Miracle-dz/UniFAR-FTUs huggingface dataset:
A Structural Equation Modeling Approach to Understand User's Perceptions of Acceptance of Ride-Sharing Services in Dhaka City

This research aims at building a multivariate statistical model for assessing users' perceptions of acceptance of ride-sharing services in Dhaka City. A structured questionnaire is developed based on the users' reported attitudes and perceived risks. A total of 350 normally distributed responses are collected from ride-sharing service users and stakeholders of Dhaka City. Respondents are interviewed to express their experience and opinions on ride-sharing services through the stated preference questionnaire. Structural Equation Modeling (SEM) is used to validate the research hypotheses. Statistical parameters and several trials are used to choose the best SEM. The responses are also analyzed using the Relative Importance Index (RII) method, validating the chosen SEM. Inside SEM, the quality of ride-sharing services is measured by two latent and eighteen observed variables. The latent variable 'safety&security' is more influential than 'service performance' on the overall quality of service index. Under 'safety&security' the other two variables, i.e., 'account information' and 'personal information' are found to be the most significant that impact the decision to share rides with others. In addition, 'risk of conflict' and 'possibility of accident' are identified using the perception model as the lowest contributing variables. Factor analysis reveals the suitability and reliability of the proposed SEM. Identifying the influential parameters in this will help the service providers understand and improve the quality of ride-sharing service for users.
Common Randomness Generation from Sources with Countable Alphabet

We study a two-source model for common randomness (CR) generation in which the sender Alice and the receiver Bob generate a common random variable with a high probability of agreement by observing independent and identically distributed (i.i.d.) samples of correlated sources on countably infinite alphabets. The two parties are additionally allowed to communicate over a noisy memoryless channel. In our work, we establish a single-letter lower and upper-bound on the CR capacity for the proposed model. This is a challenging scenario because some of the finite alphabet properties, namely of the entropy can not be extended to the countably infinite case. We use a generalized typicality criterion, called unified typicality, which can be applied to random variables on countably infinite alphabets.
Large-scale linear programming using the Cholesky factorization

A variation of the revised simplex method is proposed for solving the standard linear programming problem. The method is derived from an algorithm recently proposed by Gill and Murray, and is based upon the orthogonal factorization B = LQ or, equivalently, upon the Cholesky factorization ${BB}^T = {LL}^T$ where B is the usual square basis, L is lower triangular and Q is orthogonal. We wish to retain the favorable numerical properties of the orthogonal factorization, while extending the work of Gill and Murray to the case of linear programs which are both large and sparse. The principal property exploited is that the Cholesky factor L depends only on $underline{which}$ variables are in the basis, and not upon the $underline{order}$ in which they happen to enter. A preliminary ordering of the rows of the full data matrix therefore promises to ensure that L will remain sparse throughout the iterations of the simplex method. An initial (in-core) version of the algorithm has been implemented in Algol W on the IBM 360/91 and tested on several medium-scale problems from industry (up to 930 constraints). While performance has not been especially good on problems of high density, the method does appear to be efficient on problems which are very sparse, and on structured problems which have either generalized upper bounding, block-angular, or staircase form.
CORRELATION OF CHANDRA PHOTONS WITH THE RADIO GIANT PULSES FROM THE CRAB PULSAR

No apparent correlation was found between giant pulses (GPs) and X-ray photons from the Crab pulsar during 5.4 hr of simultaneous observations with the Green Bank Telescope at 1.5 GHz and Chandra X-Ray Observatory primarily in the energy range of 1.5–4.5 keV. During the Crab pulsar periods with GPs, the X-ray flux in radio emission phase windows does not change more than by ±10% for main pulse (MP) GPs and ±30% for interpulse (IP) GPs. During GPs themselves, the X-ray flux does not change by more than two times for MP GPs and five times for IP GPs. All limits quoted are compatible with 2σ fluctuations of the X-ray flux around the sets of false GPs with random arrival times. The results speak in favor of changes in plasma coherence as the origin of GPs. However, the results do not rule out variations in the rate of particle creation if the particles that emit coherent radio emission are mostly at the lowest Landau level.
DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems

Recently, deep reinforcement learning (DRL) models have shown promising results in solving NP-hard Combinatorial Optimization (CO) problems. However, most DRL solvers can only scale to a few hundreds of nodes for combinatorial optimization problems on graphs, such as the Traveling Salesman Problem (TSP). This paper addresses the scalability challenge in large-scale combinatorial optimization by proposing a novel approach, namely, DIMES. Unlike previous DRL methods which suffer from costly autoregressive decoding or iterative refinements of discrete solutions, DIMES introduces a compact continuous space for parameterizing the underlying distribution of candidate solutions. Such a continuous space allows stable REINFORCE-based training and fine-tuning via massively parallel sampling. We further propose a meta-learning framework to enable the effective initialization of model parameters in the fine-tuning stage. Extensive experiments show that DIMES outperforms recent DRL-based methods on large benchmark datasets for Traveling Salesman Problems and Maximal Independent Set problems.
THETANULLS OF CYCLIC CURVES OF SMALL GENUS

We study relations among the classical thetanulls of cyclic curves, namely curves $\mathcal X$ (of genus $g(\mathcal X)>1$) with an automorphism $\sigma$ such that $\sigma$ generates a normal subgroup of the group $G$ of automorphisms, and $g (\mathcal X/ ) =0$. Relations between thetanulls and branch points of the projection are the object of much classical work, especially for hyperelliptic curves, and of recent work, in the cyclic case. We determine the curves of genus 2 and 3 in the locus $\mathcal M_g (G, \textbf{C})$ for all $G$ that have a normal subgroup $ $ as above, and all possible signatures \textbf{C}, via relations among their thetanulls.
Detailed Abundances for 28 Metal-poor Stars: Stellar Relics in the Milky Way

We present the results of an abundance analysis for a sample of stars with –4 < [ Fe/H ] < − 2. The data were obtained with the HIRES spectrograph at Keck Observatory. The set includes 28 stars, with effective temperature ranging from 4800 to 6600 K. For 13 stars with [ Fe/H ] < − 2.6, including nine with [ Fe/H ] < − 3.0 and one with [ Fe/H ] = − 4.0, these are the first reported detailed abundances. For the most metal-poor star in our sample, CS 30336–049, we measure an abundance pattern that is very similar to stars in the range [ Fe/H ] ∼ − 3.5, including a normal C + N abundance. We also find that it has very low but measurable Sr and Ba, indicating some neutron-capture activity even at this low of a metallicity. We explore this issue further by examining other very neutron capture-deficient stars and find that, at the lowest levels, [Ba/Sr] exhibits the ratio of the main r-process. We also report on a new r-process-enhanced star, CS 31078–018. This star has [ Fe/H ] = − 2.85, [ Eu/Fe ] = 1.23, and [ Ba/Eu ] = − 0.51. CS 31078–018 exhibits an “actinide boost,” i.e., much higher [Th/Eu] than expected and at a similar level to CS 31082–001. Our spectra allow us to further constrain the abundance scatter at low metallicities, which we then use to fit to the zero-metallicity Type II supernova yields of Heger & Woosley (2008). We find that supernovae with progenitor masses between 10 and 20 M☉ provide the best matches to our abundances.
Spectrally-Corrected and Regularized Linear Discriminant Analysis for Spiked Covariance Model

This paper proposes an improved linear discriminant analysis called spectrally-corrected and regularized LDA (SRLDA). This method integrates the design ideas of the sample spectrally-corrected covariance matrix and the regularized discriminant analysis. With the support of a large-dimensional random matrix analysis framework, it is proved that SRLDA has a linear classification global optimal solution under the spiked model assumption. According to simulation data analysis, the SRLDA classifier performs better than RLDA and ILDA and is closer to the theoretical classifier. Experiments on different data sets show that the SRLDA algorithm performs better in classification and dimensionality reduction than currently used tools.
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction

Despite the proliferation of explainable AI (XAI) methods, little is understood about end-users’ explainability needs and behaviors around XAI explanations. To address this gap and contribute to understanding how explainability can support human-AI interaction, we conducted a mixed-methods study with 20 end-users of a real-world AI application, the Merlin bird identification app, and inquired about their XAI needs, uses, and perceptions. We found that participants desire practically useful information that can improve their collaboration with the AI, more so than technical system details. Relatedly, participants intended to use XAI explanations for various purposes beyond understanding the AI’s outputs: calibrating trust, improving their task skills, changing their behavior to supply better inputs to the AI, and giving constructive feedback to developers. Finally, among existing XAI approaches, participants preferred part-based explanations that resemble human reasoning and explanations. We discuss the implications of our findings and provide recommendations for future XAI design.
Training Spiking Neural Networks with Local Tandem Learning

Spiking neural networks (SNNs) are shown to be more biologically plausible and energy efficient over their predecessors. However, there is a lack of an efficient and generalized training method for deep SNNs, especially for deployment on analog computing substrates. In this paper, we put forward a generalized learning rule, termed Local Tandem Learning (LTL). The LTL rule follows the teacher-student learning approach by mimicking the intermediate feature representations of a pre-trained ANN. By decoupling the learning of network layers and leveraging highly informative supervisor signals, we demonstrate rapid network convergence within five training epochs on the CIFAR-10 dataset while having low computational complexity. Our experimental results have also shown that the SNNs thus trained can achieve comparable accuracies to their teacher ANNs on CIFAR-10, CIFAR-100, and Tiny ImageNet datasets. Moreover, the proposed LTL rule is hardware friendly. It can be easily implemented on-chip to perform fast parameter calibration and provide robustness against the notorious device non-ideality issues. It, therefore, opens up a myriad of opportunities for training and deployment of SNN on ultra-low-power mixed-signal neuromorphic computing chips.10
Estimating Neural Reflectance Field from Radiance Field using Tree Structures

We present a new method for estimating the Neural Reflectance Field (NReF) of an object from a set of posed multi-view images under unknown lighting. NReF represents 3D geometry and appearance of objects in a disentangled manner, and are hard to be estimated from images only. Our method solves this problem by exploiting the Neural Radiance Field (NeRF) as a proxy representation, from which we perform further decomposition. A high-quality NeRF decomposition relies on good geometry information extraction as well as good prior terms to properly resolve ambiguities between different components. To extract high-quality geometry information from radiance fields, we re-design a new ray-casting based method for surface point extraction. To efficiently compute and apply prior terms, we convert different prior terms into different type of filter operations on the surface extracted from radiance field. We then employ two type of auxiliary data structures, namely Gaussian KD-tree and octree, to support fast querying of surface points and efficient computation of surface filters during training. Based on this, we design a multi-stage decomposition optimization pipeline for estimating neural reflectance field from neural radiance fields. Extensive experiments show our method outperforms other state-of-the-art methods on different data, and enable high-quality free-view relighting as well as material editing tasks.
Rethinking the Detection Head Configuration for Traffic Object Detection

Multi-scale detection plays an important role in object detection models. However, researchers usually feel blank on how to reasonably configure detection heads combining multi-scale features at different input resolutions. We find that there are different matching relationships between the object distribution and the detection head at different input resolutions. Based on the instructive findings, we propose a lightweight traffic object detection network based on matching between detection head and object distribution, termed as MHD-Net. It consists of three main parts. The first is the detection head and object distribution matching strategy, which guides the rational configuration of detection head, so as to leverage multi-scale features to effectively detect objects at vastly different scales. The second is the cross-scale detection head configuration guideline, which instructs to replace multiple detection heads with only two detection heads possessing of rich feature representations to achieve an excellent balance between detection accuracy, model parameters, FLOPs and detection speed. The third is the receptive field enlargement method, which combines the dilated convolution module with shallow features of backbone to further improve the detection accuracy at the cost of increasing model parameters very slightly. The proposed model achieves more competitive performance than other models on BDD100K dataset and our proposed ETFOD-v2 dataset. The code will be available.
Conditions for Proton Temperature Anisotropy to Drive Instabilities in the Solar Wind

Using high-resolution data from Solar Orbiter, we investigate the plasma conditions necessary for the proton temperature-anisotropy-driven mirror-mode and oblique firehose instabilities to occur in the solar wind. We find that the unstable plasma exhibits dependencies on the angle between the direction of the magnetic field and the bulk solar wind velocity which cannot be explained by the double-adiabatic expansion of the solar wind alone. The angle dependencies suggest that perpendicular heating in Alfvénic wind may be responsible. We quantify the occurrence rate of the two instabilities as a function of the length of unstable intervals as they are convected over the spacecraft. This analysis indicates that mirror-mode and oblique firehose instabilities require a spatial interval of length greater than 2–3 unstable wavelengths in order to relax the plasma into a marginally stable state and thus closer to thermodynamic equilibrium in the solar wind. Our analysis suggests that the conditions for these instabilities to act effectively vary locally on scales much shorter than the correlation length of solar wind turbulence.
Truncated proposals for scalable and hassle-free simulation-based inference

Simulation-based inference (SBI) solves statistical inverse problems by repeatedly running a stochastic simulator and inferring posterior distributions from model-simulations. To improve simulation efficiency, several inference methods take a sequential approach and iteratively adapt the proposal distributions from which model simulations are generated. However, many of these sequential methods are difficult to use in practice, both because the resulting optimisation problems can be challenging and efficient diagnostic tools are lacking. To overcome these issues, we present Truncated Sequential Neural Posterior Estimation (TSNPE). TSNPE performs sequential inference with truncated proposals, sidestepping the optimisation issues of alternative approaches. In addition, TSNPE allows to efficiently perform coverage tests that can scale to complex models with many parameters. We demonstrate that TSNPE performs on par with previous methods on established benchmark tasks. We then apply TSNPE to two challenging problems from neuroscience and show that TSNPE can successfully obtain the posterior distributions, whereas previous methods fail. Overall, our results demonstrate that TSNPE is an efficient, accurate, and robust inference method that can scale to challenging scientific models.
Utilizing Explainable AI for improving the Performance of Neural Networks

Nowadays, deep neural networks are widely used in a variety of fields that have a direct impact on society. Although those models typically show outstanding performance, they have been used for a long time as black boxes. To address this, Explainable Artificial Intelligence (XAI) has been developing as a field that aims to improve the transparency of the model and increase their trustworthiness. We propose a retraining pipeline that consistently improves the model predictions starting from XAI and utilizing state-of-the-art techniques. To do that, we use the XAI results, namely SHapley Additive exPlanations (SHAP) values, to give specific training weights to the data samples. This leads to an improved training of the model and, consequently, better performance. In order to benchmark our method, we evaluate it on both real-life and public datasets. First, we perform the method on a radar-based people counting scenario. Afterward, we test it on the CIFAR-10, a public Computer Vision dataset. Experiments using the SHAP-based retraining approach achieve a 4% more accuracy w.r. t. the standard equal weight retraining for people counting tasks. Moreover, on the CIFAR-10, our SHAP-based weighting strategy ends up with a 3% accuracy rate than the training procedure with equal weighted samples.
FCNC Processes in the Littlest Higgs Model with T-Parity: an Update

We update our 2006-2007 results for FCNC processes in the Littlest Higgs model with T-parity (LHT). The removal of the logarithmic UV cuto dependence in our previous results through a new contribution to the Z 0 -penguin diagrams identied by Goto et al. and del Aguila et al., while making the deviations from the SM expectations in the quark sector less spectacular, still allows for sizable new physics eects in
Sensitivity Analysis for Marginal Structural Models

Summary . We introduce several methods for assessing sensitivity to unmeasured confounding in marginal structural models; importantly we allow treatments to be discrete or continuous, static or time-varying. We consider three sensitivity models: a propensity-based model, an outcome-based model, and a subset confounding model, in which only a fraction of the population is subject to unmeasured confounding. In each case we develop efﬁcient estimators and conﬁdence intervals for bounds on the causal parameters.
Ribbon concordance and the minimality of tight fibered knots

Agol proved that ribbon concordance forms a partial ordering on the set of knots in the $3$-sphere. In this paper, we prove that all tight fibered knots are minimal in this partially ordered set. We also give the table of prime minimal knots up to $8$-crossings except for $8_{15}$.
A note on the dissipation for the general Muskat problem

We consider the dissipation of the Muskat problem and we give an elementary proof of a surprising inequality of Constantin-Cordoba-Gancedo-Strain [J. Eur. Math. Soc. (JEMS) 15 (2013), pp. 201–227 and Amer. J. Math. 138 (2016), pp. 1455–1494] which holds in greater generality.
FLUTE: Figurative Language Understanding through Textual Explanations

Figurative language understanding has been recently framed as a recognizing textual entailment (RTE) task (a.k.a. natural language inference (NLI)). However, similar to classical RTE/NLI datasets they suffer from spurious correlations and annotation artifacts. To tackle this problem, work on NLI has built explanation-based datasets such as eSNLI, allowing us to probe whether language models are right for the right reasons. Yet no such data exists for figurative language, making it harder to assess genuine understanding of such expressions. To address this issue, we release FLUTE, a dataset of 9,000 figurative NLI instances with explanations, spanning four categories: Sarcasm, Simile, Metaphor, and Idioms. We collect the data through a Human-AI collaboration framework based on GPT-3, crowd workers, and expert annotators. We show how utilizing GPT-3 in conjunction with human annotators (novices and experts) can aid in scaling up the creation of datasets even for such complex linguistic phenomena as figurative language. The baseline performance of the T5 model fine-tuned on FLUTE shows that our dataset can bring us a step closer to developing models that understand figurative language through textual explanations.
LOSS Revisited. I. Unraveling Correlations between Supernova Rates and Galaxy Properties, as Measured in a Reanalysis of the Lick Observatory Supernova Search

Most types of supernovae (SNe) have yet to be connected with their progenitor stellar systems. Here, we reanalyze the 10-year SN sample collected during 1998–2008 by the Lick Observatory Supernova Search (LOSS) in order to constrain the progenitors of SNe Ia and stripped-envelope SNe (SE SNe, i.e., SNe IIb, Ib, Ic, and broad-lined Ic). We matched the LOSS galaxy sample with spectroscopy from the Sloan Digital Sky Survey and measured SN rates as a function of galaxy stellar mass, specific star formation rate, and oxygen abundance (metallicity). We find significant correlations between the SN rates and all three galaxy properties. The SN Ia correlations are consistent with other measurements, as well as with our previous explanation of these measurements in the form of a combination of the SN Ia delay-time distribution and the correlation between galaxy mass and age. The ratio between the SE SN and SN II rates declines significantly in low-mass galaxies. This rules out single stars as SE SN progenitors, and is consistent with predictions from binary-system progenitor models. Using well-known galaxy scaling relations, any correlation between the rates and one of the galaxy properties examined here can be expressed as a correlation with the other two. These redundant correlations preclude us from establishing causality—that is, from ascertaining which of the galaxy properties (or their combination) is the physical driver for the difference between the SE SN and SN II rates. We outline several methods that have the potential to overcome this problem in future works.
The Small Solution Hypothesis for MAPF on Directed Graphs Is True

The determination of the computational complexity of multi-agent pathﬁnding on directed graphs has been an open problem for many years. Only recently, it has been established that the problem is NP-hard. Further, it has been proved that it is in NP, provided the short solution hypothesis for strongly connected digraphs holds. In this paper, it is shown that this hypothesis is indeed true.