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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: Was accepted to and withdrawn from Findings of EMNLP 2023

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