Miracle-dz/UniFAR-FTUs huggingface dataset:
Equitable and Optimal Transport with Multiple Agents

We introduce an extension of the Optimal Transport problem when multiple costs are involved. Considering each cost as an agent, we aim to share equally between agents the work of transporting one distribution to another. To do so, we minimize the transportation cost of the agent who works the most. Another point of view is when the goal is to partition equitably goods between agents according to their heterogeneous preferences. Here we aim to maximize the utility of the least advantaged agent. This is a fair division problem. Like Optimal Transport, the problem can be cast as a linear optimization problem. When there is only one agent, we recover the Optimal Transport problem. When two agents are considered, we are able to recover Integral Probability Metrics defined by $\alpha$-Holder functions, which include the widely-known Dudley metric. To the best of our knowledge, this is the first time a link is given between the Dudley metric and Optimal Transport. We provide an entropic regularization of that problem which leads to an alternative algorithm faster than the standard linear program.
The Mathematical Foundations of Manifold Learning

Manifold learning is a popular and quickly-growing subfield of machine learning based on the assumption that one's observed data lie on a low-dimensional manifold embedded in a higher-dimensional space. This thesis presents a mathematical perspective on manifold learning, delving into the intersection of kernel learning, spectral graph theory, and differential geometry. Emphasis is placed on the remarkable interplay between graphs and manifolds, which forms the foundation for the widely-used technique of manifold regularization. This work is written to be accessible to a broad mathematical audience, including machine learning researchers and practitioners interested in understanding the theorems underlying popular manifold learning algorithms and dimensionality reduction techniques.
Weak gravity on a $\Lambda$CDM background

We consider Horndeski modified gravity models obeying stability, velocity of gravitational waves $c_T$ equals $c$ and quasistatic approximation (QSA) on subhorizon scales. We assume further a $\Lambda$CDM background expansion and a monotonic evolution of the $\alpha$ functions as $\alpha_i= \alpha_{i0}~a^s$ where $i=M,B$, $a$ is the scale factor and $\alpha_{i0}$ ($\alpha_{M0}, \alpha_{B0}$), $s$ are arbitrary parameters. We show that the growth and lensing reduced (dimensionless) gravitational couplings $\mu\equiv G_{\rm growth}/G$, $\Sigma\equiv G_{\rm lensing}/G$ exhibit the following generic properties today: $\Sigma_0 1$ is favored for large $s$. We establish also the relation $\mu\geq \Sigma$ at all times. Taking into account the $f\sigma_8$ and $E_G$ data constrains the parameter $s$ to satisfy $s\lesssim 2$. Hence these data select essentially the weak gravity regime today ($\mu_0 1$ subsists only marginally for $s\approx 2$. We consider further the growth index $\gamma(z)$ and identify the $(\alpha_{M0},\alpha_{B0},s)$ parameter region that corresponds to specific signs of the differences $\gamma_0-\gamma_0^{\Lambda CDM}$, and $\gamma_1-\gamma_1^{\Lambda CDM}$, where $\gamma_0\equiv \gamma\bigl|_{z=0}$ and $\gamma_1\equiv \frac{{\rm d}\gamma}{\rm d z}\bigl|_{z=0}$. In this way important information is gained on the past evolution of $\mu$. We obtain in particular the signature $\gamma_0>\gamma_0^{\Lambda CDM}$ for $s<2$ in the selected weak gravity region.
Grain-boundary topological phase transitions

Significance We reveal the existence of a topological phase transition (Kosterlitz–Thouless type) in grain boundaries (GBs)—important internal surfaces in crystalline materials. GB dynamics are controlled by the formation/migration of line defects (disconnections) with dislocation and step character. Below the GB KT transition, disconnections of opposite signs are bound as pairs, while above it they unbind and proliferate. We demonstrate that GB KT transitions provide a fundamental understanding of many GB properties, the temporal evolution of microstructure, and how it deforms/degrades under stress. The formation and migration of disconnections (line defects constrained to the grain boundary [GB] plane with both dislocation and step character) control many of the kinetic and dynamical properties of GBs and the polycrystalline materials of which they are central constituents. We demonstrate that GBs undergo a finite-temperature topological phase transition of the Kosterlitz–Thouless (KT) type. This phase transition corresponds to the screening of long-range interactions between (and unbinding of) disconnections. This phase transition leads to abrupt changes in the behavior of GB migration, GB sliding, and roughening. We analyze this KT transition through mean-field theory, renormalization group theory, and kinetic Monte Carlo simulations and examine how this transition affects microstructure-scale phenomena such as grain growth stagnation, abnormal grain growth, and superplasticity.
Crossed Andreev reflection in topological insulator nanowire T junctions

We numerically study crossed Andreev reflection (CAR) in a topological insulator nanowire T-junction where one lead is proximitized by a superconductor. We perform realistic simulations based on the 3D BHZ model and compare the results with those from an effective 2D surface model, whose computational cost is much lower. Both approaches show that CAR should be clearly observable in a wide parameter range, including perfect CAR in a somewhat more restricted range. Furthermore, it can be controlled by a magnetic field and is robust to disorder. Our effective 2D implementation allows to model systems of micronsize, typical of experimental setups, but computationally too heavy for 3D models.
CUTIE: Beyond PetaOp/s/W Ternary DNN Inference Acceleration With Better-Than-Binary Energy Efficiency

We present a 3.1 POp/s/W fully digital hardware accelerator for ternary neural networks (TNNs). CUTIE, the completely unrolled ternary inference engine, focuses on minimizing noncomputational energy and switching activity so that dynamic power spent on storing (locally or globally) intermediate results is minimized. This is achieved by: 1) a data-path architecture completely unrolled in the feature map and filter dimensions to reduce switching activity by favoring silencing over iterative computation and maximizing data reuse; 2) targeting TNNs which, in contrast to binary NNs, allow for sparse weights that reduce switching activity; and 3) introducing an optimized training method for higher sparsity of the filter weights, resulting in a further reduction of the switching activity. Compared with state-of-the-art accelerators, CUTIE achieves greater or equal accuracy while decreasing the overall core inference energy cost by a factor of <inline-formula> <tex-math notation="LaTeX">$4.8\times $ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$21\times $ </tex-math></inline-formula>.
A Framework for Prediction and Storage of Battery Life in IoT Devices using DNN and Blockchain

As digitization increases, the need to automate various entities becomes crucial for development. The data generated by the IoT devices need to be processed accurately and in a secure manner. The basis for the success of such a scenario requires blockchain as a means of unalterable data storage to improve the overall security and trust in the system. By providing trust in an automated system, with real-time data updates to all stakeholders, an improved form of implementation takes the stage and can help reduce the stress of adaptability to complete automated systems. This research focuses on a use case with respect to the real time Internet of Things (IoT) network which is deployed at the beach of Chicago Park District. This real time data which is collected from various sensors is then used to design a predictive model using Deep Neural Networks for estimating the battery life of IoT sensors that is deployed at the beach. This proposed model could help the government to plan for placing orders of replaceable batteries before time so that there can be an uninterrupted service. Since this data is sensitive and requires to be secured, the predicted battery life value is stored in blockchain which would be a tamper-proof record of the data.
Dynamic latency speech recognition with asynchronous revision

In this work we propose an inference technique, asynchronous revision, to unify streaming and non-streaming speech recognition models. Specifically, we achieve dynamic latency with only one model by using arbitrary right context during inference. The model is composed of a stack of convolutional layers for audio encoding. In inference stage, the history states of encoder and decoder can be asynchronously revised to trade off between the latency and the accuracy of the model. To alleviate training and inference mismatch, we propose a training technique, segment cropping, which randomly splits input utterances into several segments with forward connections. This allows us to have dynamic latency speech recognition results with large improvements in accuracy. Experiments show that our dynamic latency model with asynchronous revision gives 8\%-14\% relative improvements over the streaming models.
Compressed Sensing With Upscaled Vector Approximate Message Passing

The Recently proposed Vector Approximate Message Passing (VAMP) algorithm demonstrates a great reconstruction potential at solving compressed sensing related linear inverse problems. VAMP provides high per-iteration improvement, can utilize powerful denoisers like BM3D, has rigorously defined dynamics and is able to recover signals measured by highly undersampled and ill-conditioned linear operators. Yet, its applicability is limited to relatively small problem sizes due to the necessity to compute the expensive LMMSE estimator at each iteration. In this work we consider the problem of upscaling VAMP by utilizing Conjugate Gradient (CG) to approximate the intractable LMMSE estimator. We propose a rigorous method for correcting and tuning CG withing CG-VAMP to achieve a stable and efficient reconstruction. To further improve the performance of CG-VAMP, we design a warm-starting scheme for CG and develop theoretical models for the Onsager correction and the State Evolution of Warm-Started CG-VAMP (WS-CG-VAMP). Additionally, we develop robust and accurate methods for implementing the WS-CG-VAMP algorithm. The numerical experiments on large-scale image reconstruction problems demonstrate that WS-CG-VAMP requires much fewer CG iterations compared to CG-VAMP to achieve the same or superior level of reconstruction.
Pulsar glitches in a strangeon star model. II. The activity

Glitch is supposed to be a useful probe into pulsar's interior, but the underlying physics remains puzzling. The glitch activity may reflect a lower limit of the crustal moment of inertia in conventional neutron star models. Nevertheless, its statistical feature could also be reproduced in the strangeon star model, which is focused here. We formulate the glitch activity of normal radio pulsars under the framework of starquake of solid strangeon star model, the shear modulus of strangeon matter is constrained to be $\mu\simeq 3\times10^{34}~\rm erg/cm^{3}$, consistent with previous work. Nevertheless, about ten times the shift in oblateness accumulated during glitch interval is needed to fulfill the statistical observations. The fact that typical glitch sizes of two rapidly evolving pulsars (the Crab pulsar and PSR B0540-69) are about two orders of magnitude lower than that of the Vela pulsar, significantly lower than the oblateness change they can supply, indicates probably that only a part of oblateness change is relieved when a pulsar is young. The unreleased oblateness and stress may relax as compensation in the following evolution. The small glitch sizes and low glitch activity of the Crab pulsar can be explained simultaneously in this phenomenological model. Finally, we obtain energy release to be $\Delta E\sim 2.4\times 10^{40}~\rm erg$ and $\Delta E\sim 4.2\times 10^{41}~\rm erg$ for typical glitch size of $\Delta\nu/\nu\sim 10^{-6}$ (Vela-like) and $\sim 10^{-8}$ (Crab-like). The upcoming SKA may test this model through the energy release and the power-law relation between the reduced recovery coefficient $Q/|\dot\nu|^{1/2}$ and $\Delta\nu/\nu$.
A framework for causal inference in the presence of extreme inverse probability weights: the role of overlap weights

In this paper, we consider recent progress in estimating the average treatment effect when extreme inverse probability weights are present and focus on methods that account for a possible violation of the positivity assumption. These methods aim at estimating the treatment effect on the subpopulation of patients for whom there is a clinical equipoise. We propose a systematic approach to determine their related causal estimands and develop new insights into the properties of the weights targeting such a subpopulation. Then, we examine the roles of overlap weights, matching weights, Shannon's entropy weights, and beta weights. This helps us characterize and compare their underlying estimators, analytically and via simulations, in terms of the accuracy, precision, and root mean squared error. Moreover, we study the asymptotic behaviors of their augmented estimators (that mimic doubly robust estimators), which lead to improved estimations when either the propensity or the regression models are correctly specified. Based on the analytical and simulation results, we conclude that overall overlap weights are preferable to matching weights, especially when there is moderate or extreme violations of the positivity assumption. Finally, we illustrate the methods using a real data example marked by extreme inverse probability weights.
Creating a Domain-diverse Corpus for Theory-based Argument Quality Assessment

Computational models of argument quality (AQ) have focused primarily on assessing the overall quality or just one specific characteristic of an argument, such as its convincingness or its clarity. However, previous work has claimed that assessment based on theoretical dimensions of argumentation could benefit writers, but developing such models has been limited by the lack of annotated data. In this work, we describe GAQCorpus, the first large, domain-diverse annotated corpus of theory-based AQ. We discuss how we designed the annotation task to reliably collect a large number of judgments with crowdsourcing, formulating theory-based guidelines that helped make subjective judgments of AQ more objective. We demonstrate how to identify arguments and adapt the annotation task for three diverse domains. Our work will inform research on theory-based argumentation annotation and enable the creation of more diverse corpora to support computational AQ assessment.
Developing High Quality Training Samples for Deep Learning Based Local Climate Classification in Korea

Two out of three people will be living in urban areas by 2050, as projected by the United Nations, emphasizing the need for sustainable urban development and monitoring. Common urban footprint data provide high-resolution city extents but lack essential information on the distribution, pattern, and characteristics. The Local Climate Zone (LCZ) offers an efficient and standardized framework that can delineate the internal structure and characteristics of urban areas. Global-scale LCZ mapping has been explored, but are limited by low accuracy, variable labeling quality, or domain adaptation challenges. Instead, this study developed a custom LCZ data to map key Korean cities using a multi-scale convolutional neural network. Results demonstrated that using a novel, custom LCZ data with deep learning can generate more accurate LCZ map results compared to conventional community-based LCZ mapping with machine learning as well as transfer learning of the global So2Sat dataset.
Mitigating Backdoor Attacks in Federated Learning

Malicious clients can attack federated learning systems by using malicious data, including backdoor samples, during the training phase. The compromised global model will perform well on the validation dataset designed for the task. However, a small subset of data with backdoor patterns may trigger the model to make a wrong prediction. Previously, there was an arms race. Attackers tried to conceal attacks and defenders tried to detect attacks during the aggregation stage of training on the server-side in a federated learning system. In this work, we propose a new method to mitigate backdoor attacks after the training phase. Specifically, we designed a federated pruning method to remove redundant neurons in the network and then adjust the model's extreme weight values. Experiments conducted on distributed Fashion-MNIST have shown that our method can reduce the average attack success rate from 99.7% to 1.9% with a 5.5% loss of test accuracy on the validation dataset. To minimize the pruning influence on test accuracy, we can fine-tune after pruning, and the attack success rate drops to 6.4%, with only a 1.7% loss of test accuracy.
Wheat Crop Yield Prediction Using Deep LSTM Model

An in-season early crop yield forecast before harvest can benefit the farmers to improve the production and enable various agencies to devise plans accordingly. We introduce a reliable and inexpensive method to predict crop yields from publicly available satellite imagery. The proposed method works directly on raw satellite imagery without the need to extract any hand-crafted features or perform dimensionality reduction on the images. The approach implicitly models the relevance of the different steps in the growing season and the various bands in the satellite imagery. We evaluate the proposed approach on tehsil (block) level wheat predictions across several states in India and demonstrate that it outperforms existing methods by over 50\%. We also show that incorporating additional contextual information such as the location of farmlands, water bodies, and urban areas helps in improving the yield estimates.
Reinforcement Learning for Test Case Prioritization

Continuous Integration (CI) significantly reduces integration problems, speeds up development time, and shortens release time. However, it also introduces new challenges for quality assurance activities, including regression testing, which is the focus of this work. Though various approaches for test case prioritization have shown to be very promising in the context of regression testing, specific techniques must be designed to deal with the dynamic nature and timing constraints of CI. Recently, Reinforcement Learning (RL) has shown great potential in various challenging scenarios that require continuous adaptation, such as game playing, real-time ads bidding, and recommender systems. Inspired by this line of work and building on initial efforts in supporting test case prioritization with RL techniques, we perform here a comprehensive investigation of RL-based test case prioritization in a CI context. To this end, taking test case prioritization as a ranking problem, we model the sequential interactions between the CI environment and a test case prioritization agent as an RL problem, using three alternative ranking models. We then rely on carefully selected and tailored state-of-the-art RL techniques to automatically and continuously learn a test case prioritization strategy, whose objective is to be as close as possible to the optimal one. Our extensive experimental analysis shows that the best RL solutions provide a significant accuracy improvement over previous RL-based work, with prioritization strategies getting close to being optimal, thus paving the way for using RL to prioritize test cases in a CI context.
Semi-Supervised Cleansing of Web Argument Corpora

Debate portals and similar web platforms constitute one of the main text sources in computational argumentation research and its applications. While the corpora built upon these sources are rich of argumentatively relevant content and structure, they also include text that is irrelevant, or even detrimental, to their purpose. In this paper, we present a precision-oriented approach to detecting such irrelevant text in a semi-supervised way. Given a few seed examples, the approach automatically learns basic lexical patterns of relevance and irrelevance and then incrementally bootstraps new patterns from sentences matching the patterns. In the existing args.me corpus with 400k argumentative texts, our approach detects almost 87k irrelevant sentences, at a precision of 0.97 according to manual evaluation. With low effort, the approach can be adapted to other web argument corpora, providing a generic way to improve corpus quality.
WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization

In the Query Focused Multi-Document Summarization (QF-MDS) task, a set of documents and a query are given where the goal is to generate a summary from these documents based on the given query. However, one major challenge for this task is the lack of availability of labeled training datasets. To overcome this issue, in this paper, we propose a novel weakly supervised learning approach via utilizing distant supervision. In particular, we use datasets similar to the target dataset as the training data where we leverage pre-trained sentence similarity models to generate the weak reference summary of each individual document in a document set from the multi-document gold reference summaries. Then, we iteratively train our summarization model on each single-document to alleviate the computational complexity issue that occurs while training neural summarization models in multiple documents (i.e., long sequences) at once. Experimental results on the Document Understanding Conferences (DUC) datasets show that our proposed approach sets a new state-of-the-art result in terms of various evaluation metrics.
The alternating presentation of $U_q(\widehat{gl_2})$ from Freidel-Maillet algebras

An infinite dimensional algebra denoted $\bar{\cal A}_q$ that is isomorphic to a central extension of $U_q^+$ - the positive part of $U_q(\widehat{sl_2})$ - has been recently proposed by Paul Terwilliger. It provides an `alternating' Poincar\'e-Birkhoff-Witt (PBW) basis besides the known Damiani's PBW basis built from positive root vectors. In this paper, a presentation of $\bar{\cal A}_q$ in terms of a Freidel-Maillet algebra is obtained. Using this presentation: (a) finite dimensional tensor product representations for $\bar{\cal A}_q$ are constructed; (b) explicit isomorphisms from $\bar{\cal A}_q$ to certain Drinfeld type `alternating' subalgebras of $U_q(\widehat{gl_2})$ are obtained; (c) the image in $U_q^+$ of all the generators of $\bar{\cal A}_q$ in terms of Damiani's root vectors is obtained. A new tensor product decomposition of $U_q(\widehat{sl_2})$ in terms of Drinfeld type `alternating' subalgebras follows. The specialization $q\rightarrow 1$ of $\bar{\cal A}_q$ is also introduced and studied in details. In this case, a presentation is given as a non-standard Yang-Baxter algebra. This paper is dedicated to Paul Terwilliger for his 65th birthday.
The Long, the Short and the Random

We furnish solid evidence, both theoretical and empirical, towards the existence of a deterministic algorithm for random sparse $\#\Omega(\log n)$-SAT instances, which computes the exact counting of satisfying assignments in sub-exponential time. The algorithm uses a nice combinatorial property that every CNF formula has, which relates its number of unsatisfying assignments to the space of its monotone sub-formulae.