecting asset prices, interest rates and credit flows will depend on the state of the economy and the functioning of financial markets. Marginal costs could rise due to excessive risk-taking; higher inflation expectations; higher likelihood of ever-greening; and higher risks of financial instability in the exit phase, especially when exit from monetary accommodation is close in time and signs of negative effects are already apparent. The balance of marginal benefits and costs is found to be different across the main OECD areas. In the United States, the case for additional stimulus is weakening, while the opposite is true for the euro area and Japan. In the United Kingdom, the assessment is less clear cut. Les avantages et couts d'une politique monetaire tres expansionniste Jusqu’ou aller – et demeurer – dans la direction d’une politique monetaire hautement expansionniste depend du solde entre les avantages et couts de l’assouplissement monetaire additionnel et de son evolution attendue dans le temps. Ce document propose une ebauche d’un cadre d’analyse pour evaluer ce solde et l’applique a quatre principales regions economiques de l’OCDE : les Etats-Unis, la zone euro, le Japon et le Royaume-Uni. L’efficacite d’un stimulus additionnel via l’assouplissement quantitatif ou des indications prospectives pour affecter les prix d’actifs, les taux d’interet et les flux de credit dependra de l’etat de l’economie et du fonctionnement des marches financiers. Les couts marginaux pourraient croitre en raison d’une prise de risques excessive ; d’anticipations d’inflation plus elevees ; d’une plus grande probabilite de regeneration des creances douteuses ; et de risques accrus d’instabilite financiere dans la phase de sortie, surtout lorsque la sortie de la politique monetaire accommodante est proche dans le temps et que les signes d’effets negatifs sont deja apparents. Le solde entre les avantages et couts est estime etre different au sein des principales regions de l’OCDE. Aux Etats-Unis, l’argument en faveur d’un stimulus additionnel s’est affaibli, tandis que l’oppose est vrai pour la zone euro et le Japon. Au Royaume-Uni, le diagnostic est moins clair.
Electromagnetically driven flow in unsupported electrolyte layers: lubrication theory and linear stability of annular flow

We consider a thin horizontal layer of a non-magnetic electrolyte containing a bulk solution of salt and carrying an electric current. The layer is bounded by two deformable free surfaces loaded with an insoluble surfactant and is placed in a vertical magnetic field. The arising Lorentz force drives the electrolyte in the plane of the layer. We employ the long-wave approximation to derive general two-dimensional hydrodynamic equations describing symmetric pinching-type deformations of the free surfaces. These equations are used to study the azimuthal flow in an annular film spanning the gap between two coaxial cylindrical electrodes. In weakly deformed films, the base azimuthal flow and its linear stability with respect to azimuthally invariant perturbations are studied analytically. For relatively thick layers and weak magnetic fields, the leading mode with the smallest decay rate is found to correspond to a monotonic azimuthal velocity perturbation. The Marangoni effect leads to further stabilisation of the flow while perturbations of the solute concentration in the bulk of the fluid have no influence on the flow stability. In strongly deformed films in the diffusion-dominated regime, the azimuthal flow becomes linearly unstable with respect to an oscillatory mixed mode characterised by the combination of radial and azimuthal velocity perturbations when the voltage applied between electrodes exceeds the critical value.
Complete next-to-leading order calculation of single inclusive 
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 production in forward proton-

We present the first fully consistent calculation of inclusive $\pi^0$ production at forward rapidities at next-to-leading order (NLO) accuracy in proton-lead collisions at $\sqrt{s}=8.16$ TeV within the Color Glass Condensate approach. The center-of-mass energy dependence is determined by the Balitsky-Kovchegov equation with the initial condition constrained at NLO accuracy by the proton structure function measurements. We find that the LHCb data further constrain this non-perturbative input, and that both the NLO corrections to the evolution equation and to the impact factor have significant effects on the nuclear modification factor $R_{p\mathrm{Pb}}$. The complete NLO calculation is shown to be in qualitative agreement with the LHCb data, and the predictions are found to be sensitive to the details of the applied deep inelastic scattering (DIS) fit. These results demonstrate the need to perform global analyses of DIS and LHC data in order to probe gluon saturation phenomena at precision level at very small momentum fraction $x$.
Protecting Sensitive Data through Federated Co-Training

In many applications, sensitive data is inherently distributed and may not be pooled due to privacy concerns. Federated learning allows us to collaboratively train a model without pooling the data by iteratively aggregating the parameters of local models. It is possible, though, to infer upon the sensitive data from the shared model parameters. We propose to use a federated co-training approach where clients share hard labels on a public unlabeled dataset instead of model parameters. A consensus on the shared labels forms a pseudo labeling for the unlabeled dataset that clients use in combination with their private data to train local models. We show that sharing hard labels substantially improves privacy over sharing model parameters. At the same time, federated co-training achieves a model quality comparable to federated learning. Moreover, it allows us to use local models such as (gradient boosted) decision trees, rule ensembles, and random forests that do not lend themselves to the parameter aggregation used in federated learning.