Miracle-dz/UniFAR-FTUs huggingface dataset:
Tying Into the Idealized Standard Deviation

>> We present a metric to calculate the idealized standard deviation (ISD) in the event that ties are possible and receive points in team-sport league standings. Our aim is to raise some issues regarding the customary application of that concept in leagues that do not employ a binomial system for determining rankings, to suggest that the reciprocal of the ISD may be a more intuitive construct if the intent is to represent parity, to provide scholars with a set of ISDs, and to suggest some interesting topics that arise from a quick perusal of the time series.
Entropy inequalities for classes of probability distributions I. The univariate case

Entropy functionals of probability densities feature importantly in classifying certain finite-state stationary stochastic processes, in discriminating among competing hypotheses, in characterizing Gaussian, Poisson, and other densities, in describing information processes, and in other contexts. Two general types of problems are considered. For a given parametric family of densities the member of maximal (or sometimes minimal) entropy is ascertained. Secondly, we determine a natural (partial) ordering over for which the entropy functional is monotone. The examples include the multiparameter binomial, multiparameter negative binomial, some classes of log concave densities, and others.
A Power Formula for the SIBTEST Procedure for Differential Item Functioning

A power formula for the SIBTEST procedure for differential item functioning (DIF) is derived. The power of SIBTEST is related to the item response function (IRF) for the studied item, the latent trait distributions and the sample sizes for the reference and focal groups, and the proportion of each group in the sample. Based on the power formula, a method for calculating the sample size for a desired power level is presented. Monte Carlo simulation studies show that the theoretical values calculated from the formulas derived in the article are close to what are observed in the simulated data.
Parametric Estimation of Gibbs distributions as generalized maximum-entropy models for the analysis of spike train statistics.

We propose a generalization of the existing maximum entropy models used for spike trains statistics analysis. We bring a simple method to estimate Gibbs distributions, generalizing existing approaches based on Ising model or one step Markov chains to arbitrary parametric potentials. Our method enables one to take into account memory effects in dynamics. It provides directly the "free-energy" density and the Kullback-Leibler divergence between the empirical statistics and the statistical model. It does not assume a specific Gibbs potential form and does not require the assumption of detailed balance. Furthermore, it allows the comparison of different statistical models and offers a control of finite-size sampling effects, inherent to empirical statistics, by using large deviations results. A numerical validation of the method is proposed and the perspectives regarding spike-train code analysis are also discussed.
More Is Less?

Was there a tradeoff between family size and education during the demographic transition in the United States? Exploiting the occurrence of twin births as a source of exogenous variation in family size, I find that an additional sibling reduces the likelihood of attending school by one to two percentage points. To evaluate the persistence of family size effects, I create a linked sample of boys between 1920 and 1940. Individuals raised in larger families accumulate less human capital by adulthood. However, the impact of family size is quantitatively small relative to the average level of education in the population.
Enhanced 5-aminolevulinic acid-gold nanoparticle conjugate-based photodynamic therapy using pulse laser

The low bioavailability is a crucial limitation for the application of 5-aminolevulinic acid (ALA) in theranostics. In this research, 5-aminolevulinic acid and gold nanoparticle conjugates (ALA-GNPs) were synthesized to improve the bioavailability of ALA and to investigate the impact of ALA photodynamic therapy (ALA-PDT) in Hela cells. A 532 nm pulse laser and light-emitting diode (central wavelengths 502 nm) were jointly used as light sources in PDT research. The results show a 532 nm pulse laser can control ALA release from ALA-GNPs by adjusting the pulse laser dose. This laser control release may be attributed to the heat generation from GNPs under pulse laser irradiation, which indicates accurately adjusting the pulse laser dose to control the drug release in the cell interior can be considered as a new cellular surgery modality. Furthermore, the PDT results in Hela cells indicate the enhancement of ALA release by pulse laser before PDT can promote the efficacy of cell eradication in the light-emitting diode PDT (LED-PDT). This laser mediated drug release system can provide a new online therapy approach in PDT and it can be utilized in the optical monitor technologies based individual theranostics.
Vitamin D deficiency increases vulnerability to canagliflozin-induced adverse effects on 1,25-dihydroxyvitamin D and PTH

Context. Canagliflozin has been reported to increase the risk of bone fracture, possibly mediated by decreasing 1,25-dihydroxyvitamin D [1,25(OH)2D] and increasing PTH. Objective. To investigate whether baseline vitamin D (VitD) deficiency renders individuals vulnerable to this adverse effect and whether VitD3 supplementation is protective. Design. This study had a paired design comparing individual participants before and after VitD3 supplementation. Setting. Community-based outpatient. Patients. 11 VitD deficient (25-hydroxyvitamin D [25(OH)D] [≤] 20 ng/mL) individuals recruited from the Amish population in Lancaster PA. Interventions. Participants underwent two canagliflozin challenge protocols (300 mg daily for five days): the first before and the second after VitD3 supplementation. In the VitD3 supplementation protocol, participants received VitD3 supplementation (50,000 IU once or twice a week depending on BMI for 4-6 weeks) to achieve 25(OH)D [≥] 30 ng/mL. Main Outcome Measures. Two co-primary endpoints were identified: effects of VitD3 supplementation on canagliflozin-induced changes in 1,25(OH)2D and PTH. Secondary endpoints included effects of VitD3 supplementation on baseline levels of VitD metabolites and PTH. Results. VitD3 supplementation increased mean 25(OH)D from 16.5+/-1.6 to 44.3+/-5.5 ng/mL (p=0.0006) and 24,25-dihydroxyvitamin D [24,25(OH)2D] from 1.0+/-0.1 to 4.3+/-0.6 ng/mL (p=0.0002). Mean 1,25(OH)2D and PTH were unchanged. VitD3 supplementation decreased the magnitude of canagliflozin-induced changes in 1,25(OH)2D (from -31.3%+/-4.7% to -9.3%+/-8.3%; p=0.04) and PTH (from +36.2%+/-6.2% to +9.7%+/-3.7%; p=0.005). Conclusions. VitD deficiency rendered individuals more vulnerable to adverse effects of canagliflozin on biomarkers associated with bone health. VitD3 supplementation was protective against canagliflozins short-term adverse effects on 1,25(OH)2D and PTH.
L0-norm constraint normalized subband adaptive filtering algorithm: Performance development and AEC application

Limited by fixed step-size and sparsity penalty factor, the conventional sparsity-aware normalized subband adaptive filtering (NSAF) type algorithms suffer from trade-off requirements of high filtering accurateness and quicker convergence behavior for sparse system identification. To deal with this problem, this paper proposes variable step-size L0-norm constraint NSAF algorithms (VSS-L0-NSAFs). We first analyze mean-square-deviation (MSD) statistics behavior of the L0-NSAF innovatively in according to novel weight recursion form and arrive at corresponding expressions for the cases that background noise variance is available and unavailable, where correlation degree of system input is indicated by scaling parameter r. Based on derivations, we develop an effective variable step-size scheme through minimizing the upper bounds of the MSD under some reasonable assumptions and lemma. Furthermore, an effective reset strategy is incorporated into presented algorithms to tackle with non-stationary situations. Finally, numerical simulations corroborate that the proposed algorithms achieve better performance in terms of estimation accurateness and tracking capability in comparison with existing related algorithms in sparse system identification and adaptive echo cancellation circumstances.
Analysing similarities between legal court documents using natural language processing approaches based on Transformers

Recent advances in Artificial Intelligence (AI) have leveraged promising results in solving complex problems in the area of Natural Language Processing (NLP), being an important tool to help in the expeditious resolution of judicial proceedings in the legal area. In this context, this work targets the problem of detecting the degree of similarity between judicial documents that can be achieved in the inference group, by applying six NLP techniques based on the transformers architecture to a case study of legal proceedings in the Brazilian judicial system. The NLP transformer-based models, namely BERT, GPT-2 and RoBERTa, were pre-trained using a general purpose corpora of the Brazilian Portuguese language, and then were fine-tuned and specialised for the legal sector using 210,000 legal proceedings. Vector representations of each legal document were calculated based on their embeddings, which were used to cluster the lawsuits, calculating the quality of each model based on the cosine of the distance between the elements of the group to its centroid. We noticed that models based on transformers presented better performance when compared to previous traditional NLP techniques, with the RoBERTa model specialised for the Brazilian Portuguese language presenting the best results. This methodology can be also applied to other case studies for different languages, making it possible to advance in the current state of the art in the area of NLP applied to the legal sector.
Musketeer: Joint Training for Multi-task Vision Language Model with Task Explanation Prompts

We present a vision-language model whose parameters are jointly trained on all tasks and fully shared among multiple heterogeneous tasks which may interfere with each other, resulting in a single model which we named Musketeer. The integration of knowledge across heterogeneous tasks is enabled by a novel feature called Task Explanation Prompt (TEP). With rich and structured information such as task input/output format, TEP reduces interference among tasks, allowing the model to focus on their shared structure. With a single model, Musketeer achieves results comparable to or better than strong baselines trained on single tasks, almost uniformly across multiple tasks.
A Data-Driven Rutting Depth Short-Time Prediction Model with Metaheuristic Optimization for Asphalt Pavements Based on RIOHTrack

Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements' rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from 19 asphalt pavements with different crude oil sources on a 2.038 km long full-scale field accelerated pavement test track (Road Track Institute, RIOHTrack) in Tongzhou, Beijing. In addition, this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction (RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of average root mean squared error (MSE), average mean absolute error (MAE), and average mean absolute percentage error (MAPE) for 19 asphalt pavements reaching 1.742, 1.363, and 1.94% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters. It is a promising alternative method that facilitates the rapid assessment of pavement conditions and could be applied in the future to production processes in the oil and gas industry.
Decentralization and Acceleration Enables Large-Scale Bundle Adjustment

Scaling to arbitrarily large bundle adjustment problems requires data and compute to be distributed across multiple devices. Centralized methods in prior works are only able to solve small or medium size problems due to overhead in computation and communication. In this paper, we present a fully decentralized method that alleviates computation and communication bottlenecks to solve arbitrarily large bundle adjustment problems. We achieve this by reformulating the reprojection error and deriving a novel surrogate function that decouples optimization variables from different devices. This function makes it possible to use majorization minimization techniques and reduces bundle adjustment to independent optimization subproblems that can be solved in parallel. We further apply Nesterov's acceleration and adaptive restart to improve convergence while maintaining its theoretical guarantees. Despite limited peer-to-peer communication, our method has provable convergence to first-order critical points under mild conditions. On extensive benchmarks with public datasets, our method converges much faster than decentralized baselines with similar memory usage and communication load. Compared to centralized baselines using a single device, our method, while being decentralized, yields more accurate solutions with significant speedups of up to 953.7x over Ceres and 174.6x over DeepLM. Code: https://joeaortiz.github. io/daba.
An Information-Spectrum Approach to Distributed Hypothesis Testing for General Sources

This paper investigates Distributed Hypothesis testing (DHT), in which a source $\mathbf{X}$ is encoded given that side information $\mathbf{Y}$ is available at the decoder only. Based on the received coded data, the receiver aims to decide on the two hypotheses $H_0$ or $H_1$ related to the joint distribution of $\mathbf{X}$ and $\mathbf{Y}$. While most existing contributions in the literature on DHT consider i.i. d. assumptions, this paper assumes more generic, non-i.i. d. , non-stationary, and non-ergodic sources models. It relies on information-spectrum tools to provide general formulas on the achievable Type-II error exponent under a constraint on the Type-I error. The achievability proof is based on a quantize-and-binning scheme. It is shown that with the quantize-and-binning approach, the error exponent boils down to a trade-off between a binning error and a decision error, as already observed for the i.i. d. sources. The last part of the paper provides error exponents for particular source models, \emph{e.g.}, Gaussian, stationary, and ergodic models.
Pseudo-Hamiltonian system identification

Identifying the underlying dynamics of physical systems can be challenging when only provided with observational data. In this work, we consider systems that can be modelled as first-order ordinary differential equations. By assuming a certain pseudo-Hamiltonian formulation, we are able to learn the analytic terms of internal dynamics even if the model is trained on data where the system is affected by unknown damping and external disturbances. In cases where it is difficult to find analytic terms for the disturbances, a hybrid model that uses a neural network to learn these can still accurately identify the dynamics of the system as if under ideal conditions. This makes the models applicable in some situations where other system identification models fail. Furthermore, we propose to use a fourth-order symmetric integration scheme in the loss function and avoid actual integration in the training, and demonstrate on varied examples how this leads to increased performance on noisy data.
Image-to-Text Translation for Interactive Image Recognition: A Comparative User Study with Non-Expert Users

Interactive machine learning (IML) allows users to build their custom machine learning models without expert knowledge. While most existing IML systems are designed with classification algorithms, they sometimes oversimplify the capabilities of machine learning algorithms and restrict the user's task definition. On the other hand, as recent large-scale language models have shown, natural language representation has the potential to enable more flexible and generic task descriptions. Models that take images as input and output text have the potential to represent a variety of tasks by providing appropriate text labels for training. However, the effect of introducing text labels to IML system design has never been investigated. In this work, we aim to investigate the difference between image-to-text translation and image classification for IML systems. Using our prototype systems, we conducted a comparative user study with non-expert users, where participants solved various tasks. Our results demonstrate the underlying difficulty for users in properly defining image recognition tasks while highlighting the potential and challenges of interactive image-to-text translation systems.
Generalization bounds for neural ordinary differential equations and deep residual networks

Neural ordinary differential equations (neural ODEs) are a popular family of continuous-depth deep learning models. In this work, we consider a large family of parameterized ODEs with continuous-in-time parameters, which include time-dependent neural ODEs. We derive a generalization bound for this class by a Lipschitz-based argument. By leveraging the analogy between neural ODEs and deep residual networks, our approach yields in particular a generalization bound for a class of deep residual networks. The bound involves the magnitude of the difference between successive weight matrices. We illustrate numerically how this quantity affects the generalization capability of neural networks.
Constraining primordial magnetic fields with line-intensity mapping

Primordial magnetic fields (PMFs) offer a compelling explanation for the origin of observed magnetic fields, especially on extragalactic scales. Such PMFs give rise to excess of power in small scale matter perturbations that could strongly influence structure formation. We study the impact of the magnetically enhanced matter power spectrum on the signal that will be observed by line-intensity mapping (LIM) surveys targeting carbon monoxide (CO) emission from star-forming galaxies at high redshifts. Specifically, the voxel intensity distribution of intensity maps provides access to small-scale information, which makes it highly sensitive to signatures of PMFs on matter overdensities. We present forecasts for future LIM CO surveys, finding that they can constrain PMF strength as small as B 1Mpc ∼ 0.006–1 nG, depending on the magnetic spectral index and the targeted redshifts.
EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention

Vision transformers have shown great success due to their high model capabilities. However, their remarkable performance is accompanied by heavy computation costs, which makes them unsuitable for real-time applications. In this paper, we propose a family of high-speed vision transformers named Efficient ViT. We find that the speed of existing transformer models is commonly bounded by memory inefficient operations, especially the tensor reshaping and element-wise functions in MHSA. Therefore, we design a new building block with a sandwich layout, i.e., using a single memory-bound MHSA between efficient FFN layers, which improves memory efficiency while enhancing channel communication. Moreover, we discover that the attention maps share high similarities across heads, leading to computational redundancy. To address this, we present a cascaded group attention module feeding attention heads with different splits of the full feature, which not only saves computation cost but also improves attention diversity. Comprehensive experiments demonstrate EfficientViT outperforms existing efficient models, striking a good trade-off between speed and accuracy. For instance, our EfficientViT-M5 surpasses MobileNetV3-Large by 1.9% in accuracy, while getting 40.4% and 45.2% higher throughput on Nvidia V100 GPU and Intel Xeon CPU, respectively. Compared to the recent efficient model MobileViT-XXS, EfficientViT-M2 achieves 1.8% superior accuracy, while running $5.8\times/3.7\times$ faster on the GPU/CPU, and $7.4\times faster$ when converted to ONNX format. Code and models are available at here.
Adaptive Privacy-Preserving Coded Computing With Hierarchical Task Partitioning

Distributed computing is known as an emerging and efficient technique to support various intelligent services, such as large-scale machine learning. However, privacy leakage and random delays from straggling servers pose significant challenges. To address these issues, coded computing, a promising solution that combines coding theory with distributed computing, recovers computation tasks with results from a subset of workers. In this paper, we propose the adaptive privacy-preserving coded computing (APCC) strategy, which can adaptively provide accurate or approximated results according to the form of computation functions, so as to suit diverse types of computation tasks. We prove that APCC achieves complete data privacy preservation and demonstrate its optimality in terms of encoding rate, defined as the ratio between the computation loads of tasks before and after encoding. To further alleviate the straggling effect and reduce delay, we integrate hierarchical task partitioning and task cancellation into the coding design of APCC. The corresponding partitioning problems are formulated as mixed-integer nonlinear programming (MINLP) problems with the objective of minimizing task completion delay. We propose a low-complexity maximum value descent (MVD) algorithm to optimally solve these problems. Simulation results show that APCC can reduce task completion delay by a range of 20.3% to 47.5% when compared to other state-of-the-art benchmarks.
Minimax optimal density estimation using a shallow generative model with a one-dimensional latent variable

A deep generative model yields an implicit estimator for the unknown distribution or density function of the observation. This paper investigates some statistical properties of the implicit density estimator pursued by VAE-type methods from a nonparametric density estimation framework. More specifically, we obtain convergence rates of the VAE-type density estimator under the assumption that the underlying true density function belongs to a locally H\"{o}lder class. Remarkably, a near minimax optimal rate with respect to the Hellinger metric can be achieved by the simplest network architecture, a shallow generative model with a one-dimensional latent variable.