Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the Efficiency of Senior Secondary Schools: Evidence From Schools in the Greek Region of Central Macedonia

Purpose: This study examines the efficiency of Senior Secondary Schools, in the Region of Central Macedonia in Greece, using input-oriented Data Envelopment Analysis, with three inputs and two outputs variables. Data concern schools in urban, semi-urban/rural, for the school years 2007-08 (before the economic crisis) and 2010-11 (during the economic crisis). Design/methodology/approach: In this study, Data Envelopment Analysis is applied under the Constant Returns to Scale (CRS) or Variable Returns to Scale (VRS) hypotheses. The study used a Senior Secondary Schools sample with stratified proportional sampling. This studyâ€™s data collection has been accomplished with the help of the information systems and the databases maintained in every school. Findings: The empirical analysis revealed that the majority of schools were inefficient. The technical efficiency under constant returns to scale varies in interval [0.510-1] and [0.511-1], with average score being 0.729 and 0.827, for the school years 2007-08 and 2010-11, respectively. Under variable returns to scale varies in interval [0.521-1] and [0.516-1], with average efficiency score being 0.815 and 0.834, for the school years 2007-08 and 2010-11, respectively. The scale efficiency for 2007-08 varies in interval [0.673-1], for 2010-11 varies in interval [0.939-1], with average efficiency score being 0.897 and 0.991, respectively. During the economic crisis the performance of schools improved. Additionally, the schools in semi-urban/rural areas had on average higher efficiency than those in urban areas. Research limitations/implications: Further research can extend this study. For instance, future studies could introduce additional input and output variables. Moreover, researchers might use a combination of available techniques such as bootstrapping to estimate the efficiency of schools over a longer period of time and after the economic crisis. Originality/value: To the best of our knowledge, this is the first study of the aforementioned issue for a region on the European Unionâ€™s periphery one year before and one year during the economic crisis. The results are expected to provide insightful information for policymakers in order to better understand the performance of the schools and seek more appropriate solutions aiming at moving the sector forward. The proposal of this study is the establishment of an Observatory, authored by the Greek Ministry of Education, monitoring the diachronic data and measurements of the schoolsâ€™ efficiency.
I Want to be Healthy: How Belief Influence the Usage Intention of Mobile-health Application

Mobile-health application has a great potential to intervene unhealthy life-style. However, the adoption of mobile-health application is still low compared to other types of mobile application. There were several previous studies discussing the usage behavior of mobilehealth applications in various perspective. However, there is still few discussing the usage behavior from the intrinsic factors of human, one of which is belief. This study aims to determine the impact of belief consisting of attitudinal, health, and social belief on the intention to adopt mobile-health application. This study used quantitative methodology and Partial Least Square Structural Equation Modeling (PLS-SEM) with SmartPLS 3.0 software to analyze the data. There were 663 respondents from the online questionnaire distributed. This study found that relative advantage, compatibility, health threat, and social influence affect user’s intention to use mobile health application with relative advantage as the most significant.
Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data

Robotic systems that rely primarily on self-supervised learning have the potential to decrease the amount of human annotation and engineering effort required to learn control strategies. In the same way that prior robotic systems have leveraged self-supervised techniques from computer vision (CV) and natural language processing (NLP), our work builds on prior work showing that the reinforcement learning (RL) itself can be cast as a self-supervised problem: learning to reach any goal without human-specified rewards or labels. Despite the seeming appeal, little (if any) prior work has demonstrated how self-supervised RL methods can be practically deployed on robotic systems. By first studying a challenging simulated version of this task, we discover design decisions about architectures and hyperparameters that increase the success rate by $2 \times$. These findings lay the groundwork for our main result: we demonstrate that a self-supervised RL algorithm based on contrastive learning can solve real-world, image-based robotic manipulation tasks, with tasks being specified by a single goal image provided after training.
Cluster structure on genus 2 spherical DAHA: seven-colored flower

We construct an embedding of the Arthamonov-Shakirov algebra of genus 2 knot operators into the quantized coordinate ring of the cluster Poisson variety of exceptional finite mutation type $X_7$. The embedding is equivariant with respect to the action of the mapping class group of the closed surface of genus 2. The cluster realization of the mapping class group action leads to a formula for the coefficient of each monomial in the genus 2 Macdonald polynomial of type $A_1$ as sum over lattice points in a convex polyhedron in 7-dimensional space.
On commutators of square-zero Hilbert space operators

Let $\mathcal{H}$ be a complex, separable Hilbert space, and set $\mathfrak{c}($NIL$_2)=\{ MN - NM : N, M \in \mathcal{B}(\mathcal{H}), M^2 = 0 = N^2 \}$. When $\dim\, \mathcal{H}$ is finite, we characterise the set $\mathfrak{c}($NIL$_2)$ and its norm-closure CLOS$(\mathfrak{c}($NIL$_2))$. In the infinite-dimensional setting, we characterise the intersection of CLOS$(\mathfrak{c}($NIL$_2))$ with the set of biquasitriangular operators, and we exhibit an index obstruction to belonging to CLOS$(\mathfrak{c}($NIL$_2))$.
Displaced Drude peak from $\pi$-ton vertex corrections

Correlated electron systems often show strong bosonic fluctuations, e.g., of antiferromagnetic nature, around a large wave vector such as $\mathbf{q}=(\pi,\pi\ldots)$. These fluctuations can give rise to vertex corrections to the optical conductivity through the (transversal) particle-hole channel, coined $\pi$-ton contributions. Previous numerical results differed qualitatively on how such vertex corrections alter the optical conductivity. Here, we clarify that $\pi$-ton vertex corrections lead to a displaced Drude peak for correlated metals. The proximity and enhancement of the effect when approaching a phase transition of, e.g., antiferromagnetic nature can be utilized for discriminating $\pi$-tons in experiments from other physics leading to a displaced Drude peak.
Fair Resource Allocation in Multi-Task Learning

By jointly learning multiple tasks, multi-task learning (MTL) can leverage the shared knowledge across tasks, resulting in improved data efficiency and generalization performance. However, a major challenge in MTL lies in the presence of conflicting gradients, which can hinder the fair optimization of some tasks and subsequently impede MTL's ability to achieve better overall performance. Inspired by fair resource allocation in communication networks, we formulate the optimization of MTL as a utility maximization problem, where the loss decreases across tasks are maximized under different fairness measurements. To solve this problem, we propose FairGrad, a novel MTL optimization method. FairGrad not only enables flexible emphasis on certain tasks but also achieves a theoretical convergence guarantee. Extensive experiments demonstrate that our method can achieve state-of-the-art performance among gradient manipulation methods on a suite of multi-task benchmarks in supervised learning and reinforcement learning. Furthermore, we incorporate the idea of $\alpha$-fairness into loss functions of various MTL methods. Extensive empirical studies demonstrate that their performance can be significantly enhanced. Code is provided at \url{https://github.com/OptMN-Lab/fairgrad}.
MATHWELL: Generating Educational Math Word Problems at Scale

Math word problems are critical K-8 educational tools, but writing them is time-consuming and requires domain expertise. We suggest that language models can support K-8 math education by automatically generating problems at scale. To be educational, generated problems must be 1) solvable, 2) accurate, and 3) appropriate. Existing datasets are unlabeled for these criteria, making them ill-suited for training problem generators. We introduce MATHWELL, a Llama-2 (70B) model iteratively finetuned to generate K-8 math word problems using data from expert annotation. Using MATHWELL, we generate the largest English word problem dataset with Program of Thought (PoT) rationales to date, containing 20,490 problems. 3,484 are scored by domain experts who find MATHWELL has a 40% higher share of problems that have executable solutions and meet all criteria than alternatives, with 74% of its problems with executable solutions being solvable, accurate, and appropriate. We release our model, data, and annotations.
Topic-to-essay generation with knowledge-based content selection

The topic-to-essay generation task is a challenging natural language generation task that aims to generate paragraph-level text with high semantic coherence based on a given set of topic words. Previous work has focused on the introduction of external knowledge, ignoring the insufficient generated text diversity. In order to improve the generation diversity, we propose a novel copy mechanism model with a content selection module that integrates rich semantic knowledge from the language model into the decoder. Furthermore, we introduce the improved prefix tuning method to train the model, enabling it to adapt to varying input complexities. In addition, we have contributed a new Chinese dataset for TEG tasks. Experimental results demonstrate that the proposed model can improve the generated text diversity by 35\% to 59\% compared to the state-of-the-art method, while maintaining a high level of topic consistency.
Mass formulas for local Galois representations ( after Serre , Bhargava ) ( with an appendix by Daniel Gulotta )

Bhargava has given a formula, derived from a formula of Serre, computing a certain count of extensions of a local field, weighted by conductor and by number of automorphisms. We interpret this result as a counting formula for permutation representations of the absolute Galois group of the local field, then speculate on variants of this formula in which the role of the symmetric group is played by other groups. We prove an analogue of Bhargava’s formula for representations into a Weyl group in the Bn series, which suggests a possible link with integration on p-adic groups. We also obtain positive results in some other cases, and some negative results in residual characteristic 2.
Semi-supervised Open-World Object Detection

Conventional open-world object detection (OWOD) problem setting first distinguishes known and unknown classes and then later incrementally learns the unknown objects when introduced with labels in the subsequent tasks. However, the current OWOD formulation heavily relies on the external human oracle for knowledge input during the incremental learning stages. Such reliance on run-time makes this formulation less realistic in a real-world deployment. To address this, we introduce a more realistic formulation, named semi-supervised open-world detection (SS-OWOD), that reduces the annotation cost by casting the incremental learning stages of OWOD in a semi-supervised manner. We demonstrate that the performance of the state-of-the-art OWOD detector dramatically deteriorates in the proposed SS-OWOD setting. Therefore, we introduce a novel SS-OWOD detector, named SS-OWFormer, that utilizes a feature-alignment scheme to better align the object query representations between the original and augmented images to leverage the large unlabeled and few labeled data. We further introduce a pseudo-labeling scheme for unknown detection that exploits the inherent capability of decoder object queries to capture object-specific information. On the COCO dataset, our SS-OWFormer using only 50% of the labeled data achieves detection performance that is on par with the state-of-the-art (SOTA) OWOD detector using all the 100% of labeled data. Further, our SS-OWFormer achieves an absolute gain of 4.8% in unknown recall over the SOTA OWOD detector. Lastly, we demonstrate the effectiveness of our SS-OWOD problem setting and approach for remote sensing object detection, proposing carefully curated splits and baseline performance evaluations. Our experiments on 4 datasets including MS COCO, PASCAL, Objects365 and DOTA demonstrate the effectiveness of our approach. Our source code, models and splits are available here https://github.com/sahalshajim/SS-OWFormer
Problems on Group-labeled Matroid Bases

Consider a matroid equipped with a labeling of its ground set to an abelian group. We define the label of a subset of the ground set as the sum of the labels of its elements. We study a collection of problems on finding bases and common bases of matroids with restrictions on their labels. For zero bases and zero common bases, the results are mostly negative. While finding a non-zero basis of a matroid is not difficult, it turns out that the complexity of finding a non-zero common basis depends on the group. Namely, we show that the problem is hard for a fixed group if it contains an element of order two, otherwise it is polynomially solvable. As a generalization of both zero and non-zero constraints, we further study $F$-avoiding constraints where we seek a basis or common basis whose label is not in a given set $F$ of forbidden labels. Using algebraic techniques, we give a randomized algorithm for finding an $F$-avoiding common basis of two matroids represented over the same field for finite groups given as operation tables. The study of $F$-avoiding bases with groups given as oracles leads to a conjecture stating that whenever an $F$-avoiding basis exists, an $F$-avoiding basis can be obtained from an arbitrary basis by exchanging at most $|F|$ elements. We prove the conjecture for the special cases when $|F|\le 2$ or the group is ordered. By relying on structural observations on matroids representable over fixed, finite fields, we verify a relaxed version of the conjecture for these matroids. As a consequence, we obtain a polynomial-time algorithm in these special cases for finding an $F$-avoiding basis when $|F|$ is fixed.
In-Depth Search for a Coupling between Gravity and Electromagnetism with Steady Fields

Any means to control gravity like electromagnetism is currently out of reach by many orders of magnitude even under extreme laboratory conditions. Some often poorly executed experiments or pseudoscience theories appear from time to time claiming for example anomalous forces from capacitors that suggest a connection between the two fields. We developed novel and high resolution horizontal-, vertical- and rotation-balances that allow to test electric devices completely shielded and remotely controlled under high vacuum conditions to perform the first in-depth search for such a coupling using steady fields. Our testing included a variety of capacitors of different shapes and compositions as well as for the first-time solenoids and tunneling currents from Zener diodes and varistors. A comprehensive coupling-scheme table was used to test almost all combinations including capacitors and solenoids with permittivity and permeability gradients as well as capacitors and varistors within crossed magnetic fields. We also tested a crossed-coil producing helical magnetic field lines as well as interactions between a pair of shielded toroidal coils to look for proposed extensions to Maxwell's equations. No anomalous forces or torques down to the Nanonewton or Nanonewton-Meter range were found providing new limits many orders of magnitude below previous assessments ruling out claims or theories and providing a basis for future research on the topic.
System size scaling of triangularity effects on global temperature gradient-driven gyrokinetic simulations

In this work, we explore the triangularity effects on turbulent transport employing global gyrokinetic simulations performed with the ORB5 code. Numerous experiments on the Tokamak \`a Configuration Variable (TCV) and, more recently, on the DIII-D machine, have demonstrated superior confinement properties in L-mode of negative triangularity (NT) over positive triangularity (PT) configuration. This presents a particularly attractive scenario, as L-mode operation eliminates or significantly mitigates the presence of hazardous edge-localized modes (ELMs). However, a full theoretical understanding of all these observations remains elusive. Specifically, questions remain about how NT improvements can extend to the core where triangularity is very small, and whether these improvements can scale to larger devices. This paper addresses these two questions. Our analysis is divided into two parts: we first demonstrate that the confinement enhancement in NT configurations arises from the interdependent edge-core dynamics, and then we present the results of a system size scan. Crucially, we find that the relative turbulent transport reduction of NT over PT appears not to be contingent on machine dimensions or fluctuation scales and is moreover robust with respect to variations in plasma profiles. This insight underscores the fundamental nature of the NT confinement advantage and paves the way for its potential application in future fusion devices, regardless of their size.
Distilling Adversarial Robustness Using Heterogeneous Teachers

Achieving resiliency against adversarial attacks is necessary prior to deploying neural network classifiers in domains where misclassification incurs substantial costs, e.g., self-driving cars or medical imaging. Recent work has demonstrated that robustness can be transferred from an adversarially trained teacher to a student model using knowledge distillation. However, current methods perform distillation using a single adversarial and vanilla teacher and consider homogeneous architectures (i.e., residual networks) that are susceptible to misclassify examples from similar adversarial subspaces. In this work, we develop a defense framework against adversarial attacks by distilling adversarial robustness using heterogeneous teachers (DARHT). In DARHT, the student model explicitly represents teacher logits in a student-teacher feature map and leverages multiple teachers that exhibit low adversarial example transferability (i.e., exhibit high performance on dissimilar adversarial examples). Experiments on classification tasks in both white-box and black-box scenarios demonstrate that DARHT achieves state-of-the-art clean and robust accuracies when compared to competing adversarial training and distillation methods in the CIFAR-10, CIFAR-100, and Tiny ImageNet datasets. Comparisons with homogeneous and heterogeneous teacher sets suggest that leveraging teachers with low adversarial example transferability increases student model robustness.
Key Design Choices in Source-Free Unsupervised Domain Adaptation: An In-depth Empirical Analysis

This study provides a comprehensive benchmark framework for Source-Free Unsupervised Domain Adaptation (SF-UDA) in image classification, aiming to achieve a rigorous empirical understanding of the complex relationships between multiple key design factors in SF-UDA methods. The study empirically examines a diverse set of SF-UDA techniques, assessing their consistency across datasets, sensitivity to specific hyperparameters, and applicability across different families of backbone architectures. Moreover, it exhaustively evaluates pre-training datasets and strategies, particularly focusing on both supervised and self-supervised methods, as well as the impact of fine-tuning on the source domain. Our analysis also highlights gaps in existing benchmark practices, guiding SF-UDA research towards more effective and general approaches. It emphasizes the importance of backbone architecture and pre-training dataset selection on SF-UDA performance, serving as an essential reference and providing key insights. Lastly, we release the source code of our experimental framework. This facilitates the construction, training, and testing of SF-UDA methods, enabling systematic large-scale experimental analysis and supporting further research efforts in this field.
Towards Efficient Active Learning in NLP via Pretrained Representations

Fine-tuning Large Language Models (LLMs) is now a common approach for text classification in a wide range of applications. When labeled documents are scarce, active learning helps save annotation efforts but requires retraining of massive models on each acquisition iteration. We drastically expedite this process by using pretrained representations of LLMs within the active learning loop and, once the desired amount of labeled data is acquired, fine-tuning that or even a different pretrained LLM on this labeled data to achieve the best performance. As verified on common text classification benchmarks with pretrained BERT and RoBERTa as the backbone, our strategy yields similar performance to fine-tuning all the way through the active learning loop but is orders of magnitude less computationally expensive. The data acquired with our procedure generalizes across pretrained networks, allowing flexibility in choosing the final model or updating it as newer versions get released.
The Hilbert series of the preprojective algebras

The aim of this short note is to prove the formula of the Hilbert series of the preprojective algebras in arbitrary characteristic by making effective use of the formulas of the Hilbert series of differential graded (dg) algebras with Adams grading. We also compute the Hilbert series of the quiver Heisenberg algebras, a special class of central extensions of preprojective algebras.
Sparse MeZO: Less Parameters for Better Performance in Zeroth-Order LLM Fine-Tuning

While fine-tuning large language models (LLMs) for specific tasks often yields impressive results, it comes at the cost of memory inefficiency due to back-propagation in gradient-based training. Memory-efficient Zeroth-order (MeZO) optimizers, recently proposed to address this issue, only require forward passes during training, making them more memory-friendly. However, the quality of gradient estimates in zeroth order optimization often depends on the data dimensionality, potentially explaining why MeZO still exhibits significant performance drops compared to standard fine-tuning across various tasks. Inspired by the success of Parameter-Efficient Fine-Tuning (PEFT), this paper introduces Sparse MeZO, a novel memory-efficient zeroth-order optimization approach that applies ZO only to a carefully chosen subset of parameters. We propose a simple yet effective parameter selection scheme that yields significant performance gains with Sparse-MeZO. Additionally, we develop a memory-optimized implementation for sparse masking, ensuring the algorithm requires only inference-level memory consumption, allowing Sparse-MeZO to fine-tune LLaMA-30b on a single A100 GPU. Experimental results illustrate that Sparse-MeZO consistently improves both performance and convergence speed over MeZO without any overhead. For example, it achieves a 9\% absolute accuracy improvement and 3.5x speedup over MeZO on the RTE task.