Miracle-dz/UniFAR-FTUs huggingface dataset:
Search-in-the-Chain: Towards Accurate, Credible and Traceable Large Language Models for Knowledge-intensive Tasks

Making the contents generated by Large Language Model (LLM) such as ChatGPT, accurate, credible and traceable is crucial, especially in complex knowledge-intensive tasks that require multi-step reasoning and each of which needs knowledge to solve. Introducing Information Retrieval (IR) to provide LLM with external knowledge is good potential to solve this problem. However, where and how to introduce IR into LLM is a big challenge. Previous work has the disadvantage that the wrong knowledge retrieved by IR misleads the LLM or breaks the reasoning chain of LLM. In this paper, we propose a novel framework called Search-in-the-Chain (SearChain) for the interaction between LLM and IR to solve the challenges. First, LLM generates the global reasoning chain called Chain-of-Query (CoQ) where each node consists of an IR-oriented query and the answer to the query. Second, IR veriﬁes the answer of each node of CoQ, it corrects the answer that is not consistent with the retrieved information when IR gives high conﬁdence, which improves the credibility. Third, LLM can mark its missing knowledge in CoQ and IR can provide this knowledge to LLM. These three operations improve the accuracy of LLM for complex knowledge-intensive tasks in terms of reasoning ability and knowledge. Finally, SearChain generates the reasoning process and marks references to supporting documents for each reasoning step, which improves traceability. SearChain transforms the topology of reasoning from chain to tree, which can modify the reasoning direction. Experiment shows that SearChain outperforms
A MOBILIDADE FUNCIONAL DOS MEMBROS INFERIORES DE HEMIPARÉTICOS CRÔNICOS PODE MELHORAR APÓS 6 MESES DE CIRCUITO DE TREINAMENTO

O objetivo da reabilitacao apos um acidente vascular cerebral e melhorar o desempenho funcional de hemipareticos. A Fisioterapia em Grupo no formato de Circuit Training (FGCT) utiliza exercicios funcionais especificos com foco na repeticao e na progressao continua de exerciciost em um numero de estacoes de trabalho organizadas em um formato de circuito, para melhorar a capacidade funcional. O objetivo de estudo foi avaliar a performance da marcha de hemipareticos submetidos a seis meses de tratamento no (FGCT). Os hemipareticos foram avaliados com o teste de caminhada de 10 metros e Time get up test (TUG). Os resultados mostraram esta terapeutica nao foi capaz de alterar significativamente o desempenho do motor, no entanto, pode-se inferir que os pacientes tiveram a oportunidade de se beneficiar de exercicio e aumentar a sua participacao na sociedade melhorando aspectos psicossociais.
A Systematic Study of Knowledge Distillation for Natural Language Generation with Pseudo-Target Training

Modern Natural Language Generation (NLG) models come with massive computational and storage requirements. In this work, we study the potential of compressing them, which is crucial for real-world applications serving millions of users. We focus on Knowledge Distillation (KD) techniques, in which a small student model learns to imitate a large teacher model, allowing to transfer knowledge from the teacher to the student. In contrast to much of the previous work, our goal is to optimize the model for a specific NLG task and a specific dataset. Typically in real-world applications, in addition to labeled data there is abundant unlabeled task-specific data, which is crucial for attaining high compression rates via KD. In this work, we conduct a systematic study of task-specific KD techniques for various NLG tasks under realistic assumptions. We discuss the special characteristics of NLG distillation and particularly the exposure bias problem. Following, we derive a family of Pseudo-Target (PT) augmentation methods, substantially extending prior work on sequence-level KD. We propose the Joint-Teaching method, which applies word-level KD to multiple PTs generated by both the teacher and the student.Finally, we validate our findings in an extreme setup with no labeled examples using GPT-4 as the teacher. Our study provides practical model design observations and demonstrates the effectiveness of PT training for task-specific KD in NLG.
Thermodynamic instabilities of Kerr-Newman Ads black holes

In~\cite{Johnson:2019mdp}, Clifford put forward that the specific heat at constant volume $C_{V}$ is always negetive for the super-entropy black hole. Later in~\cite{Cong:2019bud}, Robert et al. found $C_{V}>0$ in certain region for the generalized exotic BTZ black holes. Futhermore, they proposed a new conjecture that as the $C_{V}>0$, the specific heat at constant pressure $C_{P}$ always satisfies $C_{P}<0$ in super-entropy black hole. In this paper, we examine the both conjectures with regard to the thermodynamic instability of super-entropy black hole. By means of a super-entropy black hole, the Kerr-Newman-AdS black hole in a coordinate system that rotates at infinity, we detailedly analyze its character and notice there exists the region where $C_{V}>0$ and meanwhile $C_{P}>0$. Hence, we find a counterexample to the two conjectures.
Low-complexity subspace-descent over symmetric positive definite manifold

This work puts forth low-complexity Riemannian subspace descent algorithms for the minimization of functions over the symmetric positive definite (SPD) manifold. Different from the existing Riemannian gradient descent variants, the proposed approach utilizes carefully chosen subspaces that allow the update to be written as a product of the Cholesky factor of the iterate and a sparse matrix. The resulting updates avoid the costly matrix operations like matrix exponentiation and dense matrix multiplication, which are generally required in almost all other Riemannian optimization algorithms on SPD manifold. We further identify a broad class of functions, arising in diverse applications, such as kernel matrix learning, covariance estimation of Gaussian distributions, maximum likelihood parameter estimation of elliptically contoured distributions, and parameter estimation in Gaussian mixture model problems, over which the Riemannian gradients can be calculated efficiently. The proposed uni-directional and multi-directional Riemannian subspace descent variants incur per-iteration complexities of $O(n)$ and $O(n^2)$ respectively, as compared to the $O(n^3)$ or higher complexity incurred by all existing Riemannian gradient descent variants. The superior runtime and low per-iteration complexity of the proposed algorithms is also demonstrated via numerical tests on large-scale covariance estimation and matrix square root problems. MATLAB code implementation is publicly available on GitHub : https://github.com/yogeshd-iitk/subspace_descent_over_SPD_manifold
Synchronization of silicon thermal-free-carrier oscillators

Recent exploration of collective phenomena in oscillator arrays has highlighted its potential for accessing a range of physical phenomena, from fundamental quantum many-body dynamics to the solution of practical optimization problems using photonic Ising machines. Spontaneous oscillations often arise in these oscillator arrays as an imbalance between gain and loss. Due to coupling between array individuals, the spontaneous oscillation is constrained and lead to interesting collective behavior, such as synchronized oscillations in optomechanical oscillator arrays, ferromagnetic-like coupling in delay-coupled optical parametric oscillators and binary phase states in coupled laser arrays. A key aspect of arrays is not only the coupling between its individuals but also their compliance towards neighbor stimuli. One self-sustaining photonic oscillator that can be readily implemented in a scalable foundry-based technology is based on the interaction of free-carriers, temperature and optical field of a resonant silicon photonic microcavity. Here we demonstrate that these silicon thermal-free-carrier oscillators are extremely compliant to external excitation and can be synchronized up to their 16$^\text{th}$ harmonic using a weak seed. Exploring this unprecedented compliance to external stimuli, we also demonstrate robust synchronization between two thermal free carrier oscillators.
KEPLET: Knowledge-Enhanced Pretrained Language Model with Topic Entity Awareness

In recent years, Pre-trained Language Models (PLMs) have shown their superiority by pre-training on unstructured text corpus and then fine-tuning on downstream tasks. On entity-rich textual resources like Wikipedia, Knowledge-Enhanced PLMs (KEPLMs) incorporate the interactions between tokens and mentioned entities in pre-training, and are thus more effective on entity-centric tasks such as entity linking and relation classification. Although exploiting Wikipedia's rich structures to some extent, conventional KEPLMs still neglect a unique layout of the corpus where each Wikipedia page is around a topic entity (identified by the page URL and shown in the page title). In this paper, we demonstrate that KEPLMs without incorporating the topic entities will lead to insufficient entity interaction and biased (relation) word semantics. We thus propose KEPLET, a novel Knowledge-Enhanced Pre-trained LanguagE model with Topic entity awareness. In an end-to-end manner, KEPLET identifies where to add the topic entity's information in a Wikipedia sentence, fuses such information into token and mentioned entities representations, and supervises the network learning, through which it takes topic entities back into consideration. Experiments demonstrated the generality and superiority of KEPLET which was applied to two representative KEPLMs, achieving significant improvements on four entity-centric tasks.
A note on asymmetric hypergraphs

A $k$-graph $\mathcal{G}$ is asymmetric if there does not exist an automorphism on $\mathcal{G}$ other than the identity, and $\mathcal{G}$ is called minimal asymmetric if it is asymmetric but every non-trivial induced sub-hypergraph of $\mathcal{G}$ is non-asymmetric. Extending a result of Jiang and Ne\v{s}et\v{r}il, we show that for every $k$-graph, $k\ge3$, there exist infinitely many minimal asymmetric $k$-graphs which have maximum degree $2$ and are linear. Further, we show that there are infinitely many $2$-regular asymmetric $k$-graphs for $k\ge3$.
Simple, but not simplified: A new approach for optimising beyond-Standard Model physics searches at the Large Hadron Collider

Searches for beyond-Standard Model physics scenarios, such as supersymmetry (SUSY), at the Large Hadron Collider (LHC) are frequently optimised on simplified models. After assuming particular particle production and decay processes, analyses are optimised by tuning event selections on benchmark models generated in 2D planes of parameters, with all other parameters held fixed. Motivated by recent evidence that this removes sensitivity to a large volume of viable SUSY models, we propose an alternative approach based on dimensional reduction of global fit results. Starting from the results of a global fit of the 4D electroweak minimal supersymmetric standard model performed by the GAMBIT collaboration, we show how to define a 2D plane by using a variational autoencoder to map points in the original 4D parameter space to a 2D latent space. This allows for easy visualisation of the 4D global fit results, which generates insights into what we may be missing at the LHC. Furthermore, the invertible nature of the map to the 2D plane allows experimentalists to choose and simulate benchmark models in a 2D plane (as they could for a simplified model) whilst still accessing the full range of phenomenological scenarios identified by the global fit in the 4D model. We provide a demonstration of how our visualisation and benchmark model simulation process works, and develop an analysis that is able to exclude four benchmark models not excluded by the ATLAS and CMS collaborations in the set of results used for the global fit.
SeqAug: Sequential Feature Resampling as a modality agnostic augmentation method

Data augmentation is a prevalent technique for improving performance in various machine learning applications. We propose SeqAug, a modality-agnostic augmentation method that is tailored towards sequences of extracted features. The core idea of SeqAug is to augment the sequence by resampling from the underlying feature distribution. Resampling is performed by randomly selecting feature dimensions and permuting them along the temporal axis. Experiments on CMU-MOSEI verify that SeqAug is modality agnostic; it can be successfully applied to a single modality or multiple modalities. We further verify its compatibility with both recurrent and transformer architectures, and also demonstrate comparable to state-of-the-art results.
A New Sample of Transient Ultraluminous X-Ray Sources Serendipitously Discovered by Swift/XRT

Ultraluminous X-ray sources (ULXs) are our best laboratories for studying extreme super-Eddington accretion. Most studies of these objects are of relatively persistent sources; however, there is growing evidence to suggest a large fraction of these sources are transient. Here we present a sample of five newly reported transient ULXs in the galaxies NGC 4945, NGC 7793, and M81 serendipitously discovered in Swift/XRT observations. Swift monitoring of these sources have provided well-sampled lightcurves, allowing for us to model the lightcurves with the disk-instability model of Hameury & Lasota, which implies durations of 60–400 days and that the mass-accretion rate through the disk is close to or greater than the Eddington rate. Of the three source regions with prior Hubble Space Telescope imaging, color–magnitude diagrams of the potential stellar counterparts show varying ages of the possible stellar counterparts. Our estimation of the rates of these sources in these three galaxies is 0.4–1.3 yr−1. We find that, while persistent ULXs dominate the high end of galaxy luminosity functions, the number of systems that produce ULX luminosities are likely dominated by transient sources.
A Mediation Analysis of the Relationship Between Land Use Regulation Stringency and Employment Dynamics

The paper examines the effects of stringent land use regulations, measured using the Wharton Residential Land Use Regulatory Index (WRLURI), on employment growth during the period 2010-2020 in the Retail, Professional, and Information sectors across 878 local jurisdictions in the United States. All the local jurisdictions exist in both (2006 and 2018) waves of the WRLURI surveys and hence constitute a unique panel data. We apply a mediation analytical framework to decompose the direct and indirect effects of land use regulation stringency on sectoral employment growth and specialization. Our analysis suggests a fully mediated pattern in the relationship between excessive land use regulations and employment growth, with housing cost burden as the mediator. Specifically, a one standard deviation increase in the WRLURI index is associated with an approximate increase of 0.8 percentage point in the proportion of cost burdened renters. Relatedly, higher prevalence of cost-burdened renters has moderate adverse effects on employment growth in two sectors. A one percentage point increase in the proportion of cost burdened renters is associated with 0.04 and 0.017 percentage point decreases in the Professional and Information sectors, respectively.
Slow Kill for Big Data Learning

Big-data applications often involve a vast number of observations and features, creating new challenges for variable selection and parameter estimation. This paper presents a novel technique called “slow kill,” which utilizes nonconvex constrained optimization, adaptive $\ell _{2}$ -shrinkage, and increasing learning rates. The fact that the problem size can decrease during the slow kill iterations makes it particularly effective for large-scale variable screening. The interaction between statistics and optimization provides valuable insights into controlling quantiles, stepsize, and shrinkage parameters in order to relax the regularity conditions required to achieve the desired level of statistical accuracy. Experimental results on real and synthetic data show that slow kill outperforms state-of-the-art algorithms in various situations while being computationally efficient for large-scale data.
Class adaptive threshold and negative class guided noisy annotation robust Facial Expression Recognition

The hindering problem in facial expression recognition (FER) is the presence of inaccurate annotations referred to as noisy annotations in the datasets. These noisy annotations are present in the datasets inherently because the labeling is subjective to the annotator, clarity of the image, etc. Recent works use sample selection methods to solve this noisy annotation problem in FER. In our work, we use a dynamic adaptive threshold to separate confident samples from non-confident ones so that our learning won't be hampered due to non-confident samples. Instead of discarding the non-confident samples, we impose consistency in the negative classes of those non-confident samples to guide the model to learn better in the positive class. Since FER datasets usually come with 7 or 8 classes, we can correctly guess a negative class by 85% probability even by choosing randomly. By learning"which class a sample doesn't belong to", the model can learn"which class it belongs to"in a better manner. We demonstrate proposed framework's effectiveness using quantitative as well as qualitative results. Our method performs better than the baseline by a margin of 4% to 28% on RAFDB and 3.3% to 31.4% on FERPlus for various levels of synthetic noisy labels in the aforementioned datasets.
Update on pulmonary arteriovenous malformations

ABSTRACT This review aimed to provide an overview of pulmonary arteriovenous malformations, including the major clinical and radiological presentations, investigation, and treatment algorithm of the condition. The primary etiology of pulmonary arteriovenous malformations is hereditary hemorrhagic telangiectasia (HHT), also known as Rendu-Osler-Weber syndrome, with mutations in the ENG gene on chromosome 9 (HHT type 1) or in the ACVRL1/ALK1 complex (HHT type 2). Epistaxis should always be evaluated when repeated, when associated with anemia, and in some cases of hypoxemia. In the investigation, contrast echocardiography and chest CT are essential for evaluating this condition. Embolization is the best treatment choice, especially for correction in cases of hypoxemia or to avoid systemic infections. Finally, disease management was addressed in special conditions such as pregnancy. CT follow-up should be performed every 3-5 years, depending on the size of the afferent and efferent vessels, and antibiotic prophylactic care should always be oriented. Ultimately, knowledge of the disease by health professionals is a crucial point for the early diagnosis of these patients in clinical practice, which can potentially modify the natural course of the disease.
Opérateur de Collisions Électroniques Dans les Plasmas : Effets Relativistes

A > % & " . & ( . / ) RÉSUMÉ : Le spectre des raies répond aux diverses interactions entre l'émetteur et les perturbateurs qui ont précédé ou accompagné l'émission, par un élargissement et /ou un déplacement. La valeur moyenne de l'effet électronique est souvent remplacée par un opérateur de collision électronique. Dans ce travail, cet opérateur est calculé pour des raies isolées sans structure fine. La trajectoire de l'électron perturbateur est prise une hyperbole dont le centre de diffusion est l'ion émetteur. L'effet moyen de ces collisions, est calculé relativement aux paramètres d'impact et aux vitesses initiales selon la distribution de Maxwell des vitesses. Nous considérons alors le mouvement de l'électron autour de l'ion émetteur en se plaçant dans les conditions où l'électron perturbateur acquiert des vitesses excessives, c'est à dire relativistes. Nous avons obtenu, pour la première fois, un opérateur de collision relativiste. Nous avons dès lors étudié l'apport des effets relativistes relativement au cas non relativiste. Un ensemble de comparaison a été fait pour différentes valeurs de la densité électronique et du paramètre d'impact minimal, et de la température et il s'avère que cette dernière a un effet plus important aux hautes températures. MOTS-CLÉS : collision électronique, élargissement Stark, opérateur de collision électronique relativiste. ABSTRACT: The line shapes responds to the different interactions between the emitter and the perturbers which preceded or accompanied the emission, by a broadening and/or a shift. The average value of the electronic effect is often replaced by an electronic collision operator. In this work, this operator is calculated for isolated lines without fine structure. The perturbing electron path is taken a hyperbole whose center of diffusion is the emitter ion. The average effect of these collisions is calculated relatively with the impact parameters and the initial speeds according to the Maxwell speeds distribution. Then we consider the movement of the electron around the transmitting ion when the disturbing electron acquires excessive speeds, in other words relativistic speeds. We obtained, for the first time, a collision relativistic operator. We consequently studied the contribution of the relativistic effects relative to the non relativistic case. Many comparisons were made for various values of the electronic density and the minimum impact parameter, and of the temperature. It proves that the high temperatures have a significant effect.
Comment on ‘Plasma metabolomics study in screening and differential diagnosis of multiple primary lung cancer’

We read with great interest the study titled ‘ Plasma metabolomics study in screening and differential diagnosis of multiple primary lung cancer ’ [1] . The study investigates the potential of plasma metabolomics as a tool for screening and diagnosing multiple primary lung cancer (MPLC). The study utilized advanced metabolomics technology to analyze the metabolic alterations associated with the disease and identi ﬁ ed several metabolites that were signi ﬁ cantly altered in patients with MPLC. The study provides valuable insights into the potential of meta-bolomics in the early detection and differential diagnosis of MPLC, which could lead to improved diagnostic and therapeutic approaches for this disease. While the study provides valuable insights into the potential of metabolomics in the early detection and differential diagnosis of MPLC, we would like to raise some potential concerns about the study.
Serious Games Based on Cognitive Bias Modification and Learned Helplessness Paradigms for the Treatment of Depression: Design and Acceptability Study

Background Depression is a debilitating mental health disorder, with a large treatment gap. Recent years have seen a surge in digital interventions to bridge this treatment gap. Most of these interventions are based on computerized cognitive behavioral therapy. Despite the efficacy of computerized cognitive behavioral therapy–based interventions, their uptake is low and dropout rates are high. Cognitive bias modification (CBM) paradigms provide a complementary approach to digital interventions for depression. However, interventions based on CBM paradigms have been reported to be repetitive and boring. Objective In this paper, we described the conceptualization, design, and acceptability of serious games based on CBM paradigms and the learned helplessness paradigm. Methods We searched the literature for CBM paradigms that were shown to be effective in reducing depressive symptoms. For each of the CBM paradigms, we ideated how to create a game so that the gameplay was engaging while the active therapeutic component remained unchanged. Results We developed 5 serious games based on the CBM paradigms and the learned helplessness paradigm. The games include various core elements of gamification, such as goals, challenges, feedback, rewards, progress, and fun. Overall, the games received positive acceptability ratings from 15 users. Conclusions These games may help improve the effectiveness and engagement levels of computerized interventions for depression.