Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Supramolecular Architecture on Halogen Bonding between Diiodine and Heteroaromatic N-Oxides

Cocrystallization of diiodine with a series of heteroaromatic N-oxides produced three types of halogen-bonded associates: (a) alternating chains comprising I2 molecules bridged by oxygen atoms of N-oxides of pyridine, quinoline, or 4-methylpyridine; (b) discrete 2:1 complexes, in which diiodine links a pair of acridine N-oxide molecules; and (c) amphoteric 1:1 adducts, in which one end of each diiodine molecule is halogen-bonded to N-oxide of 4-methoxypyridine or 4-chloroquinoline, and another diiodine’s end forms contacts with the other I2 molecules. In all cases, halogen bonds between diiodine and N-oxides are characterized by nearly linear I–I···O angles, close to perpendicular dihedral C–N–O···I angles and N–O···I angles of about 110°. The halogen bond length in the 1:1 adducts is about 0.3 A shorter than those in the 2:1 associates and in the infinite chains. Computational analyses confirmed that the variations in I···O separations are related predominantly to the distinct effects of halogen bond com.
VinaFood21: A Novel Dataset for Evaluating Vietnamese Food Recognition

Vietnam is such an attractive tourist destination with its stunning and pristine landscapes and its top-rated unique food and drink. Among thousands of Vietnamese dishes, foreigners and native people are interested in easy-to-eat tastes and easy-to-do recipes, along with reasonable prices, mouthwatering flavors, and popularity. Due to the diversity and almost all the dishes have significant similarities and the lack of quality Vietnamese food datasets, it is hard to implement an auto system to classify Vietnamese food, therefore, make people easier to discover Vietnamese food. This paper introduces a new Vietnamese food dataset named VinaFood21, which consists of 13,950 images corresponding to 21 dishes. We use 10,044 images for model training and 6,682 test images to classify each food in the VinaFood21 dataset and achieved an average accuracy of 74.81% when fine-tuning CNN EfficientNet-B0.
Two-Stage Sector Rotation Methodology Using Machine Learning and Deep Learning Techniques

Market indicators such as CPI and GDP have been widely used over decades to identify the stage of business cycles and also investment attractiveness of sectors given market conditions. In this paper, we propose a two-stage methodology that consists of predicting ETF prices for each sector using market indicators and ranking sectors based on their predicted rate of returns. We initially start with choosing sector specific macroeconomic indicators and implement Recursive Feature Elimination algorithm to select the most important features for each sector. Using our prediction tool, we implement different Recurrent Neural Networks models to predict the future ETF prices for each sector. We then rank the sectors based on their predicted rate of returns. We select the best performing model by evaluating the annualized return, annualized Sharpe ratio, and Calmar ratio of the portfolios that includes the top four ranked sectors chosen by the model. We also test the robustness of the model performance with respect to lookback windows and look ahead windows. Our empirical results show that our methodology beats the equally weighted portfolio performance even in the long run. We also find that Echo State Networks exhibits an outstanding performance compared to other models yet it is faster to implement compared to other RNN models.
Mitigating dataset harms requires stewardship: Lessons from 1000 papers

Machine learning datasets have elicited concerns about privacy, bias, and unethical applications, leading to the retraction of prominent datasets such as DukeMTMC, MS-Celeb-1M, and Tiny Images. In response, the machine learning community has called for higher ethical standards in dataset creation. To help inform these efforts, we studied three influential but ethically problematic face and person recognition datasets -- Labeled Faces in the Wild (LFW), MS-Celeb-1M, and DukeMTM -- by analyzing nearly 1000 papers that cite them. We found that the creation of derivative datasets and models, broader technological and social change, the lack of clarity of licenses, and dataset management practices can introduce a wide range of ethical concerns. We conclude by suggesting a distributed approach to harm mitigation that considers the entire life cycle of a dataset.
Particle fluctuations and the failure of simple effective models for many-body localized phases

We investigate and compare the particle number fluctuations in the putative many-body localized (MBL) phase of a spinless fermion model with potential disorder and nearest-neighbor interactions with those in the non-interacting case (Anderson localization) and in effective models where only interaction terms diagonal in the Anderson basis are kept. We demonstrate that these types of simple effective models cannot account for the particle number fluctuations observed in the MBL phase of the microscopic model. This implies that assisted and pair hopping terms-generated when transforming the microscopic Hamiltonian into the Anderson basis-cannot be neglected even at strong disorder and weak interactions. As a consequence, it appears questionable if the microscopic model possesses an exponential number of exactly conserved local charges. If such a set of conserved local charges does not exist, then particles are expected to ultimately delocalize for any finite disorder strength.
Out-of-Domain Generalization From a Single Source: An Uncertainty Quantification Approach

We are concerned with a worst-case scenario in model generalization, in the sense that a model aims to perform well on many unseen domains while there is only one single domain available for training. We propose Meta-Learning based Adversarial Domain Augmentation to solve this Out-of-Domain generalization problem. The key idea is to leverage adversarial training to create “fictitious” yet “challenging” populations, from which a model can learn to generalize with theoretical guarantees. To facilitate fast and desirable domain augmentation, we cast the model training in a meta-learning scheme and use a Wasserstein Auto-Encoder to relax the widely used worst-case constraint. We further improve our method by integrating uncertainty quantification for efficient domain generalization. Extensive experiments on multiple benchmark datasets indicate its superior performance in tackling single domain generalization.
Analyzing the Abstractiveness-Factuality Tradeoff With Nonlinear Abstractiveness Constraints

We analyze the tradeoff between factuality and abstractiveness of summaries. We introduce abstractiveness constraints to control the degree of abstractiveness at decoding time, and we apply this technique to characterize the abstractiveness-factuality tradeoff across multiple widely-studied datasets, using extensive human evaluations. We train a neural summarization model on each dataset and visualize the rates of change in factuality as we gradually increase abstractiveness using our abstractiveness constraints. We observe that, while factuality generally drops with increased abstractiveness, different datasets lead to different rates of factuality decay. We propose new measures to quantify the tradeoff between factuality and abstractiveness, incl. µ QAGS, which balances factuality with abstractiveness. We also quantify this tradeoff in previous works, aiming to establish baselines for the abstractiveness-factuality tradeoff that future publications can compare against.
Spatiotemporal Contrastive Learning of Facial Expressions in Videos

We propose a self-supervised contrastive learning approach for facial expression recognition (FER) in videos. We propose a novel temporal sampling-based augmentation scheme to be utilized in addition to standard spatial augmentations used for contrastive learning. Our proposed temporal augmentation scheme randomly picks from one of three temporal sampling techniques: (1) pure random sampling, (2) uniform sampling, and (3) sequential sampling. This is followed by a combination of up to three standard spatial augmentations. We then use a deep R(2+1)D network for FER, which we train in a self-supervised fashion based on the augmentations and subsequently fine-tune. Experiments are performed on the Oulu-CASIA dataset and the performance is compared to other works in FER. The results indicate that our method achieves an accuracy of 89.4%, setting a new state-of-the-art by outperforming other works. Additional experiments and analysis confirm the considerable contribution of the proposed temporal augmentation versus the existing spatial ones.
Angular Momentum Distributions for Observed and Modeled Exoplanetary Systems

The distribution of angular momentum of planets and their host stars provides important information on the formation and evolution of the planetary system. However, mysteries still remain, partly due to bias and uncertainty of the current observational data sets and partly due to the fact that theoretical models for the formation and evolution of planetary systems are still underdeveloped. In this study, we calculate the spin angular momentum of host stars and the orbital angular momentum of their planets using data from the NASA Exoplanet Archive along with detailed analysis of observation dependent biases and uncertainty ranges. We also analyze the angular momentum of the planetary system as a function of star age to understand their variation in different evolutionary stages. In addition, we use a population of planets from theoretical model simulations to reexamine the observed patterns and compare the simulated population with the observed samples to assess variations and differences. We found the majority of exoplanets discovered thus far do not have the angular momentum distribution similar to that of planets in our solar system, though this could be due to the observation bias. When filtered by the observational biases, the model simulated angular momentum distributions are comparable to the observed pattern in general. However, the differences between the observation and model simulation in the parameter (angular momentum) space provide more rigorous constraints and insights on the issues that needed future improvement.
Unsupervised Learning of Debiased Representations with Pseudo-Attributes

Dataset bias is a critical challenge in machine learning since it often leads to a negative impact on a model due to the unintended decision rules captured by spurious correlations. Although existing works often handle this issue based on human supervision, the availability of the proper annotations is impractical and even unrealistic. To better tackle the limitation, we propose a simple but effective unsupervised debiasing technique. Specifically, we first identify pseudo-attributes based on the results from clustering performed in the feature embedding space even without an explicit bias attribute supervision. Then, we employ a novel cluster-wise reweighting scheme to learn debiased representation; the proposed method prevents minority groups from being discounted for minimizing the overall loss, which is desirable for worst-case generalization. The extensive experiments demonstrate the outstanding performance of our approach on multiple standard benchmarks, even achieving the competitive accuracy to the supervised counterpart. The source code is available at our project page11https://github.com/skynbe/pseudo-attributes .
Interpolation can hurt robust generalization even when there is no noise

Numerous recent works show that overparameterization implicitly reduces variance for min-norm interpolators and max-margin classifiers. These findings suggest that ridge regularization has vanishing benefits in high dimensions. We challenge this narrative by showing that, even in the absence of noise, avoiding interpolation through ridge regularization can significantly improve generalization. We prove this phenomenon for the robust risk of both linear regression and classification and hence provide the first theoretical result on robust overfitting.
Few-shot Unsupervised Domain Adaptation with Image-to-Class Sparse Similarity Encoding

This paper investigates a valuable setting called few-shot unsupervised domain adaptation (FS-UDA), which has not been sufficiently studied in the literature. In this setting, the source domain data are labelled, but with few-shot per category, while the target domain data are unlabelled. To address the FS-UDA setting, we develop a general UDA model to solve the following two key issues: the few-shot labeled data per category and the domain adaptation between support and query sets. Our model is general in that once trained it will be able to be applied to various FS-UDA tasks from the same source and target domains. Inspired by the recent local descriptor based few-shot learning (FSL), our general UDA model is fully built upon local descriptors (LDs) for image classification and domain adaptation. By proposing a novel concept called similarity patterns (SPs), our model not only effectively considers the spatial relationship of LDs that was ignored in previous FSL methods, but also makes the learned image similarity better serve the required domain alignment. Specifically, we propose a novel IMage-to-class sparse Similarity Encoding (IMSE) method. It learns SPs to extract the local discriminative information for classification and meanwhile aligns the covariance matrix of the SPs for domain adaptation. Also, domain adversarial training and multi-scale local feature matching are performed upon LDs. Extensive experiments conducted on a multi-domain benchmark dataset DomainNet demonstrates the state-of-the-art performance of our IMSE for the novel setting of FS-UDA. In addition, for FSL, our IMSE can also show better performance than most of recent FSL methods on miniImageNet.
A ug 2 02 1 Interplay of magnetism and dimerization in pressurized Kitaev candidate β-Li 2 IrO

We present magnetization measurements on polycrystalline β-Li2IrO3 under hydrostatic pressures up to 3 GPa and construct the temperature-pressure phase diagram of this material. The magnetically ordered phase with TN ≃ 38 K breaks down upon a pressure-induced first-order phase transition at pc ≈ 1.4 GPa and gives way to a high-pressure phase, where a step-like feature in the magnetic susceptibility signals a structural dimerization with a loss of Ir magnetic moments. Nevertheless, magnetism manifests itself also above pc via the Curie-like susceptibility upturn with the effective moment of 0.7 μB . We suggest that a partially dimerized phase with a mixture of the magnetic and non-magnetic Ir sites develops above pc. This phase is thermodynamically stable between 1.7 and 2.7 GPa according to our ab initio calculations. It confines the magnetic Ir sites to weakly coupled tetramers with the singlet ground state and no long-range magnetic order. Our results rule out the formation of a pressure-induced spin-liquid phase in β-Li2IrO3 and reveal peculiarities of the magnetism collapse transition in a Kitaev material.
Soft-mode anisotropy in the negative thermal expansion material 
ReO3

We use a symmetry-motivated approach to analyse neutron pair distribution function data to investigate the mechanism of negative thermal expansion (NTE) in ReO$_3$. This analysis shows that the local structure of ReO$_3$ is dominated by an in-phase octahedral tilting mode and that the octahedral units are far less flexible to scissoring type deformations than the octahedra in the related compound ScF$_3$. These results support the idea that structural flexibility is an important factor in NTE materials, allowing the phonon modes that drive a volume contraction of the lattice to occupy a greater volume in reciprocal space. The lack of flexibility in ReO$_3$ restricts the NTE-driving phonons to a smaller region of reciprocal space, limiting the magnitude and temperature range of NTE. In addition, we investigate the thermal expansion properties of the material at high temperature and do not find the reported second NTE region. Finally, we show that the local fluctuations, even at elevated temperatures, respect the symmetry and order parameter direction of the observed $P4/mbm$ high pressure phase of ReO$_3$. The result indicates that the motions associated with rigid unit modes are highly anisotropic in these systems.
Electromagnetic force and torque derived from a Lagrangian in conjunction with the Maxwell-Lorentz equations

Electromagnetic force and torque are typically derived from a stress tensor in conjunction with Maxwell’s equations of classical electrodynamics. In some instances, the Principle of Least Action (built around a Lagrangian) can be used to arrive at the same mathematical expressions of force and torque as those derived from a stress tensor. This paper describes some of the underlying arguments for the existence of a Lagrangian in the case of certain simple physical systems. While some formulations of electromagnetic force and torque admit a Lagrangian, there are other formulations for which a Lagrangian may not exist.
Prediction of environmental cognition to undesired environmental behavior—the interaction effect of environmental context

There exists a complex relationship between cognition and behavior. Based on the characteristic that individuals have negative effects on the environment, this article defined the concept of undesired environmental behavior and explored the interaction effect of environmental context in the phenomenon of “disunity of cognition and behavior. ” According to the survey data of Chinese residents, three main factors of the individual's environmental context were identified through factor analysis, including social norm, policy and regulation, and facilities support. The results of hierarchical linear regression show that: (1) Both environmental cognition and environmental context can significantly predict undesired environmental behavior, and social norm as well as facilities support can inhibit undesired environmental behavior except policy and regulation; (2) Undesired environmental behavior is affected by the interactions between environmental cognition and environmental context. Particularly, social norm and facilities support have influences on the relationship between environmental cognition and environmental context, and the relationship between the two is most influenced by facilities support. Finally, this article put forward related policy implications. © 2017 American Institute of Chemical Engineers Environ Prog, 37: 1361–1370, 2018
Cosmological parameters from the likelihood analysis of the galaxy power spectrum and bispectrum in real space

We present a joint likelihood analysis of the halo power spectrum and bispectrum in real space. We take advantage of a large set of numerical simulations and of an even larger set of halo mock catalogs to provide a robust estimate of the covariance properties. We derive constraints on bias and cosmological parameters assuming a theoretical model from perturbation theory at one-loop for the power spectrum and tree-level for the bispectrum. By means of the Deviance Information Criterion, we select a reference bias model dependent on seven parameters that can describe the data up to k max,P = 0.3 h Mpc-1 for the power spectrum and k max,B = 0.09 h Mpc-1 for the bispectrum at redshift z = 1. This model is able to accurately recover three selected cosmological parameters even for the rather extreme total simulation volume of 1000h -3 Gpc3. With the same tools, we study how relations among bias parameters can improve the fit while reducing the parameter space. In addition, we compare common approximations to the covariance matrix against the full covariance estimated from the mocks, and quantify the (non-negligible) effect of ignoring the cross-covariance between the two statistics. Finally, we explore different selection criteria for the triangular configurations to include in the analysis, showing that excluding nearly equilateral triangles rather than simply imposing a fixed maximum k max,B on all triangle sides can lead to a better exploitation of the information contained in the bispectrum.
Heat emitting tear damage in porcine skin: a thermal pathway for mechanical algesia

Mechanical pain (or mechanical algesia) can both be a vital mechanism warning us for dangers or an undesired medical symptom important to mitigate. Thus, a comprehensive understanding of the different mechanisms of this type of pain is paramount. In this work, we study the tearing of porcine skin in front of an infrared camera, and show that mechanical injuries in biological tissues can generate enough heat to stimulate the neural network. In particular, we report local temperature elevations of up to 24 degrees Celsius around fast cutaneous ruptures, which shall exceed the threshold of the neural nociceptors usually involved in thermal pain. Slower fractures exhibit lower temperature elevations, and we characterise such dependency to the damaging rate. Overall, we bring experimental evidence of a novel thermal pathway for direct mechanical algesia. In addition, the implications of this pathway are discussed for mechanical hyperalgesia as well, in which a role of the cutaneous thermal sensors has long been suspected. We also show that thermal dissipation shall actually account for a significant portion of the total skin’s fracture energy, making temperature monitoring an efficient way to detect biological damages.
Lights, Camera, Action! A Framework to Improve NLP Accuracy over OCR documents

Document digitization is essential for the digital transformation of our societies, yet a crucial step in the process, Optical Character Recognition (OCR), is still not perfect. Even commercial OCR systems can produce questionable output depending on the fidelity of the scanned documents. In this paper, we demonstrate an effective framework for mitigating OCR errors for any downstream NLP task, using Named Entity Recognition (NER) as an example. We first address the data scarcity problem for model training by constructing a document synthesis pipeline, generating realistic but degraded data with NER labels. We measure the NER accuracy drop at various degradation levels and show that a text restoration model, trained on the degraded data, significantly closes the NER accuracy gaps caused by OCR errors, including on an out-of-domain dataset. For the benefit of the community, we have made the document synthesis pipeline available as an open-source project.
Almost Everywhere Nonuniqueness of Integral Curves for Divergence-Free Sobolev Vector Fields

We construct divergence-free Sobolev vector fields in C([0,1];W^{1,r}(T^d;R^d)) with r=2 which simultaneously admit any finite number of distinct positive solutions to the continuity equation. We then show that the vector fields we produce have at least as many integral curves starting from a.e. point of T^d as the number of distinct positive solutions to the continuity equation these vector fields admit. Our work uses convex integration techniques for the continuity equation introduced in [L. Szekelyhidi and S. Modena, Annals of PDE, 2018] and [E. Brue, M. Colombo, C. De Lellis, Archive for Rational Mechanics and Analysis, 2021 ] to study non-uniqueness for positive solutions. We then infer non-uniqueness for integral curves from Ambrosio superposition principle.