Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of socioeconomic factors influencing global food price security using machine learning

Global concern over food prices and security has been exacerbated by the impacts of armed conflicts such as the Russia Ukraine War, pandemic diseases, and climate change. Traditionally, analyzing global food prices and their associations with socioeconomic factors has relied on static linear regression models. However, the complexity of socioeconomic factors and their implications extend beyond simple linear relationships. By incorporating determinants, critical characteristics identification, and comparative model analysis, this study aimed to identify the critical socioeconomic characteristics and multidimensional relationships associated with the underlying factors of food prices and security. Machine learning tools were used to uncover the socioeconomic factors influencing global food prices from 2000 to 2022. A total of 105 key variables from the World Development Indicators and the Food and Agriculture Organization of the United Nations were selected. Machine learning identified four key dimensions of food price security: economic and population metrics, military spending, health spending, and environmental factors. The top 30 determinants were selected for feature extraction using data mining. The efficiency of the support vector regression model allowed for precise prediction making and correlation analysis. Keywords: environment and growth, global economics, price fluctuation, support vector regression
Spin-Phonon interaction in quasi 2D- Cr$_2Te_3$

Spin-phonon interaction plays an important role in 2D magnetic materials and motivates the development of next-generation spin- and charge-dependent microelectronic devices. Understanding the spin-phonon interaction by tuning the growth parameter of single crystal Cr$_2Te_3$, a robust quasi-2D room temperature magnetic material, is crucial for spintronic devices. The synthesis of single crystal 2D Cr$_2Te_3$ flakes on a Si substrate from co-deposited thin film by plasma annealing techniques is a significant achievement. The temperature dependence and polarization-resolved Raman spectroscopy with support of density functional theory classified lattice symmetry operations were used to identify the phonon modes to investigate the spin/electron-phonon interactions in Cr$_2Te_3$. The mean-field theory model in single crystal Cr$_2Te_3$ is employed to quantify the spin-phonon interaction and correlate with in-plane and out-of-plane magnetic behavior. The observation of a positive correlation between phonon mode frequency and spin-phonon interaction strength in single crystal Cr$_2Te_3$ can be a potential candidate for spintronic applications.
Chance-Constrained Control for Safe Spacecraft Autonomy: Convex Programming Approach

This paper presents a robust path-planning framework for safe spacecraft autonomy under uncertainty and develops a computationally tractable formulation based on convex programming. We utilize chance-constrained control to formulate the problem. It provides a mathematical framework to solve for a sequence of control policies that minimizes a probabilistic cost under probabilistic constraints with a user-defined confidence level (e.g., safety with 99.9% confidence). The framework enables the planner to directly control state distributions under operational uncertainties while ensuring the vehicle safety. This paper rigorously formulates the safe autonomy problem, gathers and extends techniques in literature to accommodate key cost/constraint functions that often arise in spacecraft path planning, and develops a tractable solution method. The presented framework is demonstrated via two representative numerical examples: safe autonomous rendezvous and orbit maintenance in cislunar space, both under uncertainties due to navigation error from Kalman filter, execution error via Gates model, and imperfect force models.
Impact of bright-dark exciton thermal population mixing on the brightness of CsPbBr$_3$ nanocrystals

Understanding the interplay between bright and dark exciton states is crucial for deciphering the luminescence properties of low-dimensional materials. The origin of the outstanding brightness of lead halide perovskites remains elusive. Here, we analyse temperature-dependent time-resolved photoluminescence to investigate the population mixing between bright and dark exciton sublevels in individual CsPbBr3 nanocrystals in the intermediate confinement regime. We extract bright and dark exciton decay rates, and show quantitatively that the decay dynamics can only be reproduced with second-order phonon transitions. Furthermore, we find that any exciton sublevel ordering is compatible with the most likely population transfer mechanism. The remarkable brightness of lead halide perovskite nanocrystals rather stems from a reduced asymmetry between bright-to-dark and dark-to-bright conversion originating from the peculiar second-order phonon-assisted transitions that freeze bright-dark conversion at low temperature together with the very fast radiative recombination and favourable degeneracy of the bright exciton state.
Cedar: A New Language for Expressive, Fast, Safe, and Analyzable Authorization (Extended Version)

Cedar is a new authorization policy language designed to be ergonomic, fast, safe, and analyzable. Rather than embed authorization logic in an application's code, developers can write that logic as Cedar policies and delegate access decisions to Cedar's evaluation engine. Cedar's simple and intuitive syntax supports common authorization use-cases with readable policies, naturally leveraging concepts from role-based, attribute-based, and relation-based access control models. Cedar's policy structure enables access requests to be decided quickly. Cedar's policy validator leverages optional typing to help policy writers avoid mistakes, but not get in their way. Cedar's design has been finely balanced to allow for a sound and complete logical encoding, which enables precise policy analysis, e.g., to ensure that when refactoring a set of policies, the authorized permissions do not change. We have modeled Cedar in the Lean programming language, and used Lean's proof assistant to prove important properties of Cedar's design. We have implemented Cedar in Rust, and released it open-source. Comparing Cedar to two open-source languages, OpenFGA and Rego, we find (subjectively) that Cedar has equally or more readable policies, but (objectively) performs far better.
Implicit Redundancy and Degeneracy in Conic Program

This paper examines the feasible region of a standard conic program represented as the intersection of a closed convex cone and a set of linear equalities. It is recently shown that when Slater constraint qualification (strict feasibility) fails for the classes of linear and semidefinite programs, two key properties emerge within the feasible region; (a) every point in the feasible region is degenerate; (b) the constraint system inherits implicit redundancies. In this paper we show that degeneracy and implicit redundancies are inherent and universal traits of all conic programs in the absence of strict feasibility.
A sharp H\"{o}rmander condition for bilinear Fourier multipliers with Lipschitz singularities

This paper studies the $L^{p}$ boundedness of bilinear Fourier multipliers in the local $L^{2}$ range. We assume a H\"{o}rmander condition relative to a singular set that is a finite union of Lipschitz curves. The H\"{o}rmander condition is sharp with respect to the Sobolev exponent. Our setup generalizes the non-degenerate bilinear Hilbert transform but avoids issues of uniform bounds near degeneracy.
A Model Hierarchy for Predicting the Flow in Stirred Tanks with Physics-Informed Neural Networks

This paper explores the potential of Physics-Informed Neural Networks (PINNs) to serve as Reduced Order Models (ROMs) for simulating the flow field within stirred tank reactors (STRs). We solve the two-dimensional stationary Navier-Stokes equations within a geometrically intricate domain and explore methodologies that allow us to integrate additional physical insights into the model. These approaches include imposing the Dirichlet boundary conditions (BCs) strongly and employing domain decomposition (DD), with both overlapping and non-overlapping subdomains. We adapt the Extended Physics-Informed Neural Network (XPINN) approach to solve different sets of equations in distinct subdomains based on the diverse flow characteristics present in each region. Our exploration results in a hierarchy of models spanning various levels of complexity, where the best models exhibit l1 prediction errors of less than 1% for both pressure and velocity. To illustrate the reproducibility of our approach, we track the errors over repeated independent training runs of the best identified model and show its reliability. Subsequently, by incorporating the stirring rate as a parametric input, we develop a fast-to-evaluate model of the flow capable of interpolating across a wide range of Reynolds numbers. Although we exclusively restrict ourselves to STRs in this work, we conclude that the steps taken to obtain the presented model hierarchy can be transferred to other applications.
Extract non-Gaussian Features in Gravitational Wave Observation Data Using Self-Supervised Learning

We propose a self-supervised learning model to denoise gravitational wave (GW) signals in the time series strain data without relying on waveform information. Denoising GW data is a crucial intermediate process for machine-learning-based data analysis techniques, as it can simplify the model for downstream tasks such as detections and parameter estimations. We use the blind-spot neural network and train it with whitened strain data with GW signals injected as both input data and target. Under the assumption of a Gaussian noise model, our model successfully denoises 38% of GW signals from binary black hole mergers in H1 data and 49% of signals in L1 data detected in the O1, O2, and O3 observation runs with an overlap greater than 0.5. We also test the model's potential to extract glitch features and loud inspiral compact binary coalescence signals a few seconds before the merger.
Spanning Tree-based Query Plan Enumeration

In this work, we define the problem of finding an optimal query plan as finding spanning trees with low costs. This approach empowers the utilization of a series of spanning tree algorithms, thereby enabling systematic exploration of the plan search space over a join graph. Capitalizing on the polynomial time complexity of spanning tree algorithms, we present the Ensemble Spanning Tree Enumeration (ESTE) strategy. ESTE employs two conventional spanning tree algorithms, Prim's and Kruskal's, together to enhance the robustness of the query optimizer. In ESTE, multiple query plans are enumerated exploring different areas of the search space. This positions ESTE as an intermediate strategy between exhaustive and heuristic enumeration strategies. We show that ESTE is more robust in identifying efficient query plans for large queries. In the case of data modifications and workload demand increase, we believe that our approach can be a cheaper alternative to maintain optimizer robustness by integrating additional spanning tree algorithms rather than completely changing the optimizer to another plan enumeration algorithm. The experimental evaluation shows that ESTE achieves better consistency in plan quality and optimization time than existing solutions while identifying similarly optimal plans.
Speeding up all-electron real-time TDDFT demonstrated by the exciting package

Currently, many ab initio codes are being prepared for exascale computing. A first and important step is to significantly improve the efficiency of existing implementations by devising better algorithms that can accomplish the same tasks with enhanced scalability. This manuscript addresses this challenge for real-time time-dependent density functional theory in the full-potential all-electron code exciting, with a focus on systems with reduced dimensionality. Following the strategy described here, calculations can run orders of magnitude faster than before. We demonstrate this with the molecules H$_2$ and CO, achieving speedups between 98 to over 50,000. We also present an example where conventional calculations would be particularly costly, namely the inorganic/organic heterostructure of pyridine physisorbed on monolayer MoS$_2$.
That's My Point: Compact Object-centric LiDAR Pose Estimation for Large-scale Outdoor Localisation

This paper is about 3D pose estimation on LiDAR scans with extremely minimal storage requirements to enable scalable mapping and localisation. We achieve this by clustering all points of segmented scans into semantic objects and representing them only with their respective centroid and semantic class. In this way, each LiDAR scan is reduced to a compact collection of four-number vectors. This abstracts away important structural information from the scenes, which is crucial for traditional registration approaches. To mitigate this, we introduce an object-matching network based on self- and cross-correlation that captures geometric and semantic relationships between entities. The respective matches allow us to recover the relative transformation between scans through weighted Singular Value Decomposition (SVD) and RANdom SAmple Consensus (RANSAC). We demonstrate that such representation is sufficient for metric localisation by registering point clouds taken under different viewpoints on the KITTI dataset, and at different periods of time localising between KITTI and KITTI-360. We achieve accurate metric estimates comparable with state-of-the-art methods with almost half the representation size, specifically 1.33 kB on average.
iScore: Visual Analytics for Interpreting How Language Models Automatically Score Summaries

The recent explosion in popularity of large language models (LLMs) has inspired learning engineers to incorporate them into adaptive educational tools that automatically score summary writing. Understanding and evaluating LLMs is vital before deploying them in critical learning environments, yet their unprecedented size and expanding number of parameters inhibits transparency and impedes trust when they underperform. Through a collaborative user-centered design process with several learning engineers building and deploying summary scoring LLMs, we characterized fundamental design challenges and goals around interpreting their models, including aggregating large text inputs, tracking score provenance, and scaling LLM interpretability methods. To address their concerns, we developed iScore, an interactive visual analytics tool for learning engineers to upload, score, and compare multiple summaries simultaneously. Tightly integrated views allow users to iteratively revise the language in summaries, track changes in the resulting LLM scores, and visualize model weights at multiple levels of abstraction. To validate our approach, we deployed iScore with three learning engineers over the course of a month. We present a case study where interacting with iScore led a learning engineer to improve their LLM's score accuracy by three percentage points. Finally, we conducted qualitative interviews with the learning engineers that revealed how iScore enabled them to understand, evaluate, and build trust in their LLMs during deployment.
The Power of Lorentz Quantum Computer

We demonstrate the superior capabilities of the recently proposed Lorentz quantum computer (LQC) compared to conventional quantum computers. We introduce an associated computational complexity class, bounded-error Lorentz quantum polynomial-time (BLQP), and prove that the complexity class ${\text P}^{\sharp \text{P}}$ is contained within BLQP. We present LQC algorithms that solve in polynomial time the problem of maximum independent set and the problems in the classes of NP, co-NP, PH (polynomial hierarchy), PP (probabilistic polynomial-time), and ${\text P}^{\sharp \text{P}}$. We show that the quantum computing with postselection proposed by Aaronson can be simulated efficiently by LQC, but not vice versa.
Rangeland Management Series: Annual Rangeland Forage Quality

Author(s): George, Melvin; Nader, Glenn; McDougald, Neil; Connor, Mike; Frost, Bill | Abstract: Livestock on California's foothill rangelands get much of their nutrition from rangeland forage plants. Year-to-year variations in environmental conditions determine the nutrient content of this forage.
Retinopathy associated with ivermectin toxicosis in two dogs

Case Description —2 dogs (dogs 1 and 2) were examined for sudden onset of blindness. Both dogs had mild obtundation and mydriasis in both eyes. It was thought that dog 1 may have ingested ivermectin; dog 2 had been treated with ivermectin
Research on College English Teaching Reform Based on China’s Standards of English Language Ability

In recent years, with the in-depth development of the new curriculum teaching reform and the rapid development of the social economy, traditional English teaching is no longer suitable for the development of the current era. The setting of teaching objectives, the planning of the learning process, the setting of curriculum construction and the setting of evaluation systems cannot meet the current situation and requirements of students and teaching. English teaching re-form is imminent, only reform can better meet the needs of social development, improve the quality of teaching, and promote the improvement of students’ English ability. China’s Standards of English Language Ability is a new English teaching concept born under the background of reform. The Scale provides a more scientific, effective, unified, and coherent standard for Chinese English learners in various stages of education, which has a great impact on college English teaching. Under the guidance of the Scale, this paper aims to optimize the existing college English teaching model, reconstruct the new teaching environment, and improve the quality of foreign language application-oriented talents training from the aspects of optimizing teaching, adjusting students’ learning methods and perfecting the assessment.
Latent Proﬁle Analysis of Cognitive Performance and Depressive Symptoms Among People with HIV

Depression and cognitive impairment are prevalent conditions among people with HIV (PWH), likely attributable to shared causes and common risk factors. Identifying subtypes of PWH with similar patterns of neurocognitive impairment (NCI) and depressive symptoms may inform development of patient-centered interventions that target-speciﬁc proﬁles. This study aimed to (1) classify PWH based on patterns of domain-speciﬁc NCI and depression; and (2) determine the relationship between latent class membership and pertinent clinical characteristics. PWH ( N = 580, 86.2% male, 57.1% non-Hispanic White, 69.2% unemployed) completed a comprehensive neuropsychological test battery assessing global and domain-speciﬁc cognition. Domain-speciﬁc NCI was classiﬁed as deﬁcit score > 0.5. Participants completed the Beck Depression Inventory-II (BDI-II), and domain-speciﬁc BDI-II scores reﬂecting cognitive, affective, and somatic symptoms were computed. Latent proﬁle analysis (LPA) was used to determine latent subgroups of NCI and depression. The optimal LPA solution consisted of ﬁve classes: minimal NCI and minimal depression (Class 1), amnestic and minimal depression (Class 2), severe multi-domain NCI and moderate depression (somatic and affective; Class 3), mild NCI and mild depression (Class 4), and moderate multi-domain NCI and severe depression (Class 5). Despite similar levels of functional impairment, Class 5 had a signiﬁcant psychiatric proﬁle, whereas Class 3 had a complex medical proﬁle (i
with Deceptive

—Routing protocols based on the link-state paradigm notoriously suffer from the problem of stale information in highly dynamic trafﬁc scenarios, leading to an overall loss in routing efﬁciency. Solutions are not easy to come by, since an increase in the frequency of link state advertisements is equally dangerous: route ﬂapping (i.e., periodic route changes that force trafﬁc to be routed through an alternately underloaded set of paths) is one of the main drawbacks. In this paper we propose a novel, yet simple link state mechanism that may deliberately advertise false link state information with the purpose of stabilizing the routing, while keeping a high network utilization.
Static Memory Dependence Analysis for Hardware Optimizations

Modern Out-of-Order hardware can determine the memory dependencies between individual instructions and change their execution order to maximize pipeline usage, executing non-dependent instructions during long-latency memory accesses. However, this kind of computation is redundant, as the information computed by the hardware at run-time is available to the compiler at compile-time. We introduce a collaborative software-hardware approach that leverages the LLVM compiler infrastructure to perform memory disambiguation at compile-time. We show that a majority of load instructions can beneﬁt from such a technique for a wide range of workloads.
Assessing Joint Engagement Between Children With Autism Spectrum Disorder and Their Parents During The Home Intervention Sessions From the Expressive Language Aspect

The World Health Organization (WHO) has instituted the Caregiver Skill Training (CST) program to assist families with children diagnosed with Autism Spectrum Disorder. The Joint Engagement Rating Inventory (JERI) protocol evaluates participants’ engagement levels within the CST initiative. Traditionally, JERI assessments rely on retrospective video analysis conducted by qualiﬁed professionals, thus incurring substantial labor costs. This study aims to augment the evaluation eﬃ-ciency of the Expressive Language Level and Use (EXLA) criterion within JERI, striving for consistency with human-based scoring. To this end, we introduce a multimodal behavioral signal-processing framework designed to analyze both child and caregiver behaviors, thereby oﬀering grading recommendations as an alternative to medical professional input. Initially, raw audio and video signals are segmented into concise intervals via voice activity detection, speaker diarization and speaker age classiﬁcation, serving the dual purpose of eliminating non-speech content and tagging each segment with its respective speaker. Subsequently, we extract an array of audio-visual features, encompassing our proposed interpretable, hand-crafted textual features, end-to-end audio embeddings and end-to-end video embeddings. Finally, these features are fused at the feature level to train a linear regression model aimed at predicting the EXLA scores. Our framework has been evaluated on the largest in-the-wild database currently available under the CST program. Experimental results indicate that the proposed system achieves a Pearson Correlation Coeﬃcient of 0.713 against the expert ratings, evidencing performance comparable to that of human experts. This approach not only provides immediate feedback for CST participants but also optimizes resource allocation in less developed regions.
Choice in Blended Learning: Effects on Student Motivation and Mathematics Achievement

: Over the past decade, there has been an urgent calling for the improvement of teaching and learning of mathematics. At that same time, research has provided promising findings in the value of self-regulation and choice on learning for student achievement. With the ever-increasing availability of resources for educators to support student learning, this study explores student motivation, learning strategies and learning associated with student choice in a blended learning environment. A quantitative quasi-experimental designed study revealed no significant difference in student motivation, learning strategies, or mastery of geometry content between groups of students.