Miracle-dz/UniFAR-FTUs huggingface dataset:
Mixed-Precision Paterson-Stockmeyer Method for Evaluating Polynomials of Matrices

The Paterson--Stockmeyer method is an evaluation scheme for matrix polynomials with scalar coefficients that arise in many state-of-the-art algorithms based on polynomial or rational approximation, for example, those for computing transcendental matrix functions. We derive a mixed-precision version of the Paterson--Stockmeyer method that is particularly useful for evaluating matrix polynomials with scalar coefficients of decaying magnitude. The key idea is to perform computations on data of small magnitude in low precision, and rounding error analysis is provided for the use of lower-than-working precisions. We focus on the evaluation of the Taylor approximants of the matrix exponential and show the applicability of our method to the existing scaling and squaring algorithms, particularly when the norm of the input matrix (which in practical algorithms is often scaled towards to origin) is sufficiently small. We also demonstrate through experiments the general applicability of our method to the computation of the polynomials from the Pad\'e approximant of the matrix exponential and the Taylor approximant of the matrix cosine. Numerical experiments show our mixed-precision Paterson--Stockmeyer algorithms can be more efficient than its fixed-precision counterpart while delivering the same level of accuracy.
Count What You Want: Exemplar Identification and Few-shot Counting of Human Actions in the Wild

This paper addresses the task of counting human actions of interest using sensor data from wearable devices. We propose a novel exemplar-based framework, allowing users to provide exemplars of the actions they want to count by vocalizing predefined sounds ``one'', ``two'', and ``three''. Our method first localizes temporal positions of these utterances from the audio sequence. These positions serve as the basis for identifying exemplars representing the action class of interest. A similarity map is then computed between the exemplars and the entire sensor data sequence, which is further fed into a density estimation module to generate a sequence of estimated density values. Summing these density values provides the final count. To develop and evaluate our approach, we introduce a diverse and realistic dataset consisting of real-world data from 37 subjects and 50 action categories, encompassing both sensor and audio data. The experiments on this dataset demonstrate the viability of the proposed method in counting instances of actions from new classes and subjects that were not part of the training data. On average, the discrepancy between the predicted count and the ground truth value is 7.47, significantly lower than the errors of the frequency-based and transformer-based methods. Our project, code and dataset can be found at https://github.com/cvlab-stonybrook/ExRAC.
Faithful geometric measures for genuine tripartite entanglement

We present a faithful geometric picture for genuine tripartite entanglement of discrete, continuous, and hybrid quantum systems. We first find that the triangle relation $\mathcal{E}^\alpha_{i|jk}\leq \mathcal{E}^\alpha_{j|ik}+\mathcal{E}^\alpha_{k|ij}$ holds for all subadditive bipartite entanglement measure $\mathcal{E}$, all permutations under parties $i, j, k$, all $\alpha \in [0, 1]$, and all pure tripartite states. It provides a geometric interpretation that bipartition entanglement, measured by $\mathcal{E}^\alpha$, corresponds to the side of a triangle, of which the area with $\alpha \in (0, 1)$ is nonzero if and only if the underlying state is genuinely entangled. Then, we rigorously prove the non-obtuse triangle area with $0<\alpha\leq 1/2$ is a measure for genuine tripartite entanglement. Useful lower and upper bounds for these measures are obtained, and generalizations of our results are also presented. Finally, it is significantly strengthened for qubits that, given a set of subadditive and non-additive measures, some state is always found to violate the triangle relation for any $\alpha>1$, and the triangle area is not a measure for any $\alpha>1/2$. Hence, our results are expected to aid significant progress in studying both discrete and continuous multipartite entanglement.
Sparsity Exploitation via Joint Receive Processing and Transmit Beamforming Design for MIMO-OFDM ISAC Systems

Integrated sensing and communication (ISAC) is widely recognized as a pivotal enabling technique for the advancement of future wireless networks. This paper aims to efficiently exploit the inherent sparsity of echo signals for the multi-input-multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) based ISAC system. A novel joint receive echo processing and transmit beamforming design is presented to achieve this goal. Specifically, we first propose a compressive sensing (CS)-assisted estimation approach to facilitate ISAC receive echo processing, which can not only enable accurate recovery of target information, but also allow substantial reduction in the number of sensing subcarriers to be sampled and processed. Then, based on the proposed CS-assisted processing method, the associated transmit beamforming design is formulated with the objective of maximizing the sum-rate of multiuser communications while satisfying the transmit power budget and ensuring the received signal-to-noise ratio (SNR) for the designated sensing subcarriers. In order to address the formulated non-convex problem involving high-dimensional variables, an effective iterative algorithm employing majorization minimization (MM), fractional programming (FP), and the nonlinear equality alternative direction method of multipliers (neADMM) with closed-form solutions has been developed. Finally, extensive numerical simulations are conducted to verify the effectiveness of the proposed algorithm and the superior performance of the introduced sparsity exploitation strategy.
Phase Boundaries, Isotope Effect and Superconductivity of Lithium Under Hydrostatic Conditions

We present theoretical and experimental studies of superconductivity and low temperature structural phase boundaries in lithium. We mapped the structural phase diagram of 6Li and 7Li under hydrostatic conditions between 5 top 55GPa and within the temperature range of 15 to 75K, observing the FCC-hR1-cI16 phase transitions. 6Li and 7Li show some differences at the structural boundaries, with a potential shift of the phase boundaries of 6Li to lower pressures. Density functional theory calculations and topological analysis of the electron density elucidates the superconducting properties and interatomic interactions within these phases of lithium.
RS-DGC: Exploring Neighborhood Statistics for Dynamic Gradient Compression on Remote Sensing Image Interpretation

Distributed deep learning has recently been attracting more attention in remote sensing (RS) applications due to the challenges posed by the increased amount of open data that are produced daily by Earth observation programs. However, the high communication costs of sending model updates among multiple nodes are a significant bottleneck for scalable distributed learning. Gradient sparsification has been validated as an effective gradient compression (GC) technique for reducing communication costs and thus accelerating the training speed. Existing state-of-the-art gradient sparsification methods are mostly based on the"larger-absolute-more-important"criterion, ignoring the importance of small gradients, which is generally observed to affect the performance. Inspired by informative representation of manifold structures from neighborhood information, we propose a simple yet effective dynamic gradient compression scheme leveraging neighborhood statistics indicator for RS image interpretation, termed RS-DGC. We first enhance the interdependence between gradients by introducing the gradient neighborhood to reduce the effect of random noise. The key component of RS-DGC is a Neighborhood Statistical Indicator (NSI), which can quantify the importance of gradients within a specified neighborhood on each node to sparsify the local gradients before gradient transmission in each iteration. Further, a layer-wise dynamic compression scheme is proposed to track the importance changes of each layer in real time. Extensive downstream tasks validate the superiority of our method in terms of intelligent interpretation of RS images. For example, we achieve an accuracy improvement of 0.51% with more than 50 times communication compression on the NWPU-RESISC45 dataset using VGG-19 network.
Assisted Path Planning for a UGV-UAV Team Through a Stochastic Network

In this article, we consider a multi-agent path planning problem in a stochastic environment. The environment, which can be an urban road network, is represented by a graph where the travel time for selected road segments (impeded edges) is a random variable because of traffic congestion. An unmanned ground vehicle (UGV) wishes to travel from a starting location to a destination while minimizing the arrival time at the destination. UGV can traverse through an impeded edge but the true travel time is only realized at the end of that edge. This implies that the UGV can potentially get stuck in an impeded edge with high travel time. A support vehicle, such as an unmanned aerial vehicle (UAV) is simultaneously deployed from its starting position to assist the UGV by inspecting and realizing the true cost of impeded edges. With the updated information from UAV, UGV can efficiently reroute its path to the destination. The UGV does not wait at any time until it reaches the destination. The UAV is permitted to terminate its path at any vertex. The goal is then to develop an online algorithm to determine efficient paths for the UGV and the UAV based on the current information so that the UGV reaches the destination in minimum time. We refer to this problem as Stochastic Assisted Path Planning (SAPP). We present Dynamic $k$-Shortest Path Planning (D*KSPP) algorithm for the UGV planning and Rural Postman Problem (RPP) formulation for the UAV planning. Due to the scalability challenges of RPP, we also present a heuristic based Priority Assignment Algorithm (PAA) for the UAV planning. Computational results are presented to corroborate the effectiveness of the proposed algorithm to solve SAPP.
Continuous families of bipartite 2-unitary matrices: a Paradigm for quantum convolution gates

As quantum computing develops, the problem of implementing entangling and disentangling quantum gates in a controllable manner reemerges in multiple contexts. One of the newest applications of such disentangling channels are quantum convolutional neural networks, where the core idea lies in the systematic decrease of qudit numbers without loss of information encoded in entangled states. In this work, we focus on quantum analogues of convolution and pooling - basic building block for convolutional networks - and construct and characterize parametrizable ``quantum convolution'' channels as coherifications of permutation tensors. Operations constructed in this manner generically provide high (dis)entangling power. In particular, we identify conditions necessary for the convolution channels constructed using our method to possess maximal entangling power. Based on this, we establish new, continuous classes of bipartite 2-unitary matrices of dimension $d^2$ for $d = 7$ and $d = 9$, with $2$ and $4$ free nonlocal parameters, corresponding to perfect tensors of rank $4$ or $4$-partite absolutely maximally entangled states. The newly established families may serve as the prototype for trainable convolution/pooling layers in quantum convolutional neural networks.
LiDAR Odometry Survey: Recent Advancements and Remaining Challenges

Odometry is crucial for robot navigation, particularly in situations where global positioning methods like global positioning system are unavailable. The main goal of odometry is to predict the robot’s motion and accurately determine its current location. Various sensors, such as wheel encoder, inertial measurement unit (IMU), camera, radar, and Light Detection and Ranging (LiDAR), are used for odometry in robotics. LiDAR, in particular, has gained attention for its ability to provide rich three-dimensional (3D) data and immunity to light variations. This survey aims to examine advancements in LiDAR odometry thoroughly. We start by exploring LiDAR technology and then scrutinize LiDAR odometry works, categorizing them based on their sensor integration approaches. These approaches include methods relying solely on LiDAR, those combining LiDAR with IMU, strategies involving multiple LiDARs, and methods fusing LiDAR with other sensor modalities. In conclusion, we address existing challenges and outline potential future directions in LiDAR odometry. Additionally, we analyze public datasets and evaluation methods for LiDAR odometry. To our knowledge, this survey is the first comprehensive exploration of LiDAR odometry.
Quasinormal modes of quantum-corrected black holes

In this paper, we investigate the quasinormal mode (QNM) spectra for scalar perturbation over a quantum-corrected black hole (BH). The fundamental modes of this quantum-corrected BH exhibit two key properties. Firstly, there is a non-monotonic behavior concerning the quantum-corrected parameter for zero multipole number. Secondly, the quantum gravity effects result in slower decay modes. For higher overtones, a significant deviation becomes evident between the quasinormal frequencies (QNFs) of the quantum-corrected and Schwarzschild BHs. The intervention of quantum gravity corrections induces a significant outburst of overtones. This outburst of these overtones can be attributed to the distinctions near the event horizons between the Schwarzschild and quantum-corrected BHs. Therefore, overtones can serve as a means to probe physical phenomena or disparities in the vicinity of the event horizon.
Classifying Intermediate-redshift Galaxies in SDSS: Alternative Diagnostic Diagrams

We select a sample of 1437 active galactic nuclei (AGN) from the catalog of the Sloan Digital Sky Survey galaxy properties from the Portsmouth group by detection of the high-ionization [Ne v] 3426 Å emission line. We compare the fluxes of [Ne III] 3869 Å, [O III] 5007 Å, [O II] 3726, 3728 Å, and [O i] 6300 Å to that of [Ne v]. All four lines show a strong linear correlation with [Ne v], although lines from ions with lower ionization potentials have a lower correlation coefficient. We investigate the use of two forbidden line ratio diagnostic diagrams that do not rely on Hα in order to classify high-redshift galaxies. These use the [Ne III]/[O II] line ratio plotted against [O III]/[O i] and [O III]/[O II], respectively. We use photoionization modeling to characterize the behavior of the narrow-line region in AGN and star-forming regions and test the validity of our diagnostic diagrams. We also use a luminosity cutoff of log L [O III] [erg s−1] = 42, which lowers the contamination of the AGN region by star-forming galaxies down to 10% but does not remove green pea and purple grape galaxies from the AGN region. We also investigate the OHNO diagram, which uses [Ne III]/[O II] plotted against [O III]/Hβ. Using our new diagnostic diagrams, we are able to reliably classify AGN up to a redshift of z ≤ 1.06 and add more than 822 new AGN to the [Ne v]-selected AGN sample.
Density estimation using the perceptron

We propose a new density estimation algorithm. Given $n$ i.i. d. samples from a distribution belonging to a class of densities on $\mathbb{R}^d$, our estimator outputs any density in the class whose ''perceptron discrepancy'' with the empirical distribution is at most $O(\sqrt{d/n})$. The perceptron discrepancy between two distributions is defined as the largest difference in mass that they place on any halfspace of $\mathbb{R}^d$. It is shown that this estimator achieves expected total variation distance to the truth that is almost minimax optimal over the class of densities with bounded Sobolev norm and Gaussian mixtures. This suggests that regularity of the prior distribution could be an explanation for the efficiency of the ubiquitous step in machine learning that replaces optimization over large function spaces with simpler parametric classes (e.g. in the discriminators of GANs). We generalize the above to show that replacing the ''perceptron discrepancy'' with the generalized energy distance of Sz\'ekeley-Rizzo further improves total variation loss. The generalized energy distance between empirical distributions is easily computable and differentiable, thus making it especially useful for fitting generative models. To the best of our knowledge, it is the first example of a distance with such properties for which there are minimax statistical guarantees.
Parameter Optimization with Conscious Allocation (POCA)

The performance of modern machine learning algorithms depends upon the selection of a set of hyperparameters. Common examples of hyperparameters are learning rate and the number of layers in a dense neural network. Auto-ML is a branch of optimization that has produced important contributions in this area. Within Auto-ML, hyperband-based approaches, which eliminate poorly-performing configurations after evaluating them at low budgets, are among the most effective. However, the performance of these algorithms strongly depends on how effectively they allocate the computational budget to various hyperparameter configurations. We present the new Parameter Optimization with Conscious Allocation (POCA), a hyperband-based algorithm that adaptively allocates the inputted budget to the hyperparameter configurations it generates following a Bayesian sampling scheme. We compare POCA to its nearest competitor at optimizing the hyperparameters of an artificial toy function and a deep neural network and find that POCA finds strong configurations faster in both settings.
Olapa-MCoT: Enhancing the Chinese Mathematical Reasoning Capability of LLMs

CoT (Chain-of-Thought) is a way to solve reasoning problems for LLMs . Recently, many researches appear for improving the CoT capability of LLMs. In this work, we also proposed Olapa-MCoT, which is a LLMs based on llama2-13B PLM for finetuning and alignment learning. During the alignment training, we proposed the SimRRHF algorithm and Incorrect Data Relearning and mainly focused on optimizing the Chinese mathematical reasoning ability of Olapa-MCoT. The experiment achieved significant results, with the accuracy of Chinese mathematical reasoning up to 50%, 36% rise compared to llama2-13B. In addition, the accuracy of English reasoning ability also increased by nearly 4%.
Futures Trading, Information and Spot Price Volatility of Nse-50 Index Futures Contract

The purpose of the study is to examine if there is any change in the volatility of Nifty index due to the introduction of Nifty futures and whether movements in the futures price provide predictive information regarding subsequent movements in the index prices. The study shows that inception of futures trading has reduced the volatility of spot index returns. The information flow is higher in the post futures period resulting in decline in spot index volatility in the post futures period. The lead lag analysis shows that futures have little or no memory effect and infrequent trading is virtually absent in futures market. The study also shows that futures market transmits information to cash market and futures market is faster than spot market in processing information. The futures returns lead the spot index returns by one day and this relationship is robust. It is also shown that the cash index returns do not lead the futures returns.
Modified Osgood Equation for Acacia Mangium

The purpose of this study is to identify the Osgood’s coefficient of species and the Modified Osgood Equation for Acacia mangium. Acacia mangium trees were cut to produce oven-dried Small Clear Specimens that were then tested until fracture. Results were gathered from static tensile tests in the direction parallel (0° angle), perpendicular (90° angle) and at 30° angle to the wood grain. All test results confirmed that the Acacia mangium wood is brittle as there was no obvious necking observed on the test specimens. From the static testing, the Osgood’s coefficient of species for Acacia mangium, (a), is identified algebraically to be 0.49. Acacia mangium, by nature, has a significant difference in the strengths parallel and perpendicular to the grain line. The finalized results of the Ultimate Tensile Strength for 15-year old Acacia mangium demonstrated that the Ultimate Tensile Strength in parallel and perpendicular to the grain directions are 143.87 MPa and 6.32 MPa respectively, while the Ultimate Tensile Strength at 30° grain angle is 32.985 MPa. An extreme reduction of 95.6% of the Ultimate Tensile Strength was identified between 0° and 90° grain angles with a decreased value from 143.87 MPa to 6.32 MPa.
Political Fragmentation, Labour Mobility, and Voter Turnout Decline in Border Areas (Batam Island) in Indonesia

The fall in voter turnover has emerged as a significant concern, particularly in island regions, warranting increased attention from researchers. Therefore, the primary objective of this study is to investigate the impact of political fragmentation and labor migration on the fall in voter turnout in Batam, Indonesia. This study also investigates the moderating influence of insufficient government support on the relationship between political fragmentation, labor migration, and the fall in voter turnout in Batam, Indonesia.The analysis utilized a quantitative methodology and collected primary data through the administration of questionnaires. The research employed the smart-PLS methodology to examine the interrelationships among the variables. The findings revealed a strong correlation between political fragmentation and labor migration and the fall in voter turnout in Batam, Indonesia. The findings also revealed that the absence of government assistance has a substantial moderating role in the relationship between political fragmentation, labor migration, and the fall in voter turnout in Batam, Indonesia. This study serves to assist policymakers in formulating policies aimed at enhancing voter turnout in Batam, Indonesia. Additionally, it provides guidance to election organizers, emphasizing the importance of recognizing migrant behavior in order to mitigate any potential reduction in voter participation that may undermine confidence in the democratic process.
INDUSTRIAL POLICY AND MANUFACTURING SUBSECTOR PERFORMANCE IN NIGERIA: An Assessment of Two Regimes

Recently, the Nigerian government signalled a return to an explicit industrial policy regime by prioritizing the growth of selected manufacturing subsectors, such as the textile, apparel and footwear subsector through several support interventions and incentives. This study empirically investigated if this new industrial policy regime has significantly improved performance of the manufacturing sector, especially comparing prioritized sectors with less-prioritized sectors. Using the manufacturing subsectors’ performance data panel compiled by the National Bureau of Statistics and applying rigorous policy impact evaluation approaches, the study found no significant performance difference between prioritized subsectors and less prioritized subsectors or between the earlier regime of implicit industrial policy and the new explicit industrial policy regime.