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Conversations often adhere to well-understood social norms that vary across cultures. For example, while "addressing parents by name" is commonplace in the West, it is rare in most Asian cultures. Adherence or violation of such norms often dictates the tenor of conversations. Humans are able to navigate social situations requiring cultural awareness quite adeptly. However, it is a hard task for NLP models. In this paper, we tackle this problem by introducing a "Cultural Context Schema" for conversations. It comprises (1) conversational information such as emotions, dialogue acts, etc., and (2) cultural information such as social norms, violations, etc. We generate ~110k social norm and violation descriptions for ~23k conversations from Chinese culture using LLMs. We refine them using automated verification strategies which are evaluated against culturally aware human judgements. We organize these descriptions into meaningful structures we call "Norm Concepts", using an interactive human-in-loop framework. We ground the norm concepts and the descriptions in conversations using symbolic annotation. Finally, we use the obtained dataset for downstream tasks such as emotion, sentiment, and dialogue act detection. We show that it significantly improves the empirical performance., Comment: 19 pages, 9 figures, 7 tables

Recognizing texts from camera images is a known hard problem because of the difficulties in text detection from the varied and complicated background. In this paper we propose a novel and efficient method to detect text region from images with complex background using Wavelet Transforms. The framework uses Wavelet Transformation of the original image in its grayscale form followed by Sub-band filtering. Then Region clustering technique is applied using centroids of the regions, further Bounding box is fitted to each region thus identifying the text regions. This method is much sophisticated and efficient than the previous methods as it doesn't stick to a particular font size of the text thus, making it generalized. The sample set used for experimental purpose consists of 50 images with varying backgrounds. Images with edge prominence are considered. Furthermore, our method can be easily customized for applications with different scopes.

In this paper, we investigate the problem of "generation supervision" in large language models, and present a novel bicameral architecture to separate supervision signals from their core capability, helpfulness. Doppelg\"anger, a new module parallel to the underlying language model, supervises the generation of each token, and learns to concurrently predict the supervision score(s) of the sequences up to and including each token. In this work, we present the theoretical findings, and leave the report on experimental results to a forthcoming publication.

The ground state of a one-dimensional spin-1/2 uniform antiferromagnetic Heisenberg chain (AfHc) is a Tomonaga-Luttinger liquid which is quantum-critical with respect to applied magnetic fields upto a saturation field Hs beyond which it transforms to a fully polarised state. Wilson ratio has been predicted to be a good indicator for demarcating these phases [Phys. Rev. B 96, 220401 (2017)]. From detailed temperature and magnetic field dependent magnetisation, magnetic susceptibility and specific heat measurements in a metalorganic complex and comparisons with field theory and quantum transfer matrix method calculations, the complex was found to be a very good realisation of a spin-1/2 AfHc. Wilson ratio obtained from experimentally obtained magnetic susceptibility and magnetic contribution of specific heat values was used to map the magnetic phase diagram of the uniform spin-1/2 AfHc over large regions of phase space demarcating Tomonaga-Luttinger liquid, saturation field quantum critical, and fully polarised states. Luttinger parameter and spinon velocity were found to match very well with the values predicted from conformal field theory., Comment: Accepted for publication in PNAS Nexus

Phase transitions in many-body systems are often associated with the emergence of spatial inhomogeneities. Such features may develop at microscopic lengthscales and are not necessarily evident in measurements of macroscopic quantities. In this work, we address the topic of distribution of current paths in superconducting films. Typical lengthscales associated with superconductivity are in the range of nanometres. Accordingly, measurements of electrical resistance over much larger distances are supposed to be insensitive to details of spatial inhomogeneities of electronic properties. We observe that, contrary to expectations, current paths adopt a highly non-uniform distribution at the onset of the superconducting transition which is manifested in the development of a finite transverse resistance. The anisotropic distribution of current density is unrelated to the structural properties of the superconducting films, and indicates the emergence of electronic inhomogeneities perceivable over macroscopic distances. Our experiments reveal the ubiquitous nature of this phenomenon in conventional superconductors.

The physics of the paradigmatic one-dimensional transverse field quantum Ising model $J \sum_{\langle i,j \rangle} \sigma^x_i \sigma^x_j + h \sum_i \sigma^z_i$ is well-known. Instead, let us imagine "applying" the transverse field via a transverse Ising coupling of the spins to partner auxiliary spins, i.e. $H= J_x \sum_{\langle i,j \rangle} \sigma^x_i \sigma^x_j + J_z \sum_i \sigma^z_i \sigma^z_{\text{partner of }i}$. If each spin of the chain has a unique auxiliary partner, then the resultant eigenspectrum is still the same as that of the quantum Ising model with $\frac{h}{J} = \frac{J_z}{J_x}$ and the degeneracy of the entire spectrum is $2^{\text{number of auxiliary spins}}$. We can interpret this as the auxiliary spins remaining paramagnetic down to zero temperature and an extensive ground state entropy. This follows from the existence of extensively large and mutually \guillemotleft anticommuting\guillemotright $\;$ sets of $local$ conserved quantities for $H$. Such a structure will be shown to be not unnatural in the class of bond-dependent Hamiltonians. In the above quantum Ising model inspired example of $H$, this is lost upon the loss of the unique partner condition for the full spin chain. Other cases where such degeneracy survives or gets lost are also discussed. Thus this is more general and forms the basis for an exact statement on the existence of extensive ground state entropy in any dimension. Furthermore this structure can be used to prove spin liquidity non-perturbatively in the ground state manifold. Higher-dimensional quantum spin liquid constructions based on this are given which may evade a quasiparticle description., Comment: 11+ pages, 6 figures, 3 tables. No major changes compared to v4. Multi-spin correlations results sharpened and made complete. Additional discussion on global parity conservation in the two-dimensional models added

We write down a class of two-dimensional quantum spin-1/2 Hamiltonians whose eigenspectra are exactly solvable via the Jordan-Wigner transformation. The general structure corresponds to a suitable grid composed of XY or XX-Ising spin chains and ZZ-Ising spin chains and is generalizable to higher dimensions. They can host stacks of one-dimensional spinless fermion liquids with gapless excitations and power-law correlations coexisting with ordered spin moments (localized spinless fermions). Bond-dependent couplings thus can be an alternate mechanism than geometric frustration of SU(2)-symmetric couplings to obtain spinless fermionic excitations. Put in a different way, bond-dependent couplings allow for an embedding of one-dimensional spinless fermion (Tomonoga-Luttinger) liquids and solids and also Majorana excitations in higher dimensions. They can accommodate a simpler set of site-local conserved quantities apart from the more intricate, interlocked set of plaquette-local or bond-local conserved quantities in Kitaev's honeycomb model with Majorana excitations. The proposed grid structure may provide an architecture for quantum engineering with controllable qubits., Comment: 5 pages, 7 figures for journal publication. An earlier conference contribution based on this work is available at arXiv:2406.10669

This contribution summarizes the main results of a work on exactly solvable Hamiltonians for quantum magnets. A class of Hamiltonians which supports fractionalized spinless fermionic excitations in dimensions greater than one is written down. A well-known one-dimensional example is that of S=1/2 spin chains with Luttinger liquid physics and spinless fermionic excitations that are also called spinons. A well-known two-dimensional example is that of Kitaev's S=1/2 honeycomb model with bond-dependent magnetic couplings which supports Majorana fermionic excitations. The class of models to be discussed here also exploits bond-dependent couplings in a different way to non-perturbatively stabilize spinless fermionic spinons and also Majorana fermions. A more detailed account of these results is being prepared for publication elsewhere., Comment: 5 pages, 1 figure. Part of the proceedings of 67th DAE Solid State Physics Symposium of the BARC, India. The tentative title for the planned publication is "A solvable embedding mechanism for one-dimensional spinless fermions liquids and solids in higher-dimensional spin-1/2 magnets". First appeared publicly at https://tinyurl.com/5umc4dzx Supporting data available at https://tinyurl.com/47jfku9d

We report the synthesis and properties of (H,Li)$_{6}$Ru$_{2}$O$_{6}$, which is shown to be a $J_{\text{eff}}=\frac{1}{2}$ system made out of Ru$^{3+}$ moments in a honeycomb geometry. Bulk magnetization, heat capacity, nuclear magnetic resonance (NMR), and muon spin relaxation ($\mu$SR) rule out the presence of static moments or any spin glass phase down to 500 mK. All techniques suggest a crossover to a liquid-like state below about 40 K. The $^{7}$Li nuclear magnetic resonance (NMR) shift data suggest a non-zero $T$-independent spin susceptibility at low $T$. In zero field, $C_m/T$ shows $T^{-1}$ divergence which is consistent with vacancy-induced effects on low-energy excitations of the pristine Kitaev spin liquid. With field, power-law variations in the $^{7}$Li NMR spin-lattice relaxation rate 1/T$_{1}$ and magnetic heat capacity $C_{m}$ show quantitatively new scaling behaviors. A two-step entropy release in heat capacity is also observed putatively from $Z_{2}$ flux (low-$T$ step) and itinerant Majorana fermions (high-$T$ step). Based on these findings, we propose that (H,Li)$_{6}$Ru$_{2}$O$_{6}$ realizes a Kitaev spin liquid with no evidence of inherent magnetic ordering in zero field unlike $\alpha$-RuCl$_{3}$ where approximately $8$ Tesla field is required to suppress magnetic order., Comment: 5 pages, 6 figures of main text + 12 pages, 25 figures of supplementary information

Monitoring the threat landscape to be aware of actual or potential attacks is of utmost importance to cybersecurity professionals. Information about cyber threats is typically distributed using natural language reports. Natural language processing can help with managing this large amount of unstructured information, yet to date, the topic has received little attention. With this paper, we present AnnoCTR, a new CC-BY-SA-licensed dataset of cyber threat reports. The reports have been annotated by a domain expert with named entities, temporal expressions, and cybersecurity-specific concepts including implicitly mentioned techniques and tactics. Entities and concepts are linked to Wikipedia and the MITRE ATT&CK knowledge base, the most widely-used taxonomy for classifying types of attacks. Prior datasets linking to MITRE ATT&CK either provide a single label per document or annotate sentences out-of-context; our dataset annotates entire documents in a much finer-grained way. In an experimental study, we model the annotations of our dataset using state-of-the-art neural models. In our few-shot scenario, we find that for identifying the MITRE ATT&CK concepts that are mentioned explicitly or implicitly in a text, concept descriptions from MITRE ATT&CK are an effective source for training data augmentation., Comment: Accepted at LREC-COLING 2024. Corpus available at https://github.com/boschresearch/anno-ctr-lrec-coling-2024

Within-basket recommendation (WBR) refers to the task of recommending items to the end of completing a non-empty shopping basket during a shopping session. While the latest innovations in this space demonstrate remarkable performance improvement on benchmark datasets, they often overlook the complexity of user behaviors in practice, such as 1) co-existence of multiple shopping intentions, 2) multi-granularity of such intentions, and 3) interleaving behavior (switching intentions) in a shopping session. This paper presents Neural Pattern Associator (NPA), a deep item-association-mining model that explicitly models the aforementioned factors. Specifically, inspired by vector quantization, the NPA model learns to encode common user intentions (or item-combination patterns) as quantized representations (a.k.a. codebook), which permits identification of users's shopping intentions via attention-driven lookup during the reasoning phase. This yields coherent and self-interpretable recommendations. We evaluated the proposed NPA model across multiple extensive datasets, encompassing the domains of grocery e-commerce (shopping basket completion) and music (playlist extension), where our quantitative evaluations show that the NPA model significantly outperforms a wide range of existing WBR solutions, reflecting the benefit of explicitly modeling complex user intentions., Comment: 13 pages, 9 figures

Smart shoes have ushered in a new era of personalised health monitoring and assistive technology. The shoe leverages technologies such as Bluetooth for data collection and wireless transmission and incorporates features such as GPS tracking, obstacle detection, and fitness tracking. This article provides an overview of the current state of smart shoe technology, highlighting the integration of advanced sensors for health monitoring, energy harvesting, assistive features for the visually impaired, and deep learning for data analysis. The study discusses the potential of smart footwear in medical applications, particularly for patients with diabetes, and the ongoing research in this field. Current footwear challenges are also discussed, including complex construction, poor fit, comfort, and high cost.

This paper presents a comprehensive comparative analysis of Large Language Models (LLMs) for generation of code documentation. Code documentation is an essential part of the software writing process. The paper evaluates models such as GPT-3.5, GPT-4, Bard, Llama2, and Starchat on various parameters like Accuracy, Completeness, Relevance, Understandability, Readability and Time Taken for different levels of code documentation. Our evaluation employs a checklist-based system to minimize subjectivity, providing a more objective assessment. We find that, barring Starchat, all LLMs consistently outperform the original documentation. Notably, closed-source models GPT-3.5, GPT-4, and Bard exhibit superior performance across various parameters compared to open-source/source-available LLMs, namely LLama 2 and StarChat. Considering the time taken for generation, GPT-4 demonstrated the longest duration, followed by Llama2, Bard, with ChatGPT and Starchat having comparable generation times. Additionally, file level documentation had a considerably worse performance across all parameters (except for time taken) as compared to inline and function level documentation., Comment: Under review

Particle Swarm Optimization (PSO) has emerged as a powerful metaheuristic global optimization approach over the past three decades. Its appeal lies in its ability to tackle complex multidimensional problems that defy conventional algorithms. However, PSO faces challenges, such as premature stagnation in single-objective scenarios and the need to strike a balance between exploration and exploitation. Hybridizing PSO by integrating its cooperative nature with established optimization techniques from diverse paradigms offers a promising solution. In this paper, we investigate various strategies for synergizing gradient-based optimizers with PSO. We introduce different hybridization principles and explore several approaches, including sequential decoupled hybridization, coupled hybridization, and adaptive hybridization. These strategies aim to enhance the efficiency and effectiveness of PSO, ultimately improving its ability to navigate intricate optimization landscapes. By combining the strengths of gradient-based methods with the inherent social dynamics of PSO, we seek to address the critical objectives of intelligent exploration and exploitation in complex optimization tasks. Our study delves into the comparative merits of these hybridization techniques and offers insights into their application across different problem domains.

Social media discourse frequently consists of 'seemingly similar language used by opposing sides of the political spectrum', often translating to starkly contrasting perspectives. E.g., 'thoughts and prayers', could express sympathy for mass-shooting victims, or criticize the lack of legislative action on the issue. This paper defines the context required to fully understand such ambiguous statements in a computational setting and ground them in real-world entities, actions, and attitudes. We propose two challenging datasets that require an understanding of the real-world context of the text. We benchmark these datasets against models built upon large pre-trained models, such as RoBERTa and GPT-3. Additionally, we develop and benchmark more structured models building upon existing Discourse Contextualization Framework and Political Actor Representation models. We analyze the datasets and the predictions to obtain further insights into the pragmatic language understanding challenges posed by the proposed social grounding tasks., Comment: Accepted as an oral at EMNLP 2024 main conference

Cavansite is a visually stunning blue vanadosilicate mineral with limited occurrences worldwide, whereas Pentagonite is a closely related dimorph with similar physical and chemical properties, yet is extremely rare compared to Cavansite. The reasons behind Pentagonite's exceptional rarity remain largely unknown. In this study, (a) density functional theory (DFT) is utilized to investigate the electronic structures of Cavansite and Pentagonite at ground state and finite pressures; (b) a two-state Boltzmann probability model is then employed to construct a comprehensive phase diagram that reveals the abundance of each species across a wide range of pressure and temperature conditions; and (c) dehydration characteristics of these two minerals are explored. The present analysis reveals the key factors that contribute to the relative scarcity of Pentagonite, including differences in structural arrangement and electronic configurations between the two minerals. Specifically, it shows that (a) because of the peculiar arrangements of SiO4 polyhedra, Cavansite forms a compact structure (about 2.7% less in volume) resulting in lower energy; (b) at a temperature of about 650K only about 1% Pentagonite can form; (c) vanadium induces a highly localized state in both of these otherwise large-band-gap insulators resulting in an extremely weak magnetic phase that is unlikely to be observed at any reasonable finite temperature; and (d) water molecules are loosely bound inside the microporous crystals of Cavansite and Pentagonite, suggesting potential applications of these minerals in various technological fields.

Recommender systems aim to enhance the overall user experience by providing tailored recommendations for a variety of products and services. These systems help users make more informed decisions, leading to greater user engagement with the platform. However, the implementation of these systems largely depends on the context, which can vary from recommending an item or package to a user or a group. This requires careful exploration of several models during the deployment, as there is no comprehensive and unified approach that deals with recommendations at different levels. Furthermore, these individual models must be closely attuned to their generated recommendations depending on the context to prevent significant variation in their generated recommendations. In this paper, we propose a novel unified recommendation framework that addresses all four recommendation tasks, namely, personalized, group, package, and package-to-group recommendation, filling the gap in the current research landscape. The proposed framework can be integrated with most of the traditional matrix factorization-based collaborative filtering (CF) models. This research underscores the significance of including group and package information while learning latent representations of users and items for personalized recommendations. These components help in exploiting a rich latent representation of the user/item by enforcing them to align closely with their corresponding group/package representation. We consider two prominent CF techniques, namely Regularized Matrix Factorization and Maximum Margin Matrix factorization, as the baseline models and demonstrate their customization to various recommendation tasks. Experimental results on two publicly available datasets are reported, comparing them to other baseline approaches for various recommendation tasks., Comment: 29 pages

Berry phase interference arguments that underlie the theory of deconfined quantum criticality (DQC) for spin-$\frac{1}{2}$ antiferromagnets have also been invoked to allow for continuous transitions in spin-1 magnets including a N\'eel to (columnar) valence bond solid (cVBS) transition. We provide a microscopic model realization of this transition on the square lattice consisting of Heisenberg exchange ($J_H$) and biquadratic exchange ($J_B$) that favor a N\'eel phase, and a designed $Q$-term ($Q_B$) interaction which favors a cVBS through large-scale quantum Monte Carlo (QMC) simulations. For $J_H=0$, this model is equivalent to the $SU(3)$ $JQ$ model with a N\'eel-cVBS transition that has been argued to be DQC through QMC. Upon turning on $J_H$ which brings down the symmetry to $SU(2)$, we find multiple signatures -- a single critical point, high quality collapse of correlation ratios and order parameters, "$U(1)$-symmetric" cVBS histograms and lack of double-peak in order parameter histograms for largest sizes studied near the critical point -- that are highly suggestive of a continuous transition scenario. However, Binder analysis finds negative dips that grow sub-extensively that we interpret as these transitions rather being pseudocritical. This along with recent results on spin-$\frac{1}{2}$ models suggests that deconfined pseudocriticality is the more generic scenario., Comment: Several references added. No major changes. 7 pages, 5 figures of main text. 22 pages of supplementary data. Submitted to PRL. Offered PRB. (Review exchange at https://www.dropbox.com/scl/fo/cuzciggsndwwpbznytt5g/AAi9TAQ_eGEL_AQnxU-BbbU?rlkey=0aesnku2bjv62vup1o2ps06sj&dl=0 ; names and accession code redacted)

Group recommender systems (GRS) are critical in discovering relevant items from a near-infinite inventory based on group preferences rather than individual preferences, like recommending a movie, restaurant, or tourist destination to a group of individuals. The traditional models of group recommendation are designed to act like a black box with a strict focus on improving recommendation accuracy, and most often, they place the onus on the users to interpret recommendations. In recent years, the focus of Recommender Systems (RS) research has shifted away from merely improving recommendation accuracy towards value additions such as confidence and explanation. In this work, we propose a conformal prediction framework that provides a measure of confidence with prediction in conjunction with a group recommender system to augment the system-generated plain recommendations. In the context of group recommender systems, we propose various nonconformity measures that play a vital role in the efficiency of the conformal framework. We also show that defined nonconformity satisfies the exchangeability property. Experimental results demonstrate the effectiveness of the proposed approach over several benchmark datasets. Furthermore, our proposed approach also satisfies validity and efficiency properties., Comment: 23 pages

Collaborative filtering (CF) has become a popular method for developing recommender systems (RSs) where ratings of a user for new items are predicted based on her past preferences and available preference information of other users. Despite the popularity of CF-based methods, their performance is often greatly limited by the sparsity of observed entries. In this study, we explore the data augmentation and refinement aspects of Maximum Margin Matrix Factorization (MMMF), a widely accepted CF technique for rating predictions, which has not been investigated before. We exploit the inherent characteristics of CF algorithms to assess the confidence level of individual ratings and propose a semi-supervised approach for rating augmentation based on self-training. We hypothesize that any CF algorithm's predictions with low confidence are due to some deficiency in the training data and hence, the performance of the algorithm can be improved by adopting a systematic data augmentation strategy. We iteratively use some of the ratings predicted with high confidence to augment the training data and remove low-confidence entries through a refinement process. By repeating this process, the system learns to improve prediction accuracy. Our method is experimentally evaluated on several state-of-the-art CF algorithms and leads to informative rating augmentation, improving the performance of the baseline approaches., Comment: 21 pages

We show here that a direct application of resummation-based quantum Monte Carlo (QMC) -- implemented recently for sign-problem-free SU(2)-symmetric Hamiltonians in the stochastic series expansion (SSE) framework -- does not reduce the sign problem for frustrated SU(2)-symmetric Heisenberg antiferromagnets on canonical geometrically frustrated lattices composed of triangular motifs such as the triangular lattice. In the process, we demonstrate that resummation-based updates do provide an ergodic sampling of the SSE-based QMC configurations which can be an issue when using the standard SSE updates, however, severely limited by the sign problem as previously mentioned. The notions laid out in these notes may be useful in the design of better algorithms for geometrically frustrated magnets., Comment: 13 pages, 5 figures in one-column preprint format

Topological insulator-based methods underpin the topological classification of gapped bands, including those surrounding semi-metallic nodal defects. However, multiple bands with gap-closing points can also possess non-trivial topology. We construct a general wavefunction-based ``punctured-Chern" invariant to capture such topology. To show its general applicability, we analyze two systems with disparate gapless topology: 1) a recent two-dimensional fragile topological model to capture the various band-topological transitions and 2) a three-dimensional model with a triple-point nodal defect to characterize its semi-metallic topology with \emph{half-integers} that govern physical observables such as anomalous transport. This invariant also gives the classification for Nexus triple-points ($\mathbb{Z}\times\mathbb{Z}$) with certain symmetry restrictions, which is re-confirmed by abstract algebra., Comment: Significant revision compared to the previous version with additional results on a fragile topological model. Previous results are unaffected. Codes are available at https://www.dropbox.com/s/wgy3ugj7nfk0c5w/Nexus_2d_3d.nb?dl=0 on how to calculate the punctured-Chern numbers in both the 2D and 3D applications shown in the paper. 6 pages of main text with 3 figures, 4 pages of supplementary text

The role of point defects in temporal evolution of Cr-rich alpha-prime phase separation in binary Fe-20 at.% Cr alloy is elucidated by intercepting the long term (upto 1000 h at 773 K) aging at regular intervals and probing by a combination of atom probe tomography (APT) and positron annihilation spectroscopy (PAS). Since the Cr concentration of nano-scale {\alpha}/ phase in Fe-20 at.% Cr alloy increases continually on aging, the point defects play active role throughout the aging duration. The near-atomic resolution of APT and self-seeking ability of positrons towards point defects make this analysis possible. The difference of positron affinities between Fe and Cr enables identification of the Cr-rich nano-phases that are specifically associated with defects; they would otherwise be indiscernible in the absence of defect. Thus, the temporal evolution of Cr-rich nano-phase along with the associated point defects can be fully characterized at each stage of aging using APT and PAS, respectively. The combined APT-PAS study shows that Cr-rich alpha-prime precipitation is preceded by vacancy-Cr complexes that act as nucleation sites for Cr clusters, which in turn, cause an early rise in micro-hardness due to cluster hardening. Interestingly, this is accompanied by a significant rise in point defect concentration. Our results depict that the vacancies present in the core of the alpha-prime precipitates migrate towards the precipitates interface during the course of aging and eventually get recovered resulting in virtually defect-free precipitates. This study chronicles the way the point defects shape the process of alpha-prime phase separation throughout the entire length of aging., Comment: 51 pages, 13 figures

The COVID-19 pandemic has been severely impacting global society since December 2019. Massive research has been undertaken to understand the characteristics of the virus and design vaccines and drugs. The related findings have been reported in biomedical literature at a rate of about 10,000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200,000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g., Diagnosis and Treatment) to the articles in LitCovid. Despite the continuing advances in biomedical text mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset, consisting of over 30,000 articles with manually reviewed topics, was created for training and testing. It is one of the largest multilabel classification datasets in biomedical scientific literature. 19 teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181, and 0.9394 for macro F1-score, micro F1-score, and instance-based F1-score, respectively. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development.

Nitrogen-Vacancy (NV) centers in diamonds provide a room-temperature platform for various emerging quantum technologies, e.g. the long nuclear spin coherence times as potential quantum memory registers. We demonstrate a freezing protocol for an NV center to isolate its intrinsic nuclear spin from a noisy electromagnetic environment. Any initial state of the nuclear spin can be frozen when the hyperfine-coupled electron and nuclear spins are simultaneously driven with unequal Rabi frequencies. Through numerical simulations, we show that our protocol can effectively shield the nuclear spin from strong drive or noise fields. We also observe a clear suppression of quantum correlations in the frozen nuclear spin regime by measuring the quantum discord of the electron-nuclear spin system. These features can be instrumental in extending the storage times of NV nuclear-spin based quantum memories in hybrid quantum systems.

As large Pre-trained Language Models (PLMs) trained on large amounts of data in an unsupervised manner become more ubiquitous, identifying various types of bias in the text has come into sharp focus. Existing "Stereotype Detection" datasets mainly adopt a diagnostic approach toward large PLMs. Blodgett et. al (2021a) show that there are significant reliability issues with the existing benchmark datasets. Annotating a reliable dataset requires a precise understanding of the subtle nuances of how stereotypes manifest in text. In this paper, we annotate a focused evaluation set for "Stereotype Detection" that addresses those pitfalls by de-constructing various ways in which stereotypes manifest in text. Further, we present a multi-task model that leverages the abundance of data-rich neighboring tasks such as hate speech detection, offensive language detection, misogyny detection, etc., to improve the empirical performance on "Stereotype Detection". We then propose a reinforcement-learning agent that guides the multi-task learning model by learning to identify the training examples from the neighboring tasks that help the target task the most. We show that the proposed models achieve significant empirical gains over existing baselines on all the tasks., Comment: Long paper at ACL 2022 main conference

Recommender systems based on collaborative filtering play a vital role in many E-commerce applications as they guide the user in finding their items of interest based on the user's past transactions and feedback of other similar customers. Data Sparsity is one of the major drawbacks with collaborative filtering technique arising due to the less number of transactions and feedback data. In order to reduce the sparsity problem, techniques called transfer learning/cross-domain recommendation has emerged. In transfer learning methods, the data from other dense domain(s) (source) is considered in order to predict the missing ratings in the sparse domain (target). In this paper, we come up with a novel transfer learning approach for cross-domain recommendation, wherein the cluster-level rating pattern(codebook) of the source domain is obtained via a co-clustering technique. Thereafter we apply the Maximum Margin Matrix factorization (MMMF) technique on the codebook in order to learn the user and item latent features of codebook. Prediction of the target rating matrix is achieved by introducing these latent features in a novel way into the optimisation function. In the experiments we demonstrate that our model improves the prediction accuracy of the target matrix on benchmark datasets.

We present a new direct approach to obtain the Green's function of the Oseen equations, also known as the oseenlet. This approach is different in that it does not assume an initial form of the solution to begin with. The final solution is expressed in a new, simplified form that resembles the Oseen-Burger's tensor of the stokeslet equations, from which the contributions due to inertia are easily appreciable., Comment: 5 pages, 0 figures. A new way to arrive at the hydrodynamic oseenlet solution

Traditional recommendation algorithms develop techniques that can help people to choose desirable items. However, in many real-world applications, along with a set of recommendations, it is also essential to quantify each recommendation's (un)certainty. The conformal recommender system uses the experience of a user to output a set of recommendations, each associated with a precise confidence value. Given a significance level $\varepsilon$, it provides a bound $\varepsilon$ on the probability of making a wrong recommendation. The conformal framework uses a key concept called \emph{nonconformity measure} that measures the strangeness of an item concerning other items. One of the significant design challenges of any conformal recommendation framework is integrating nonconformity measures with the recommendation algorithm. This paper introduces an inductive variant of a conformal recommender system. We propose and analyze different nonconformity measures in the inductive setting. We also provide theoretical proofs on the error-bound and the time complexity. Extensive empirical analysis on ten benchmark datasets demonstrates that the inductive variant substantially improves the performance in computation time while preserving the accuracy., Comment: 25 pages

Recommender systems(RS), especially collaborative filtering(CF) based RS, has been playing an important role in many e-commerce applications. As the information being searched over the internet is rapidly increasing, users often face the difficulty of finding items of his/her own interest and RS often provides help in such tasks. Recent studies show that, as the item space increases, and the number of items rated by the users become very less, issues like sparsity arise. To mitigate the sparsity problem, transfer learning techniques are being used wherein the data from dense domain(source) is considered in order to predict the missing entries in the sparse domain(target). In this paper, we propose a transfer learning approach for cross-domain recommendation when both domains have no overlap of users and items. In our approach the transferring of knowledge from source to target domain is done in a novel way. We make use of co-clustering technique to obtain the codebook (cluster-level rating pattern) of source domain. By making use of hinge loss function we transfer the learnt codebook of the source domain to target. The use of hinge loss as a loss function is novel and has not been tried before in transfer learning. We demonstrate that our technique improves the approximation of the target matrix on benchmark datasets.

Recently, there have been contrary claims of Kitaev spin-liquid behaviour and ordered behavior in the honeycomb compound Ag$_3$LiIr$_2$O$_6$ based on various experimental signatures. Our investigations on this system reveal a low-temperature ordered state with persistent dynamics down to the lowest temperatures. Magnetic order is confirmed by clear oscillations in the muon spin relaxation ($\mu$SR) time spectrum below 9 K till 52 mK. Coincidentally in $^7$Li nuclear magnetic resonance, a wipe-out of the signal is observed below $\sim$ 10 K which again strongly indicates magnetic order in the low temperature regime. This is supported by our density functional theory calculations which show an appreciable Heisenberg exchange term in the spin Hamiltonian that favors magnetic ordering. The $^7$Li shift and spin-lattice relaxation rate also show anomalies at $\sim$ 50 K. They are likely related to the onset of dynamic magnetic correlations, but their origin is not completely clear. Detailed analysis of our $\mu$SR data is consistent with a co-existence of incommensurate N\'eel and striped environments. A significant and undiminished dynamical relaxation rate ($\sim 5$ MHz) as seen in $\mu$SR deep into the ordered phase indicates enhanced quantum fluctuations in the ordered state., Comment: 11 pages, 10 figures

For spin rotational symmetric models with a positive-definite high-temperature expansion of the partition function, a stochastic sampling of the series expansion upon partial resummation becomes logically equivalent to sampling an uncoloured closely-packed loop-gas model in one higher dimension. Based on this, we devise quantum Monte Carlo updates that importance-sample loop configurations for general $SU(N)$ in fundamental and higher-symmetric representations. The algorithmic performance systematically improves with increase in (continuous) $N$ allowing efficient simulation of quantum paramagnets. The underlying reason for the increased efficacy is the correspondence of quantum paramagnetic phases like valence bond solids to short-loop phases on the loop-gas side rather than the particular value of $N$. This also gives a connection between Sandvik's $JQ$ model class and classical loop-gas models in the deconfined universality class., Comment: This version closely matches the published one

We have pointed out the possibility of quantum Hall effect or quantum patterns of transportation in a degenerate strongly magnetized quark matter, which might be expected inside a highly dense compact star. An anisotropic pattern of electrical conductivity and resistivity tensor in classical and quantum cases is explored by considering cyclotron motion and Landau quantization respectively. With increasing magnetic field, classical to quantum transitions are realized through enhanced/reduced resistivity/conductivity with jumping pattern. Considering QCD relaxation time scale of 10 fm, $eB\approx (1-4) m_\pi^2$ might be considered as strong magnetic field for massless and degenerate quark matter with quark chemical potential $\mu\approx 0.2-0.4$ GeV. Beyond these threshold ranges of magnetic field, perpendicular motion of quarks might be stopped and 3 $\rightarrow$ 1 dimensionally reduced conduction picture might be established., Comment: 23 pages, 3 figures

We study the Hall effect in square, planar type-II superconductors using numerical simulations of time dependent Ginzburg-Landau (TDGL) equations. The Hall field in some type-II superconductors displays sign-change behavior at some magnetic fields due to the induced field of vortex flow, when its contribution is strong enough to reverse the field direction. In this work, we use modified TDGL equations which couple an externally applied current, and also incorporate normal-state and flux-flow Hall effects. We obtain the profile of Hall angle as a function of applied magnetic field for four different sizes (l\times l) of the superconductor: l/ \xi belongs to {3, 5, 15, 20}. We obtain vastly different profiles for each size, proving that size is an important parameter that determines Hall behavior. We find that electric field dynamics provides an insight into several anomalous features including signchange of Hall angle, and leads us to the precise transient behavior of order parameter responsible for them., Comment: 8 Pages, 8 Figures

This paper presents a novel two-stage framework to extract opinionated sentences from a given news article. In the first stage, Naive Bayes classifier by utilizing the local features assigns a score to each sentence - the score signifies the probability of the sentence to be opinionated. In the second stage, we use this prior within the HITS (Hyperlink-Induced Topic Search) schema to exploit the global structure of the article and relation between the sentences. In the HITS schema, the opinionated sentences are treated as Hubs and the facts around these opinions are treated as the Authorities. The algorithm is implemented and evaluated against a set of manually marked data. We show that using HITS significantly improves the precision over the baseline Naive Bayes classifier. We also argue that the proposed method actually discovers the underlying structure of the article, thus extracting various opinions, grouped with supporting facts as well as other supporting opinions from the article., Comment: Presented as a talk at TextGraphs-9: the workshop on Graph-based Methods for Natural Language Processing at EMNLP 2014

The observation of Majorana fermions as collective excitations in condensed-matter systems is an ongoing quest, and several state-of-the-art experiments have been performed in the last decade. As a potential avenue in this direction, we simulate the high-harmonic spectrum of Kitaev's superconducting chain model that hosts Majorana edge modes in its topological phase. It is well-known that this system exhibits a topological--trivial superconducting phase transition. We demonstrate that high-harmonic spectroscopy is sensitive to the phase transition in presence of open boundary conditions due to the presence or absence of these edge modes. The population dynamics of the Majorana edge modes are different from the bulk modes, which is the underlying reason for the distinct harmonic profile of both the phases. On the contrary, in presence of periodic boundary conditions with only bulk modes, high-harmonic spectroscopy becomes insensitive to the phase transition with similar harmonic profiles in both phases., Comment: 16 pages, 4 figures

In this paper, we propose a hybrid technique for semantic question matching. It uses our proposed two-layered taxonomy for English questions by augmenting state-of-the-art deep learning models with question classes obtained from a deep learning based question classifier. Experiments performed on three open-domain datasets demonstrate the effectiveness of our proposed approach. We achieve state-of-the-art results on partial ordering question ranking (POQR) benchmark dataset. Our empirical analysis shows that coupling standard distributional features (provided by the question encoder) with knowledge from taxonomy is more effective than either deep learning (DL) or taxonomy-based knowledge alone., Comment: Paper was accepted at COLING 2018, presented as a poster

When evaluating an answer choice for Reading Comprehension task, other answer choices available for the question and the answers of related questions about the same paragraph often provide valuable information. In this paper, we propose a method to leverage the natural language relations between the answer choices, such as entailment and contradiction, to improve the performance of machine comprehension. We use a stand-alone question answering (QA) system to perform QA task and a Natural Language Inference (NLI) system to identify the relations between the choice pairs. Then we perform inference using an Integer Linear Programming (ILP)-based relational framework to re-evaluate the decisions made by the standalone QA system in light of the relations identified by the NLI system. We also propose a multitask learning model that learns both the tasks jointly., Comment: Published at NAACL-HLT 2019. This version has link to code repository. 6 pages, 3 figures & 2 tables

Politicians often have underlying agendas when reacting to events. Arguments in contexts of various events reflect a fairly consistent set of agendas for a given entity. In spite of recent advances in Pretrained Language Models (PLMs), those text representations are not designed to capture such nuanced patterns. In this paper, we propose a Compositional Reader model consisting of encoder and composer modules, that attempts to capture and leverage such information to generate more effective representations for entities, issues, and events. These representations are contextualized by tweets, press releases, issues, news articles, and participating entities. Our model can process several documents at once and generate composed representations for multiple entities over several issues or events. Via qualitative and quantitative empirical analysis, we show that these representations are meaningful and effective., Comment: Accepted at EMNLP 2021 main conference; 16 pages including appendix, 18 figures, 6 tables

In the XY regime of the XXZ Heisenberg model phase diagram, we demonstrate that the origin of magnetically ordered phases is influenced by the presence of solvable points with exact quantum coloring ground states featuring a quantum-classical correspondence. Using exact diagonalization and density matrix renormalization group calculations, for both the square and the triangular lattice magnets, we show that the ordered physics of the solvable points in the extreme XY regime, at $\frac{J_z}{J_\perp}=-1$ and $\frac{J_z}{J_\perp}=-\frac{1}{2}$ respectively with $J_\perp > 0$, adiabatically extends to the more isotropic regime $\frac{J_z}{J_\perp} \sim 1$. We highlight the projective structure of the coloring ground states to compute the correlators in fixed magnetization sectors which enables an understanding of the features in the static spin structure factors and correlation ratios. These findings are contrasted with an anisotropic generalization of the celebrated one-dimensional Majumdar-Ghosh model, which is also found to be (ground state) solvable. For this model, both exact dimer and three-coloring ground states exist at $\frac{J_z}{J_\perp}=-\frac{1}{2}$ but only the two dimer ground states survive for any $\frac{J_z}{J_\perp} > -\frac{1}{2}$.

We report gapless quantum spin liquid behavior in the layered triangular Sr$_{3}$CuSb$_{2}$O$_{9}$ (SCSO) system. X-ray diffraction shows superlattice reflections associated with atomic site ordering into triangular Cu planes well-separated by Sb planes. Muon spin relaxation ($\mu$SR) measurements show that the $S = \frac{1}{2}$ moments at the magnetically active Cu sites remain dynamic down to 65 mK in spite of a large antiferromagnetic exchange scale evidenced by a large Curie-Weiss temperature $\theta_{\mathrm{cw}} \simeq $ -143 K as extracted from the bulk susceptibility. Specific heat measurements also show no sign of long-range order down to 0.35 K. The magnetic specific heat ($\mathit{C}$$_{\mathrm{m}}$) below 5 K reveals a $\mathit{C}$$_{\mathrm{m}}$ $=$ $\gamma T$ + $\alpha T$$^{2}$ behavior. The significant $T$$^{2}$ contribution to the magnetic specific heat invites a phenomenology in terms of the so-called Dirac spinon excitations with a linear dispersion. From the low-$T$ specific heat data, we estimate the dominant exchange scale to be $\sim $ 36 K using a Dirac spin liquid ansatz which is not far from the values inferred from microscopic density functional theory calculations ($\sim $ 45 K) as well as high-temperature susceptibility analysis ($\sim$ 70 K). The linear specific heat coefficient is about 18 mJ/mol-K$^2$ which is somewhat larger than for typical Fermi liquids., Comment: 16 pages, 21 figures, including supplementary material. A $S = \frac{1}{2}$ Dirac spin liquid scenario has been put forward to explain the field-dependent specific heat data. Comments are welcome

We study the fully-packed dimer model on the bilayer square lattice with fugacity equal to $z$ ($1$) for inter-layer (intra-layer) dimers, and intra-layer interaction $V$ between neighbouring parallel dimers on any elementary plaquette in either layer. For a range of not-too-large $z> 0$ and repulsive interactions $0< V < V_s$ (with $V_s \approx 2.1$), we demonstrate the existence of a {\em bilayer Coulomb phase} with purely dipolar two-point functions, {\em i.e.}, without the power-law columnar order that characterizes the usual Coulomb phase of square and honeycomb lattice dimer models. The transition line $z_{c}(V)$ separating this bilayer Coulomb phase from a large-$z$ disordered phase is argued to be in the inverted Kosterlitz-Thouless universality class. Additionally, we argue for the possibility of a tricritical point at which the bilayer Coulomb phase, the large-$z$ disordered phase and the large-$V$ staggered phase meet in the large-$z$, large-$V$ part of the phase diagram. In contrast, for the attractive case with $ V_{cb} < V \leq 0$ ($V_{cb} \approx -1.2$), we argue that any $z > 0$ destroys the power-law correlations of the $z=0$ decoupled layers, and leads immediately to a short-range correlated state, albeit with a slow crossover for small $|V|$. For $V_{c} < V < V_{cb}$ ($V_{c} \approx -1.55$), we predict that any small nonzero $z$ immediately gives rise to long-range {\em bilayer columnar order} although the $z=0$ decoupled layers remain power-law correlated in this regime; this implies a non-monotonic $z$ dependence of the columnar order parameter for fixed $V$ in this regime. This bilayer columnar ordered state is separated from the large-$z$ disordered state by a line of Ashkin-Teller transitions $z_{\rm AT}(V)$., Comment: Minor changes from v2 in response to copy editor's comments. Closely matches published version

We present a combination of thermodynamic and dynamic experimental signatures of a disorder driven dynamic cooperative paramagnet in a 50% site diluted triangular lattice spin-1/2 system, Y$_2$CuTiO$_6$. Magnetic ordering and spin freezing are absent down to 50 mK, far below the Curie Weiss scale of ~-134 K. We observe scaling collapses of the magnetic field- and temperature-dependent magnetic heat capacity and magnetisation data, respectively, in conformity with expectations from the random singlet physics. Our experiments establish the suppression of any freezing scale, if at all present, by more than three orders of magnitude, opening a plethora of interesting possibilities such as disorder-stabilized long range quantum entangled ground states., Comment: 18 pages, 9 figures, published in Physical Review Letters

Singlet fission in tetracene generates two triplet excitons per absorbed photon. If these triplet excitons can be effectively transferred into silicon (Si) then additional photocurrent can be generated from photons above the bandgap of Si. This could alleviate the thermalization loss and increase the efficiency of conventional Si solar cells. Here we show that a change in the polymorphism of tetracene deposited on Si due to air exposure, facilitates triplet transfer from tetracene into Si. Magnetic field-dependent photocurrent measurements confirm that triplet excitons contribute to the photocurrent. The decay of tetracene delayed photoluminescence was used to determine a triplet transfer time of 215 ns and a maximum yield of triplet transfer into Si of ~50 %. Our study suggests that control over the morphology of tetracene during deposition will be of great importance to boost the triplet transfer yield further.

Recently a generic class of three-dimensional band structures was identified that host two-fold line degeneracies meeting at three-fold or triple point degeneracies, which resist the usual topological characterization of isolated point degeneracies as in Dirac/Weyl semimetals. For these so-called ``Nexus" fermions which lie beyond Dirac/Weyl fermions, we lay out several concepts to characterize the wavefunction geometry and spell out its topology. Our approach is based on an understanding of the analyticity properties of Nexus wavefunctions building on a two-dimensional analogue studied recently by us. We use this to write down a homological classification of various Nexus triple point degeneracies in three dimensions., Comment: Closely matches the published version

The long spin-diffusion length, spin-lifetimes and excellent optical absorption coefficient of graphene provide an excellent platform for building opto-electronic devices as well as spin-based logic in a nanometer regime. In this study, by employing density functional theory and its time-dependent version, we provide a detailed analysis of how the size and shape of graphene nanoflakes can be used to alter their magnetic structure and optical properties. As the edges of zigzag graphene nanoribbons are known to align anti-ferromagnetically and armchair nanoribbons are typically non-magnetic, a combination of both in a nanoflake geometry can be used to optimize the ground-state magnetic structure and tailor the exchange coupling decisive for ferro- or anti-ferromagnetic edge magnetism, thereby offering the possibility to optimize the external fields needed to switch magnetic ordering. Most importantly, we show that the magnetic state alters the optical response of the flake leading to the possibility of opto-spintronic applications.

Higher-fold chiral fermions that go beyond two-fold Weyl fermions have recently been reported in crystalline systems. Here, we focus on such excitations in several binary, ternary and quaternary alloys/compounds with CoGe, BiSbPt and KMgBO3 as the representative examples that belong to the crystal space group (SG) 198. We found distinct three-fold, four-fold and six-fold chiral fermions in the bulk via Density Functional computations. We provide general symmetry arguments for the protection of these degeneracies with special emphasis on the four-fold fermions. Our surface spectra simulations show that the size of Fermi arcs resulting from these chiral fermions are large, robust and untouched from the bulk states due to the near absence of trivial bulk Fermi pockets. All these features make these systems -- especially CoGe and KMgBO3 -- promising topological semimetal candidates to realize higher-fold fermions in future photo-emission and transport experiments.

This short note documents some of the pyrochlore magnetization plateaus resulting from the arguments of Pal & Lal that were not mentioned in arxiv:1902.06475v1. These are (layered) "Kagome"-like plateaus that are commensurate with the pyrochlore lattice, including an exact wavefunction for the $\frac{m}{m_s} = \frac{5}{6}$ plateau state., Comment: 1+ page

Two-dimensional Graphene is fascinating because of its unique electronic properties. From a fundamental perspective, one among them is the geometric phase structure near the Dirac points in the Brillouin zone, owing to the SU(2) nature of the Dirac cone wave functions. We ask if there are geometric phase structures in two dimensions which go beyond that of a Dirac cone. Here we write down a family of three-band continuum models of non-interacting fermions which have more intricate geometric phase structures. This is connected to the SU(3) nature of the wavefunctions near three-fold degeneracies. We also give a tight-binding free fermion model on a two-dimensional Graphene-like lattice where the three-fold degeneracies are realized at fine-tuned points. Away from them, we obtain new "three-band" Dirac cone structures with associated non-standard Landau level quantization, whose organization is strongly affected by the non-SU(2) or beyond-Dirac geometric phase structure of the fine-tuned points., Comment: 14 pages, 16 figures. Closely matches the published version

We consider the problem of committee selection from a fixed set of candidates where each candidate has multiple quantifiable attributes. To select the best possible committee, instead of voting for a candidate, a voter is allowed to approve the preferred attributes of a given candidate. Though attribute based preference is addressed in several contexts, committee selection problem with attribute approval of voters has not been attempted earlier. A committee formed on attribute preferences is more likely to be a better representative of the qualities desired by the voters and is less likely to be susceptible to collusion or manipulation. In this work, we provide a formal study of the different aspects of this problem and define properties of weak unanimity, strong unanimity, simple justified representations and compound justified representation, that are required to be satisfied by the selected committee. We show that none of the existing vote/approval aggregation rules satisfy these new properties for attribute aggregation. We describe a greedy approach for attribute aggregation that satisfies the first three properties, but not the fourth, i.e., compound justified representation, which we prove to be NP-complete. Furthermore, we prove that finding a committee with justified representation and the highest approval voting score is NP-complete., Comment: 6 pages