Miracle-dz/UniFAR-FTUs huggingface dataset:
Code Sparsification and its Applications

We introduce a notion of code sparsification that generalizes the notion of cut sparsification in graphs. For a (linear) code $\mathcal{C} \subseteq \mathbb{F}_q^n$ of dimension $k$ a $(1 \pm \epsilon)$-sparsification of size $s$ is given by a weighted set $S \subseteq [n]$ with $|S| \leq s$ such that for every codeword $c \in \mathcal{C}$ the projection $c|_S$ of $c$ to the set $S$ has (weighted) hamming weight which is a $(1 \pm \epsilon)$ approximation of the hamming weight of $c$. We show that for every code there exists a $(1 \pm \epsilon)$-sparsification of size $s = \widetilde{O}(k \log (q) / \epsilon^2)$. This immediately implies known results on graph and hypergraph cut sparsification up to polylogarithmic factors (with a simple unified proof). One application of our result is near-linear size sparsifiers for constraint satisfaction problems (CSPs) over $\mathbb{F}_p$-valued variables whose unsatisfying assignments can be expressed as the zeros of a linear equation modulo a prime $p$. Building on this, we obtain a complete characterization of ternary Boolean CSPs that admit near-linear size sparsification. Finally, by connections between the eigenvalues of the Laplacians of Cayley graphs over $\mathbb{F}_2^k$ to the weights of codewords, we also give the first proof of the existence of spectral Cayley graph sparsifiers over $\mathbb{F}_2^k$ by Cayley graphs, i.e., where we sparsify the set of generators to nearly-optimal size.
A model independent description of $B\rightarrow D \pi \ell \nu$ decays

We introduce a new parameterization of $B\rightarrow D \pi \ell \nu$ form factors using a partial-wave expansion and derive bounds on the series coefficients using analyticity and unitarity. This is the first generalization of the model-independent formalism developed by Boyd, Grinstein, and Lebed for $B \to D \ell \nu$ to semileptonic decays with multi-hadron final states, and enables data-driven form factor determinations with robust, systematically-improvable uncertainties. Using this formalism, we extract the form-factor parameters for $B \to D_2^\ast(\to D\pi) \ell \nu$ decays in a model-independent way from fits of data from the Belle Experiment, and, for the first time, study the two-pole structure in the $D\pi$ S-wave in semileptonic decays employing lineshapes from unitarized chiral perturbation theory.
Trajectory Similarity Measurement: An Efficiency Perspective

Trajectories that capture object movement have numerous applications, in which similarity computation between trajectories often plays a key role. Traditionally, the similarity between two trajectories is quantified by means of heuristic measures, e.g., Hausdorff or ERP, that operate directly on the trajectories. In contrast, recent studies exploit deep learning to map trajectories to d-dimensional vectors, called embeddings. Then, some distance measure, e.g., Manhattan or Euclidean, is applied to the embeddings to quantify trajectory similarity. The resulting similarities are inaccurate: they only approximate the similarities obtained using the heuristic measures. As distance computation on embeddings is efficient, focus has been on achieving embeddings yielding high accuracy. Adopting an efficiency perspective, we analyze the time complexities of both the heuristic and the learning-based approaches, finding that the time complexities of the former approaches are not necessarily higher. Through extensive experiments on open datasets, we find that, on both CPUs and GPUs, only a few learning-based approaches can deliver the promised higher efficiency, when the embeddings can be pre-computed, while heuristic approaches are more efficient for one-off computations. Among the learning-based approaches, the self-attention-based ones are the fastest to learn embeddings that also yield the highest accuracy for similarity queries. These results have implications for the use of trajectory similarity approaches given different application requirements.
SmoothHess: ReLU Network Feature Interactions via Stein's Lemma

Several recent methods for interpretability model feature interactions by looking at the Hessian of a neural network. This poses a challenge for ReLU networks, which are piecewise-linear and thus have a zero Hessian almost everywhere. We propose SmoothHess, a method of estimating second-order interactions through Stein's Lemma. In particular, we estimate the Hessian of the network convolved with a Gaussian through an efficient sampling algorithm, requiring only network gradient calls. SmoothHess is applied post-hoc, requires no modifications to the ReLU network architecture, and the extent of smoothing can be controlled explicitly. We provide a non-asymptotic bound on the sample complexity of our estimation procedure. We validate the superior ability of SmoothHess to capture interactions on benchmark datasets and a real-world medical spirometry dataset.
On The Effects of Climate Change and Global Warming on Water Resources in Iran

In this paper, the effects of climate change on nationwide Iranian climate and water resources were investigated by a trend analysis of temperature, precipitation and river flow. The Mann7Kendal trend test was applied to the time series of monthly temperature and precipitation derived from satellite images. The mean monthly stream7flow data associated with representative stream gages of 6 major Iranian catchments was investigated for detecting any possible trend. Furthermore, the standardized precipitation index (SPI) was computed with the observed precipitation relative to 44 selected stations distributed throughout Iran. The results suggested a long7term increasing trend in temperature all over the country, while a statistically significant decreasing trend in precipitation was observed for north western and south eastern Iran. The results also suggested a decreasing trend in the available water resources across the country which is in agreement with the negative trend of satellite derived precipitation detected for some parts of the country. A decreasing trend was also observed in the SPI index indicating that the occurrence of severe/extreme long lasting droughts in some parts of the country might be attributed to the effects of climate change and global warming.
Schubert matroids, Delannoy paths, and Speyer's invariant

We provide a combinatorial way of computing Speyer's $g$-polynomial on arbitrary Schubert matroids via the enumeration of certain Delannoy paths. We define a new statistic of a basis in a matroid, and express the $g$-polynomial of a Schubert matroid in terms of it and internal and external activities. Some surprising positivity properties of the $g$-polynomial of Schubert matroids are deduced from our expression. Finally, we combine our formulas with a fundamental result of Derksen and Fink to provide an algorithm for computing the $g$-polynomial of an arbitrary matroid.
Joint Learning of Local and Global Features for Aspect-based Sentiment Classification

Aspect-based sentiment classification (ASC) aims to judge the sentiment polarity conveyed by the given aspect term in a sentence. The sentiment polarity is not only determined by the local context but also related to the words far away from the given aspect term. Most recent efforts related to the attention-based models can not sufficiently distinguish which words they should pay more attention to in some cases. Meanwhile, graph-based models are coming into ASC to encode syntactic dependency tree information. But these models do not fully leverage syntactic dependency trees as they neglect to incorporate dependency relation tag information into representation learning effectively. In this paper, we address these problems by effectively modeling the local and global features. Firstly, we design a local encoder containing: a Gaussian mask layer and a covariance self-attention layer. The Gaussian mask layer tends to adjust the receptive field around aspect terms adaptively to deemphasize the effects of unrelated words and pay more attention to local information. The covariance self-attention layer can distinguish the attention weights of different words more obviously. Furthermore, we propose a dual-level graph attention network as a global encoder by fully employing dependency tag information to capture long-distance information effectively. Our model achieves state-of-the-art performance on both SemEval 2014 and Twitter datasets.
Beyond Still Images: Robust Multi-Stream Spatiotemporal Networks

A defining characteristic of natural vision is its ability to withstand a variety of input alterations, resulting in the creation of an invariant representation of the surroundings. While convolutional neural networks exhibit resilience to certain forms of spatial input variation, modifications in the spatial and temporal aspects can significantly affect the representations of video content in deep neural networks. Inspired by the resilience of natural vision to input variations, we employ a simple multi-stream model to explore its potential to address spatiotemporal changes by including temporal features. Our primary goal is to introduce a video-trained model and evaluate its robustness to diverse image and video inputs, with a particular focus on exploring the role of temporal features in invariant recognition. Results show that including videos and the temporal stream during training mitigates the decline in accuracy and mAP in image and video understanding tasks by 1.36% and 3.14%, respectively.
A Novel Adaptive Inertia Strategy in Large-Scale Electric Power Grids

The increasing penetration of new renewable sources of energy in today's power grids is accompanied by a decrease in available electromechanical inertia. This leads to a reduced dynamical stability. To counterbalance this effect, virtual synchronous generators have been proposed to emulate conventional generators and provide inertia to power systems. The high flexibility of these devices makes it possible to control the synthetic inertia they provide and to have them operate even more efficiently than the electromechanical inertia they replace. Here, we propose a novel control scheme for virtual synchronous generators, where the amount of inertia provided is large at short times - thereby absorbing local faults and disturbances as efficiently as conventional generators - but decreases over a tunable time interval to prevent long-time coherent oscillations from setting in. This new model is used to investigate the effect of adaptive inertia on large-scale power grids. Our model outperforms conventional constant inertia in all scenarios and for all performance measures considered. We show how an optimized geographical distribution of adaptive inertia devices not only effectively absorbs local faults, but also significantly improves the damping of inter-area oscillations.
Multilingual Distilwhisper: Efficient Distillation of Multi-Task Speech Models Via Language-Specific Experts

Whisper is a multitask and multilingual speech model covering 99 languages. It yields commendable automatic speech recognition (ASR) results in a subset of its covered languages, but the model still underperforms on a non-negligible number of under-represented languages, a problem exacerbated in smaller model versions. In this work, we propose DistilWhisper, an approach able to bridge the performance gap in ASR for these languages while retaining the advantages of multitask and multilingual capabilities. Our approach involves two key strategies: lightweight modular ASR fine-tuning of whisper-small using language-specific experts, and knowledge distillation from whisper-large-v2. This dual approach allows us to effectively boost ASR performance while keeping the robustness inherited from the multitask and multilingual pre-training. Results demonstrate that our approach is more effective than standard fine-tuning or LoRA adapters, boosting performance in the targeted languages for both in- and out-of-domain test sets, while introducing only a negligible parameter overhead at inference.
ChineseWebText: Large-scale High-quality Chinese Web Text Extracted with Effective Evaluation Model

During the development of large language models (LLMs), the scale and quality of the pre-training data play a crucial role in shaping LLMs' capabilities. To accelerate the research of LLMs, several large-scale datasets, such as C4 [1], Pile [2], RefinedWeb [3] and WanJuan [4], have been released to the public. However, most of the released corpus focus mainly on English, and there is still lack of complete tool-chain for extracting clean texts from web data. Furthermore, fine-grained information of the corpus, e.g. the quality of each text, is missing. To address these challenges, we propose in this paper a new complete tool-chain EvalWeb to extract Chinese clean texts from noisy web data. First, similar to previous work, manually crafted rules are employed to discard explicit noisy texts from the raw crawled web contents. Second, a well-designed evaluation model is leveraged to assess the remaining relatively clean data, and each text is assigned a specific quality score. Finally, we can easily utilize an appropriate threshold to select the high-quality pre-training data for Chinese. Using our proposed approach, we release the largest and latest large-scale high-quality Chinese web text ChineseWebText, which consists of 1.42 TB and each text is associated with a quality score, facilitating the LLM researchers to choose the data according to the desired quality thresholds. We also release a much cleaner subset of 600 GB Chinese data with the quality exceeding 90%.
Sorting with Predictions

We explore the fundamental problem of sorting through the lens of learning-augmented algorithms, where algorithms can leverage possibly erroneous predictions to improve their efficiency. We consider two different settings: In the first setting, each item is provided a prediction of its position in the sorted list. In the second setting, we assume there is a"quick-and-dirty"way of comparing items, in addition to slow-and-exact comparisons. For both settings, we design new and simple algorithms using only $O(\sum_i \log \eta_i)$ exact comparisons, where $\eta_i$ is a suitably defined prediction error for the $i$th element. In particular, as the quality of predictions deteriorates, the number of comparisons degrades smoothly from $O(n)$ to $O(n\log n)$. We prove that the comparison complexity is theoretically optimal with respect to the examined error measures. An experimental evaluation against existing adaptive and non-adaptive sorting algorithms demonstrates the potential of applying learning-augmented algorithms in sorting tasks.
The Navier-Stokes equations on manifolds with boundary

We consider the motion of an incompressible viscous fluid on a compact Riemannian manifold $\sM$ with boundary. The motion on $\sM$ is modeled by the incompressible Navier-Stokes equations, and the fluid is subject to pure or partial slip boundary conditions of Navier type on $\partial\sM$. We establish existence and uniqueness of strong as well as weak (variational) solutions for initial data in critical spaces. Moreover, we show that the set of equilibria consists of Killing vector fields on $\sM$ that satisfy corresponding boundary conditions, and we prove that all equilibria are (locally) stable. In case $\sM$ is two-dimensional we show that solutions with divergence free initial condition in $L_2(\sM; T\sM)$ exist globally and converge to an equilibrium exponentially fast.
Permeability Evolution and Rock Brittle Failure

This paper reports an experimental study of the evolution of permeability during rock brittle failure and a theoretical analysis of rock critical stress level. It is assumed that the rock is a strain-softening medium whose strength can be described by Weibull’s distribution. Based on the two-dimensional renormalization group theory, it is found that the stress level λc (the ratio of the stress at the critical point to the peak stress) depends mainly on the homogeneity index or shape parameter m in the Weibull’s distribution for the rock. Experimental results show that the evolution of permeability is closely related to rock deformation stages: the permeability has a rapid increase with the growth of cracks and their surface areas (i.e., onset of fracture coalescence point), and reaches the maximum at rock failure. Both the experimental and analytical results show that this point of rapid increase in permeability on the permeability-pressure curve corresponds to the critical point on the stress-strain curve; for rock compression, the stress at this point is approximately 80% of the peak strength. Thus, monitoring the evolution of permeability may provide a new means of identifying the critical point of rock brittle fracture.
HIDM: Emulating Large Scale HI Maps using Score-based Diffusion Models

Efficiently analyzing maps from upcoming large-scale surveys requires gaining direct access to a high-dimensional likelihood and generating large-scale fields with high fidelity, which both represent major challenges. Using CAMELS simulations, we employ the state-of-the-art score-based diffusion models to simultaneously achieve both tasks. We show that our model, HIDM, is able to efficiently generate high fidelity large scale HI maps that are in a good agreement with the CAMELS's power spectrum, probability distribution, and likelihood up to second moments. HIDM represents a step forward towards maximizing the scientific return of future large scale surveys.
Effective Human-AI Teams via Learned Natural Language Rules and Onboarding

People are relying on AI agents to assist them with various tasks. The human must know when to rely on the agent, collaborate with the agent, or ignore its suggestions. In this work, we propose to learn rules grounded in data regions and described in natural language that illustrate how the human should collaborate with the AI. Our novel region discovery algorithm finds local regions in the data as neighborhoods in an embedding space that corrects the human prior. Each region is then described using an iterative and contrastive procedure where a large language model describes the region. We then teach these rules to the human via an onboarding stage. Through user studies on object detection and question-answering tasks, we show that our method can lead to more accurate human-AI teams. We also evaluate our region discovery and description algorithms separately.
The existence of inversive distance circle packing on polyhedral surface

We prove that for any discrete curvature satisfying Gauss-Bonnet formula, there exist a unique up to scaling inversive distance circle packing in the discrete conformal equivalent class, whose polyhedral metric meets the target curvature. We prove it by constructing diffeomorphism between fiber bundles with cell decomposition based on Teichm\"uller spaces, and each discrete conformal equivalent class is a fiber passing through finite cell with respect to triangulations, which means we can do surgery on the discrete Ricci flow by edge flipping using a generalized Ptolemy equation to ensure it converge and never blow up.
Exploring Unified Perspective For Fast Shapley Value Estimation

Shapley values have emerged as a widely accepted and trustworthy tool, grounded in theoretical axioms, for addressing challenges posed by black-box models like deep neural networks. However, computing Shapley values encounters exponential complexity in the number of features. Various approaches, including ApproSemivalue, KernelSHAP, and FastSHAP, have been explored to expedite the computation. We analyze the consistency of existing works and conclude that stochastic estimators can be unified as the linear transformation of importance sampling of feature subsets. Based on this, we investigate the possibility of designing simple amortized estimators and propose a straightforward and efficient one, SimSHAP, by eliminating redundant techniques. Extensive experiments conducted on tabular and image datasets validate the effectiveness of our SimSHAP, which significantly accelerates the computation of accurate Shapley values.
Bayesian inference for generalized linear models via quasi-posteriors

Generalized linear models (GLMs) are routinely used for modeling relationships between a response variable and a set of covariates. The simple form of a GLM comes with easy interpretability, but also leads to concerns about model misspecification impacting inferential conclusions. A popular semi-parametric solution adopted in the frequentist literature is quasi-likelihood, which improves robustness by only requiring correct specification of the first two moments. We develop a robust approach to Bayesian inference in GLMs through quasi-posterior distributions. We show that quasi-posteriors provide a coherent generalized Bayes inference method, while also approximating so-called coarsened posteriors. In so doing, we obtain new insights into the choice of coarsening parameter. Asymptotically, the quasi-posterior converges in total variation to a normal distribution and has important connections with the loss-likelihood bootstrap posterior. We demonstrate that it is also well-calibrated in terms of frequentist coverage. Moreover, the loss-scale parameter has a clear interpretation as a dispersion, and this leads to a consolidated method of moments estimator.
Together apart: the influence of increased crowd heterogeneity on crowd dynamics at bottlenecks

Individual differences in mobility (e.g., due to wheelchair use) are often ignored in the prediction of crowd movement. Consequently, engineering tools cannot fully describe the impact of vulnerable populations on egress performance. To contribute to closing this gap, we performed laboratory experiments with 25 pedestrians with varying mobility profiles. The control condition comprised only participants without any additional equipment; in the luggage condition and the wheelchair condition, two participants at the center of the group either carried suitcases or used a wheelchair. We found that individuals using wheelchairs and to a lesser degree those carrying luggage needed longer to pass through the bottleneck, which also affected those walking behind them. This led to slower times to fully clear the bottleneck in the wheelchair and luggage condition compared to the control group. The results challenge the status quo in existing approaches to calculating egress performance and other key performance metrics in crowd dynamics.
Organic material in the global troposphere

Interest in the global tropospheric chemistry of organic materials has been growing rapidly over the past decade. In addition to a basic concern about the fundamental biogeochemical cycles of organic matter, this interest has arisen largely because of concern about the oxidant-forming potential of natural hydrocarbons, the possible importance of nonmethane hydrocarbons as a source for atmospheric CO, and the role played by organic material in the formation of secondary aerosol particles. In this review we consider the information presently available on concentration distribution, sources, sinks, and atmospheric transformation reactions of organic matter in the global troposphere. The data base for tropospheric organic compounds is very small. However, it is apparent that while anthropogenic sources often dominate the atmospheric chemistry of organic material in urban and near-urban air, a key to understanding the global cycling of tropospheric organic substances is a clear understanding of the interaction of the atmosphere with the terrestrial and marine biosphere.