Miracle-dz/UniFAR-FTUs huggingface dataset:
A Bi-level Framework for Traffic Accident Duration Prediction: Leveraging Weather and Road Condition Data within a Practical Optimum Pipeline

Due to the stochastic nature of events, predicting the duration of a traffic incident presents a formidable challenge. Accurate duration estimation can result in substantial advantages for commuters in selecting optimal routes and for traffic management personnel in addressing non-recurring congestion issues. In this study, we gathered accident duration, road conditions, and meteorological data from a database of traffic accidents to check the feasibility of a traffic accident duration pipeline without accident contextual information data like accident severity and textual description. Multiple machine learning models were employed to predict whether an accident's impact on road traffic would be of a short-term or long-term nature, and then utilizing a bimodal approach the precise duration of the incident's effect was determined. Our binary classification random forest model distinguished between short-term and long-term effects with an 83% accuracy rate, while the LightGBM regression model outperformed other machine learning regression models with Mean Average Error (MAE) values of 26.15 and 13.3 and RMSE values of 32.91 and 28.91 for short and long-term accident duration prediction, respectively. Using the optimal classification and regression model identified in the preceding section, we then construct an end-to-end pipeline to incorporate the entire process. The results of both separate and combined approaches were comparable with previous works, which shows the applicability of only using static features for predicting traffic accident duration. The SHAP value analysis identified weather conditions, wind chill and wind speed as the most influential factors in determining the duration of an accident.
Scalable kernel balancing weights in a nationwide observational study of hospital profit status and heart attack outcomes.

Weighting is a general and often-used method for statistical adjustment. Weighting has two objectives: first, to balance covariate distributions, and second, to ensure that the weights have minimal dispersion and thus produce a more stable estimator. A recent, increasingly common approach directly optimizes the weights toward these two objectives. However, this approach has not yet been feasible in large-scale datasets when investigators wish to flexibly balance general basis functions in an extended feature space. To address this practical problem, we describe a scalable and flexible approach to weighting that integrates a basis expansion in a reproducing kernel Hilbert space with state-of-the-art convex optimization techniques. Specifically, we use the rank-restricted Nyström method to efficiently compute a kernel basis for balancing in nearly linear time and space, and then use the specialized first-order alternating direction method of multipliers to rapidly find the optimal weights. In an extensive simulation study, we provide new insights into the performance of weighting estimators in large datasets, showing that the proposed approach substantially outperforms others in terms of accuracy and speed. Finally, we use this weighting approach to conduct a national study of the relationship between hospital profit status and heart attack outcomes in a comprehensive dataset of 1.27 million patients. We find that for-profit hospitals use interventional cardiology to treat heart attacks at similar rates as other hospitals but have higher mortality and readmission rates.
Robust Safety Classifier for Large Language Models: Adversarial Prompt Shield

Large Language Models' safety remains a critical concern due to their vulnerability to adversarial attacks, which can prompt these systems to produce harmful responses. In the heart of these systems lies a safety classifier, a computational model trained to discern and mitigate potentially harmful, offensive, or unethical outputs. However, contemporary safety classifiers, despite their potential, often fail when exposed to inputs infused with adversarial noise. In response, our study introduces the Adversarial Prompt Shield (APS), a lightweight model that excels in detection accuracy and demonstrates resilience against adversarial prompts. Additionally, we propose novel strategies for autonomously generating adversarial training datasets, named Bot Adversarial Noisy Dialogue (BAND) datasets. These datasets are designed to fortify the safety classifier's robustness, and we investigate the consequences of incorporating adversarial examples into the training process. Through evaluations involving Large Language Models, we demonstrate that our classifier has the potential to decrease the attack success rate resulting from adversarial attacks by up to 60%. This advancement paves the way for the next generation of more reliable and resilient conversational agents.
Optical Properties and Variability of the Be X-Ray Binary CPD-29 2176

Be X-ray binaries (Be XRBs) are high-mass X-ray binaries, with a neutron star or black hole orbiting and accreting material from a nonsupergiant B-star that is rotating at a near critical rate. These objects are prime targets to understand past binary interactions as the neutron star or black hole progenitor likely experienced Roche lobe overflow to spin up the Be star we observe now. The stellar variability can then allow us to explore the stellar structure of these objects. It was recently demonstrated that the high-mass X-ray binary CPD −29 2176 descended from an ultrastripped supernova and is a prime target to evolve into an eventual binary neutron star and kilonova. We present the photometric variability from both TESS and ASAS along with the spectral properties and disk variability of the system in this paper. All of the optical lines are contaminated with disk emission except for the He ii λ4686 absorption line. The disk variability timescales are not the same as the orbital timescale, but could be related to the X-ray outbursts that have been recorded by Swift. We end our study with a discussion comparing CPD −29 2176 to classical Be stars and other Be X-ray binaries, finding the stellar rotation to be near a frequency of 1.5 cycles day−1, and exhibiting incoherent variability in three frequency groups.
FairWASP: Fast and Optimal Fair Wasserstein Pre-processing

Recent years have seen a surge of machine learning approaches aimed at reducing disparities in model outputs across different subgroups. In many settings, training data may be used in multiple downstream applications by different users, which means it may be most effective to intervene on the training data itself. In this work, we present FairWASP, a novel pre-processing approach designed to reduce disparities in classification datasets without modifying the original data. FairWASP returns sample-level weights such that the reweighted dataset minimizes the Wasserstein distance to the original dataset while satisfying (an empirical version of) demographic parity, a popular fairness criterion. We show theoretically that integer weights are optimal, which means our method can be equivalently understood as duplicating or eliminating samples. FairWASP can therefore be used to construct datasets which can be fed into any classification method, not just methods which accept sample weights. Our work is based on reformulating the pre-processing task as a large-scale mixed-integer program (MIP), for which we propose a highly efficient algorithm based on the cutting plane method. Experiments demonstrate that our proposed optimization algorithm significantly outperforms state-of-the-art commercial solvers in solving both the MIP and its linear program relaxation. Further experiments highlight the competitive performance of FairWASP in reducing disparities while preserving accuracy in downstream classification settings.
Transformers as Recognizers of Formal Languages: A Survey on Expressivity

As transformers have gained prominence in natural language processing, some researchers have investigated theoretically what problems they can and cannot solve, by treating problems as formal languages. Exploring questions such as this will help to compare transformers with other models, and transformer variants with one another, for various tasks. Work in this subarea has made considerable progress in recent years. Here, we undertake a comprehensive survey of this work, documenting the diverse assumptions that underlie different results and providing a unified framework for harmonizing seemingly contradictory findings.
Gap structure of the non-symmorphic superconductor LaNiGa2 probed by muSR

We report muon spin rotation (muSR) measurements of the temperature dependence of the absolute value of the magnetic penetration depth and the magnetic field dependence of the vortex core size in the mixed state of the non-symmorphic superconductor LaNiGa2. The temperature dependence of the normalized superfluid density is shown to be well described by a two-band model with strong interband coupling. Consistent with a strong coupling of the superconducting condensates in two different bands, we show that the field dependence of the vortex core size resembles that of a single-band superconductor. Our results lend support to the proposal that LaNiGa2 is a fully-gapped, internally antisymmetric nonunitary spin-triplet superconductor.
TPSeNCE: Towards Artifact-Free Realistic Rain Generation for Deraining and Object Detection in Rain

Rain generation algorithms have the potential to improve the generalization of deraining methods and scene understanding in rainy conditions. However, in practice, they produce artifacts and distortions and struggle to control the amount of rain generated due to a lack of proper constraints. In this paper, we propose an unpaired image-to-image translation framework for generating realistic rainy images. We first introduce a Triangular Probability Similarity (TPS) constraint to guide the generated images toward clear and rainy images in the discriminator manifold, thereby minimizing artifacts and distortions during rain generation. Unlike conventional contrastive learning approaches, which indiscriminately push negative samples away from the anchors, we propose a Semantic Noise Contrastive Estimation (SeNCE) strategy and reassess the pushing force of negative samples based on the semantic similarity between the clear and the rainy images and the feature similarity between the anchor and the negative samples. Experiments demonstrate realistic rain generation with minimal artifacts and distortions, which benefits image deraining and object detection in rain. Furthermore, the method can be used to generate realistic snowy and night images, underscoring its potential for broader applicability. Code is available at https://github.com/ShenZheng2000/TPSeNCE.
On the effect of curriculum learning with developmental data for grammar acquisition

This work explores the degree to which grammar acquisition is driven by language `simplicity' and the source modality (speech vs. text) of data. Using BabyBERTa as a probe, we find that grammar acquisition is largely driven by exposure to speech data, and in particular through exposure to two of the BabyLM training corpora: AO-Childes and Open Subtitles. We arrive at this finding by examining various ways of presenting input data to our model. First, we assess the impact of various sequence-level complexity based curricula. We then examine the impact of learning over `blocks' -- covering spans of text that are balanced for the number of tokens in each of the source corpora (rather than number of lines). Finally, we explore curricula that vary the degree to which the model is exposed to different corpora. In all cases, we find that over-exposure to AO-Childes and Open Subtitles significantly drives performance. We verify these findings through a comparable control dataset in which exposure to these corpora, and speech more generally, is limited by design. Our findings indicate that it is not the proportion of tokens occupied by high-utility data that aids acquisition, but rather the proportion of training steps assigned to such data. We hope this encourages future research into the use of more developmentally plausible linguistic data (which tends to be more scarce) to augment general purpose pre-training regimes.
Dark Matter in A Mirror Solution to the Strong CP Problem

We study thermal production of dark matter (DM) in a realization of the minimal models of Ref. ~\cite{Bonnefoy:2023afx}, where parity is used to solve the strong CP problem by transforming the entire Standard Model (SM) into a mirror copy. Although the mirror electron $e^{\prime}$ is a good DM candidate, its viability is mired by the presence of the mirror up-quark $u^{\prime}$, whose abundance is intimately related to the $e^{\prime}$ abundance and must be suppressed. This can be achieved through a sequential freeze-in mechanism, where mirror photons are first produced from SM gluons, and then the mirror photons produce $e'$. After computing the details of this double freeze-in, we discuss the allowed parameter space of the model, which lies at the threshold of experimental observations. We find that this origin of $e'$ DM requires a low reheating temperature after inflation and is consistent with the baryon asymmetry arising from leptogenesis, providing mirror neutrinos have a significant degeneracy. Finally, we show that this $e'$ DM is not compatible with Higgs Parity, the simplest scheme with exact parity, unless SM parameters deviate significantly from their central values or the minimal model is extended.
Tackling the Abstraction and Reasoning Corpus (ARC) with Object-centric Models and the MDL Principle

The Abstraction and Reasoning Corpus (ARC) is a challenging benchmark, introduced to foster AI research towards human-level intelligence. It is a collection of unique tasks about generating colored grids, specified by a few examples only. In contrast to the transformation-based programs of existing work, we introduce object-centric models that are in line with the natural programs produced by humans. Our models can not only perform predictions, but also provide joint descriptions for input/output pairs. The Minimum Description Length (MDL) principle is used to efficiently search the large model space. A diverse range of tasks are solved, and the learned models are similar to the natural programs. We demonstrate the generality of our approach by applying it to a different domain.
Little Giants: Exploring the Potential of Small LLMs as Evaluation Metrics in Summarization in the Eval4NLP 2023 Shared Task

This paper describes and analyzes our participation in the 2023 Eval4NLP shared task, which focuses on assessing the effectiveness of prompt-based techniques to empower Large Language Models to handle the task of quality estimation, particularly in the context of evaluating machine translations and summaries. We conducted systematic experiments with various prompting techniques, including standard prompting, prompts informed by annotator instructions, and innovative chain-of-thought prompting. In addition, we integrated these approaches with zero-shot and one-shot learning methods to maximize the efficacy of our evaluation procedures. Our work reveals that combining these approaches using a “small”, open source model (orca_mini_v3_7B) yields competitive results.
A higher order numerical method for singularly perturbed elliptic problems with characteristic boundary layers

A Petrov-Galerkin finite element method is constructed for a singularly perturbed elliptic problem in two space dimensions. The solution contains a regular boundary layer and two characteristic boundary layers. Exponential splines are used as test functions in one coordinate direction and are combined with bilinear trial functions defined on a Shishkin mesh. The resulting numerical method is shown to be a stable parameter-uniform numerical method that achieves a higher order of convergence compared to upwinding on the same mesh.
Symmetry-Resolved Entanglement Entropy for Local and Non-local QFTs

In this paper, we study symmetry-resolved entanglement entropy in free bosonic quantum many-body systems. Precisely, by making use of the lattice regularization scheme, we compute symmetry-resolved R\'enyi entropies for free complex scalar fields as well as for a simple class of non-local field theories in which entanglement entropy exhibits volume-law scaling. We present effective and approximate eigenvalues for the correlation matrix used to compute the symmetry-resolved entanglement entropy and show that they are consistent with the numerical results. Furthermore, we explore the equipartition of entanglement entropy and verify an effective equipartition in the massless limit. Finally, we make a comment on the entanglement entropy in the non-local quantum field theories and write down an explicit expression for the symmetry-resolved R\'enyi entropies.
Influence of models on seismic-event localization

Source localization is a fundamental problem in seismology. Current localization techniques often rely on homogeneous isotropic models, even if a survey region is known to be geologically complex or anisotropic. We investigated a model’s influence on localization, using data from a hydraulic injection experiment at the Continental Deep Drilling (Kontinentale Tiefbohrung, or KTB) site in Germany. We performed the localization with a grid-search algorithm and two additional methods for verification. From previous work, a homogeneous isotropic model based on a borehole check shot and a heterogeneous 3D isotropic model were available. Vertical seismic profiling (VSP) and borehole data as well as laboratory data from rock samples provided homogeneous anisotropic models. Although localization with the isotropic and anisotropic models led to the same magnitude of residuals and therefore to a comparable quality of fit, the distribution of the event cloud differed significantly for isotropic and anisotropic media.
Inference of CO2 flow patterns - a feasibility study

As the global deployment of carbon capture and sequestration (CCS) technology intensifies in the fight against climate change, it becomes increasingly imperative to establish robust monitoring and detection mechanisms for potential underground CO2 leakage, particularly through pre-existing or induced faults in the storage reservoir's seals. While techniques such as history matching and time-lapse seismic monitoring of CO2 storage have been used successfully in tracking the evolution of CO2 plumes in the subsurface, these methods lack principled approaches to characterize uncertainties related to the CO2 plumes' behavior. Inclusion of systematic assessment of uncertainties is essential for risk mitigation for the following reasons: (i) CO2 plume-induced changes are small and seismic data is noisy; (ii) changes between regular and irregular (e.g., caused by leakage) flow patterns are small; and (iii) the reservoir properties that control the flow are strongly heterogeneous and typically only available as distributions. To arrive at a formulation capable of inferring flow patterns for regular and irregular flow from well and seismic data, the performance of conditional normalizing flow will be analyzed on a series of carefully designed numerical experiments. While the inferences presented are preliminary in the context of an early CO2 leakage detection system, the results do indicate that inferences with conditional normalizing flows can produce high-fidelity estimates for CO2 plumes with or without leakage. We are also confident that the inferred uncertainty is reasonable because it correlates well with the observed errors. This uncertainty stems from noise in the seismic data and from the lack of precise knowledge of the reservoir's fluid flow properties.
The Minimax Wiener Sequential Testing Problem

Consider the sample path of a one-dimensional diffusion for which the diffusion coefficient is given and where the drift may take on one of two values: $\mu_0$ or $\mu_1$. Suppose that the signal-to-noise ratio (defined as the difference between the two possible drifts divided by the diffusion coefficient) is non-constant. Given an initial state for the observed process, we consider a minimax formulation of the Wiener sequential testing problem for detecting the correct drift coefficient as soon as possible and with minimal probabilities of incorrect terminal decisions. We solve the problem in the Bayesian formulation, under any prior probabilities of the process having drift $\mu_0$ or $\mu_1$, when the passage of time is penalized linearly. In the case where the signal-to-noise ratio is assumed constant, we obtain an explicit formula for the least favorable distribution.
MNN: Mixed Nearest-Neighbors for Self-Supervised Learning

In contrastive self-supervised learning, positive samples are typically drawn from the same image but in different augmented views, resulting in a relatively limited source of positive samples. An effective way to alleviate this problem is to incorporate the relationship between samples, which involves including the top-K nearest neighbors of positive samples. However, the problem of false neighbors (i.e., neighbors that do not belong to the same category as the positive sample) is an objective but often overlooked challenge due to the query of neighbor samples without supervision information. In this paper, we present a simple self-supervised learning framework called Mixed Nearest-Neighbors for Self-Supervised Learning (MNN). MNN optimizes the influence of neighbor samples on the semantics of positive samples through an intuitive weighting approach and image mixture operations. The results demonstrate that MNN exhibits exceptional generalization performance and training efficiency on four benchmark datasets.
Measurement of the Ratio of Partial Branching Fractions of Inclusive $\overline{B} \to X_u \ell \overline{\nu}$ to $\overline{B} \to X_{c} \ell \overline{\nu}$ and the Ratio of their Spectra with Hadronic Tagging

We present a measurement of the ratio of partial branching fractions of the semi-leptonic inclusive decays, $\overline{B} \to X_{u} \ell \overline{\nu}$ to $\overline{B} \to X_{c} \ell \overline{\nu}$, where $\ell = (e, \mu)$, using the full Belle sample of $772 \times 10^{6}$ $B \kern 0.18em\overline{\kern -0.18em B}$ pairs collected at the $\Upsilon(4S)$ resonance. The ratio is measured via a two-dimensional fit to the squared four-momentum transfer to the lepton pair, and the charged lepton energy in the $B$ meson rest frame, where the latter must be larger than $1$ Ge\kern -0.1em V, covering approximately $86\%$ and $78\%$ of the $\overline{B} \to X_{u} \ell \overline{\nu}$ and $\overline{B} \to X_{c} \ell \overline{\nu}$ phase space, respectively. We find $\Delta \mathcal{B}(\overline{B} \to X_{u} \ell \overline{\nu})/ \Delta \mathcal{B}(\overline{B} \to X_{c} \ell \overline{\nu}) = 0.0196(1 \pm 8.4\%_{\rm stat} \pm 7.9\%_{\rm syst})$ where the uncertainties are statistical and systematic, respectively. In addition, we report the partial branching fractions separately for charged and neutral $B$ meson decays, and for electron and muon decay channels. We place a limit on isospin breaking in $\overline{B} \to X_{u} \ell \overline{\nu}$ decays, and find no indication of lepton flavor universality violation in either the charmed or charmless mode. Furthermore, we unfold the $\overline{B} \to X_{u} \ell \overline{\nu}$ and $\overline{B} \to X_{c} \ell \overline{\nu}$ yields and report the differential ratio in lepton energy and four-momentum transfer squared.