Miracle-dz/UniFAR-FTUs huggingface dataset:
Collaborative Knowledge Infusion for Low-resource Stance Detection

Stance detection is the view towards a specific target by a given context (\textit{e.g.} tweets, commercial reviews). Target-related knowledge is often needed to assist stance detection models in understanding the target well and making detection correctly. However, prevailing works for knowledge-infused stance detection predominantly incorporate target knowledge from a singular source that lacks knowledge verification in limited domain knowledge. The low-resource training data further increases the challenge for the data-driven large models in this task. To address those challenges, we propose a collaborative knowledge infusion approach for low-resource stance detection tasks, employing a combination of aligned knowledge enhancement and efficient parameter learning techniques. Specifically, our stance detection approach leverages target background knowledge collaboratively from different knowledge sources with the help of knowledge alignment. Additionally, we also introduce the parameter-efficient collaborative adaptor with a staged optimization algorithm, which collaboratively addresses the challenges associated with low-resource stance detection tasks from both network structure and learning perspectives. To assess the effectiveness of our method, we conduct extensive experiments on three public stance detection datasets, including low-resource and cross-target settings. The results demonstrate significant performance improvements compared to the existing stance detection approaches.
Magic-induced computational separation in entanglement theory

Entanglement serves as a foundational pillar in quantum information theory, delineating the boundary between what is classical and what is quantum. The common assumption is that higher entanglement corresponds to a greater degree of 'quantumness'. However, this folk belief is challenged by the fact that classically simulable operations, such as Clifford circuits, can create highly entangled states. The simulability of these states raises a question: what are the differences between 'low-magic' entanglement, and 'high-magic' entanglement? We answer this question in this work with a rigorous investigation into the role of magic in entanglement theory. We take an operational approach to understanding this relationship by studying tasks such as entanglement estimation, distillation and dilution. This approach reveals that magic has surprisingly strong implications for entanglement. Specifically, we find a sharp operational separation that splits Hilbert space into two distinct phases: the entanglement-dominated (ED) phase and magic-dominated (MD) phase. Roughly speaking, ED states have entanglement that significantly surpasses their magic, while MD states have magic that dominates their entanglement. The competition between the two resources in these two phases induces a computational phase separation between them: there are sample- and time-efficient quantum algorithms for almost any entanglement task on ED states, while these tasks are provably computationally intractable in the MD phase. To demonstrate the power of our results beyond entanglement theory, we highlight the relevance of our findings in many-body physics and topological error correction. Additionally, we offer simple theoretical explanations for phenomenological observations made in previous numerical studies using ED-MD phases.
Topological Phases and Phase Transitions with Dipolar Symmetry Breaking

Systems with dipole moment conservation have been of recent interest, as they realize both novel quantum dynamics and exotic ground state phases. In this work, we study some generic properties of 1-D and 2-D dipole-conserving fermionic models at integer fillings. We find that a dipolar symmetry-breaking phase can result in a mean-field band insulator whose topological indices can strongly affect the low-energy physics of the dipolar Goldstone modes. We study the 2-D topological phase transition of the mean-field ground states in the presence of the Goldstone modes. The critical theory resembles the 2+1d quantum electrodynamics coupled to massless Dirac fermions with some crucial differences and shows a novel quantum critical point featuring a nontrivial dynamical exponent. We also discuss the analogous case of 1-D dipole-conserving models and the role of topological invariants.
KazParC: Kazakh Parallel Corpus for Machine Translation

We introduce KazParC, a parallel corpus designed for machine translation across Kazakh, English, Russian, and Turkish. The first and largest publicly available corpus of its kind, KazParC contains a collection of 371,902 parallel sentences covering different domains and developed with the assistance of human translators. Our research efforts also extend to the development of a neural machine translation model nicknamed Tilmash. Remarkably, the performance of Tilmash is on par with, and in certain instances, surpasses that of industry giants, such as Google Translate and Yandex Translate, as measured by standard evaluation metrics, such as BLEU and chrF. Both KazParC and Tilmash are openly available for download under the Creative Commons Attribution 4.0 International License (CC BY 4.0) through our GitHub repository.
The neurophysiologic types of nonconvulsive status epilepticus: EEG patterns of different phenotypes

Proceeding from the proposed classification of status epilepticus syndromes by Shorvon in 1994, we performed a systematic search for reports presenting electroencephalography (EEG) patterns of nonconvulsive status epilepticus (NCSE) on all syndromes in the classification, where possible. Using the online medical search engine PubMed for 22 different search strategies, EEG patterns supporting a diagnosis of NCSE were sought. From a total of 4,328 search results, 123 cases with corresponding EEG patterns could be allocated to underlying epilepsy syndromes. Based on the characteristic EEG patterns found for the different NCSE syndromes, we present a synthesis of the significant EEG morphologies and evolutions in the individual NCSE syndromes.
Primal-dual coding to probe light transport

We present primal-dual coding, a photography technique that enables direct fine-grain control over which light paths contribute to a photo. We achieve this by projecting a sequence of patterns onto the scene while the sensor is exposed to light. At the same time, a second sequence of patterns, derived from the first and applied in lockstep, modulates the light received at individual sensor pixels. We show that photography in this regime is equivalent to a matrix probing operation in which the elements of the scene's transport matrix are individually re-scaled and then mapped to the photo. This makes it possible to directly acquire photos in which specific light transport paths have been blocked, attenuated or enhanced. We show captured photos for several scenes with challenging light transport effects, including specular inter-reflections, caustics, diffuse inter-reflections and volumetric scattering. A key feature of primal-dual coding is that it operates almost exclusively in the optical domain: our results consist of directly-acquired, unprocessed RAW photos or differences between them.
Predicting Persuasion-Induced Behavior Change from the Brain

Although persuasive messages often alter people's self-reported attitudes and intentions to perform behaviors, these self-reports do not necessarily predict behavior change. We demonstrate that neural responses to persuasive messages can predict variability in behavior change in the subsequent week. Specifically, an a priori region of interest (ROI) in medial prefrontal cortex (MPFC) was reliably associated with behavior change (r = 0.49, p < 0.05). Additionally, an iterative cross-validation approach using activity in this MPFC ROI predicted an average 23% of the variance in behavior change beyond the variance predicted by self-reported attitudes and intentions. Thus, neural signals can predict behavioral changes that are not predicted from self-reported attitudes and intentions alone. Additionally, this is the first functional magnetic resonance imaging study to demonstrate that a neural signal can predict complex real world behavior days in advance.
MMCert: Provable Defense against Adversarial Attacks to Multi-modal Models

Different from a unimodal model whose input is from a single modality, the input (called multi-modal input) of a multi-modal model is from multiple modalities such as image, 3D points, audio, text, etc. Similar to unimodal models, many existing studies show that a multi-modal model is also vulnerable to adversarial perturbation, where an attacker could add small perturbation to all modalities of a multi-modal input such that the multi-modal model makes incorrect predictions for it. Existing certified defenses are mostly designed for unimodal models, which achieve sub-optimal certified robustness guarantees when extended to multi-modal models as shown in our experimental results. In our work, we propose MMCert, the first certified defense against adversarial attacks to a multi-modal model. We derive a lower bound on the performance of our MMCert under arbitrary adversarial attacks with bounded perturbations to both modalities (e.g., in the context of auto-driving, we bound the number of changed pixels in both RGB image and depth image). We evaluate our MMCert using two benchmark datasets: one for the multi-modal road segmentation task and the other for the multi-modal emotion recognition task. Moreover, we compare our MMCert with a state-of-the-art certified defense extended from unimodal models. Our experimental results show that our MMCert outperforms the baseline.
Privacy-Preserving Public Auditing for Regenerating-Code-Based Cloud Storage

To protect outsourced data in cloud storage against corruptions, adding fault tolerance to cloud storage together with data integrity checking and failure reparation becomes critical. Recently, regenerating codes have gained popularity due to their lower repair bandwidth while providing fault tolerance. Existing remote checking methods for regenerating-coded data only provide private auditing, requiring data owners to always stay online and handle auditing, as well as repairing, which is sometimes impractical. In this paper, we propose a public auditing scheme for the regenerating-code-based cloud storage. To solve the regeneration problem of failed authenticators in the absence of data owners, we introduce a proxy, which is privileged to regenerate the authenticators, into the traditional public auditing system model. Moreover, we design a novel public verifiable authenticator, which is generated by a couple of keys and can be regenerated using partial keys. Thus, our scheme can completely release data owners from online burden. In addition, we randomize the encode coefficients with a pseudorandom function to preserve data privacy. Extensive security analysis shows that our scheme is provable secure under random oracle model and experimental evaluation indicates that our scheme is highly efficient and can be feasibly integrated into the regenerating-code-based cloud storage.
Restless legs syndrome and pregnancy: prevalence, possible pathophysiological mechanisms and treatment

Restless legs syndrome (RLS) is a common sleep disorder that may be associated with pregnancy. Studies have found that the prevalence of RLS among pregnant women ranged from 10 to 34%. Typically, there is complete remission of symptoms soon after parturition; however, in some patients, they may continue postpartum. RLS has been shown to be associated with a number of complications in pregnancy including preeclampsia and increased incidence of Cesarean sections. Although multiple hypotheses have been proposed to explain this association, each individual hypothesis cannot completely explain the whole pathogenesis. Present understanding suggests that a strong family history, low serum iron and ferritin level, and high estrogen level during pregnancy might play important roles. Vitamin D deficiency and calcium metabolism may also play a role. Medical treatment of RLS during pregnancy is difficult and challenging considering the risks to mother and fetus. However, in some cases, the disease may be severe enough to require treatment.
Rotator Cuff Syndrome: Personal, Work-Related Psychosocial and Physical Load Factors

Objective: To identify factors associated with rotator cuff syndrome (RCS) among active workers. Methods: Seven hundred thirty-three workers in 12 worksites participated in a cross-sectional study with individual structured physical and psychosocial health interviews, physical examinations, and exposure assessments of biomechanical factors. Work organization, including job content or structural constraints, was assessed at the departmental level. Multivariable logistic modeling was used. Results: Fifty-five subjects (7.5%) had RCS. Cases were more likely to report low job security (P < 0.04) and to have very high job structural constraints (P < 0.03). Age and body mass index were marginally significant. Upper arm flexion ≥ 45° ≥ 15% of time and either duty cycle of forceful exertions ≥9% time (odds ratio = 2.43, 95% CI = 1.04 to 5.68) or forceful pinch >0% [odds ratio = 2.66, 95% CI = 1.26 to 5.59] were significant risk factors. Conclusions: Long duration of shoulder flexion and forceful exertion (especially pinch) in a job are significant risk factors for RCS. Work organization may impact physical and psychosocial exposures and should be further explored.
Classical simulation of non-Gaussian bosonic circuits

We propose efficient classical algorithms which (strongly) simulate the action of bosonic linear optics circuits applied to superpositions of Gaussian states. Our approach relies on an augmented covariance matrix formalism to keep track of relative phases between individual terms in a linear combination. This yields an exact simulation algorithm whose runtime is polynomial in the number of modes and the size of the circuit, and quadratic in the number of terms in the superposition. We also present a faster approximate randomized algorithm whose runtime is linear in this number. Our main building blocks are a formula for the triple overlap of three Gaussian states and a fast algorithm for estimating the norm of a superposition of Gaussian states up to a multiplicative error. Our construction borrows from earlier work on simulating quantum circuits in finite-dimensional settings, including, in particular, fermionic linear optics with non-Gaussian initial states and Clifford computations with non-stabilizer initial states. It provides algorithmic access to a practically relevant family of non-Gaussian bosonic circuits.
Fluctuations of the additive martingales related to super-Brownian motion

Let $(W_{t}(\lambda))_{t\ge 0}$, parametrized by $\lambda\in\mathbb{R}$, be the additive martingale related to a supercritical super-Brownian motion on the real line and let $W_{\infty}(\lambda)$ be its limit. Under a natural condition for the martingale limit to be non-degenerate, we investigate the rate at which the martingale approaches its limit. Indeed, assuming certain moment conditions on the branching mechanism, we show that the tail martingale $W_{\infty}(\lambda)-W_{t}(\lambda)$, properly normalized, converges in distribution to a non-degenerate random variable, and we identify the limit laws. We find that, for parameters with small absolute value, the fluctuations are affected by the behaviour of the branching mechanism $\psi$ around $0$. In fact, we prove that, in the case of small $|\lambda|$, when $\psi$ is secondly differentiable at $0$, the limit laws are scale mixtures of the standard normal laws, and when $\psi$ is `stable-like' near $0$ in some proper sense, the limit laws are scale mixtures of the stable laws. However, the effect of the branching mechanism is limited in the case of large $|\lambda|$. In the latter case, we show that the fluctuations and limit laws are determined by the limiting extremal process of the super-Brownian motion.
Prediction and identification of point defect fingerprints in the X-ray photoelectron spectra of TiN$_x$

We investigate the effect of selected N and Ti point defects in $B$1 TiN on N 1s and Ti 2p$_{3/2}$ binding energies (BE) by experiments and ab initio calculations. X-ray photoelectron spectroscopy (XPS) measurements of Ti-deficient TiN films reveal additional N 1s spectral components at lower binding energies. Ab initio calculations predict that these components are caused by either Ti vacancies, which induce a N 1s BE shift of $-0.53$ eV in its first N neighbors, and/or N tetrahedral interstitials, which have their N 1s BE shifted by $-1.18$ eV and also shift BE of their first N neighbors by $-0.53$ eV. However, the {\it ab initio} calculations also reveal that the tetrahedral N interstitial is unstable at room temperature. We, therefore, unambiguously attribute the detected signal to Ti vacancies. Furthermore, the vacancy concentration in Ti-deficient TiN was quantified with XPS supported by ab initio calculations. The largest BE shifts of $-1.53$, $-1.80$ and $-2.28$ eV for Ti 2p$_{3/2}$ electrons are predicted for the Ti tetrahedral, split (10$\overline{1}$)-aligned and split (111)-aligned interstitial atoms, respectively, and we, therefore, propose XPS could detect them. Other defects such as N vacancy or N split (10$\overline{1}$)-aligned interstitial introduce smaller N 1s and Ti 2p$_{3/2}$ BE shifts and are unlikely to be detectable experimentally. Our work highlights the potential of ab initio-guided XPS measurements in detecting and quantifying point defects in $B$1 TiN.
Inverse Potentials for $\alpha-\alpha$ Scattering using Gaussian Interaction

Alpha-alpha scattering phase shifts (SPS) have been determined using phase function method (PFM) with local nuclear interaction between two alpha clusters modeled as double Gaussian potential and Coulomb repulsion in the form of $erf()$ function. The obtained SPS for various $\ell$-channels have been compared with experimental ones with mean absolute percentage error (MAPE) as a measure. The model parameters have been optimised by suitable optimisation technique by looking for minimum value of MAPE. The results for $\ell$=0$^+$, 2$^+$ and 4$^+$ partial waves have been obtained, to match with experimental SPS, with MAPE values of $7.6$, $2.7$ and $6.5$ respectively for data up to $120$ MeV, while for higher states 6$^+$, 8$^+$ and 10$^+$ has MAPE of $1.7, 7.4$ and $10.4$ respectively for data from $53-120$ MeV. On extrapolation for data in range E$_{\ell ab. }$ = $23-120$ MeV, using the optimised parameters, the SPS are found to be in close agreement with experimental ones for first three channels.
JDocQA: Japanese Document Question Answering Dataset for Generative Language Models

Document question answering is a task of question answering on given documents such as reports, slides, pamphlets, and websites, and it is a truly demanding task as paper and electronic forms of documents are so common in our society. This is known as a quite challenging task because it requires not only text understanding but also understanding of figures and tables, and hence visual question answering (VQA) methods are often examined in addition to textual approaches. We introduce Japanese Document Question Answering (JDocQA), a large-scale document-based QA dataset, essentially requiring both visual and textual information to answer questions, which comprises 5,504 documents in PDF format and annotated 11,600 question-and-answer instances in Japanese. Each QA instance includes references to the document pages and bounding boxes for the answer clues. We incorporate multiple categories of questions and unanswerable questions from the document for realistic question-answering applications. We empirically evaluate the effectiveness of our dataset with text-based large language models (LLMs) and multimodal models. Incorporating unanswerable questions in finetuning may contribute to harnessing the so-called hallucination generation.
Organic Geochemistry of the Cenomanian–Turonian Bahloul Formation Petroleum Source Rock, Central and Northern Tunisia

Total organic carbon (TOC) determination, Rock‐Eval pyrolysis, extractable organic matter content (EOM) fractionation, gas chromatography (GC) and gas chromatography–mass spectrometry (GC–MS) analyses, were carried out on 79 samples from eleven outcrop cross sections of the Bahloul Formation in central and northern Tunisia. The TOC content varied between 0.23 to 35.6%, the highest average values (18.73%, 8.46% and 4.02%) being at the east of the study area (at Ain Zakkar, Oued Bahloul and Dyr Ouled Yahia localities, respectively). The Rock‐Eval maximum pyrolysis temperature (Tmax) values in the 424–453°C range delineated a general east‐west trend increase in the organic matter (OM) maturity. The disparity in hydrogen index (HI) values, in the range 114–824 mg hydrocarbons (HC) g−1 TOC, is relevant for the discrepancy in the level of OM preservation and maturity among localities and samples. The n‐alkane distributions, maximizing in the C17 to C20 range, are typical for a marine planktonic origin, whereas pristine/phytane (Pr/Ph) average values in the 1–2 range indicate an oxic to suboxic depositional environment. Pr/n‐C17 and Ph/n‐C18 ratios in the 0.38–6.2 and 0.68–3.25 range, respectively, are consistent with other maturity indicators and the contribution of specific bacteria to phytol as a precursor of isoprenoids. The thermal maturity varies between late diagenesis to main‐stage of petroleum generation based on the optic and the cis‐trans isomerisation of the C29 sterane [20S/(20S+20R) and 14β(H),17β(H)/(14β(H),17β(H)+14α(H),17α(H)), respectively] and the terpane [18α(H)22,29,30‐Trisnorneohopane/(18α(H)22,29,30‐Trisnorneohopane+17α(H)22,29,30‐Trisnorhopane): Ts/(Ts+Tm)] ratios. The Bahloul OM is represented by an open marine to estuarine algal facies with a specific bacterial contribution as revealed by the relative abundance of the ααα‐20R C27 (33–44%), C28 (22–28%) and C29 (34–41%) steranes and by the total terpanes/total steranes ratio (1.2–5.33). These results attested that the Bahloul OM richness was controlled both by an oxygen minimum zone induced by high productivity and restricted circulation in narrow half graben structures and around diapirs of the Triassic salt.
Claudin-4 expression in gastric cancer cells enhances the invasion and is associated with the increased level of matrix metalloproteinase-2 and -9 expression

Claudin-4 is a member of a large family of transmembrane proteins known as claudins, which are essential for the formation and maintenance of tight junctions. Our previous studies have revealed that claudin-4 proteins are overexpressed in metastatic gastric cancer. To clarify the roles of claudin-4 in gastric cancer metastasis, human gastric adenocarcinoma (AGS) cells constitutively expressing wild-type claudin-4 were generated. Expression of claudin-4 in AGS cells was found to increase cell invasion and migration, as measured by Boyden invasion chamber assays. Moreover, the claudin-4-expressing AGS cells were found to have increased matrix metalloproteinase (MMP)-2 and -9 expression, indicating that claudin-mediated increased invasion may be mediated through the activation of the MMP protein. Overall, the results suggest that claudin-4 overexpression may promote gastric cancer metastasis through the increased invasion of gastric cancer cells.