Search

In this work, we introduce a new iterative quantum algorithm, called Iterative Symphonic Tunneling for Satisfiability problems (IST-SAT), which solves quantum spin glass optimization problems using high-frequency oscillating transverse fields. IST-SAT operates as a sequence of iterations, in which bitstrings returned from one iteration are used to set spin-dependent phases in oscillating transverse fields in the next iteration. Over several iterations, the novel mechanism of the algorithm steers the system toward the problem ground state. We benchmark IST-SAT on sets of hard MAX-3-XORSAT problem instances with exact state vector simulation, and report polynomial speedups over trotterized adiabatic quantum computation (TAQC) and the best known semi-greedy classical algorithm. When IST-SAT is seeded with a sufficiently good initial approximation, the algorithm converges to exact solution(s) in a polynomial number of iterations. Our numerical results identify a critial Hamming radius(CHR), or quality of initial approximation, where the time-to-solution crosses from exponential to polynomial scaling in problem size. By combining IST-SAT with future classical or quantum approximation algorithms, larger gains may be achieved. The mechanism we present in this work thus presents a new path toward achieving quantum advantage in optimization.

Recently, transition metal dichalcogenides (TMD) have attracted a lot of attention because they can host a number of novel phases, including charge density wave and topological superconductivity. This class of compounds is significant from application and fundamental standpoints. Here we report on the synthesis and characterization of Janus TMD compound TiSeS, which is a subclass of TMDs. We observe characters of filamentary superconductivity in TiSeS, which was not reported in earlier studies. Magnetization measurements (ZFCW& FCW) as well as isothermal magnetization indicates towards type-II superconductivity whereas transport measurements lacks of zero resistivity due to possible weak inter-grain connectivity. The observed behavior can be safely attributed to filamentary superconductivity in TiSeS. Further studies need to be carried out to determine the origin of this superconducting phase.

Writing secure code is challenging and so it is expected that, following the release of code-generative AI tools, such as ChatGPT and GitHub Copilot, developers will use these tools to perform security tasks and use security APIs. However, is the code generated by ChatGPT secure? How would the everyday software or security engineer be able to tell? As we approach the next decade we expect a greater adoption of code-generative AI tools and to see developers use them to write secure code. In preparation for this, we need to ensure security-by-design. In this paper, we look back in time to Saltzer & Schroeder's security design principles as they will need to evolve and adapt to the challenges that come with a world of AI-generated code.

Following the recent release of AI assistants, such as OpenAI's ChatGPT and GitHub Copilot, the software industry quickly utilized these tools for software development tasks, e.g., generating code or consulting AI for advice. While recent research has demonstrated that AI-generated code can contain security issues, how software professionals balance AI assistant usage and security remains unclear. This paper investigates how software professionals use AI assistants in secure software development, what security implications and considerations arise, and what impact they foresee on secure software development. We conducted 27 semi-structured interviews with software professionals, including software engineers, team leads, and security testers. We also reviewed 190 relevant Reddit posts and comments to gain insights into the current discourse surrounding AI assistants for software development. Our analysis of the interviews and Reddit posts finds that despite many security and quality concerns, participants widely use AI assistants for security-critical tasks, e.g., code generation, threat modeling, and vulnerability detection. Their overall mistrust leads to checking AI suggestions in similar ways to human code, although they expect improvements and, therefore, a heavier use for security tasks in the future. We conclude with recommendations for software professionals to critically check AI suggestions, AI creators to improve suggestion security and capabilities for ethical security tasks, and academic researchers to consider general-purpose AI in software development., Comment: 20 pages, 2 figures, 3 tables

Radiology Report Generation (R2Gen) demonstrates how Multi-modal Large Language Models (MLLMs) can automate the creation of accurate and coherent radiological reports. Existing methods often hallucinate details in text-based reports that don't accurately reflect the image content. To mitigate this, we introduce a novel strategy, SERPENT-VLM (SElf Refining Radiology RePort GENeraTion using Vision Language Models), which improves the R2Gen task by integrating a self-refining mechanism into the MLLM framework. We employ a unique self-supervised loss that leverages similarity between pooled image representations and the contextual representations of the generated radiological text, alongside the standard Causal Language Modeling objective, to refine image-text representations. This allows the model to scrutinize and align the generated text through dynamic interaction between a given image and the generated text, therefore reducing hallucination and continuously enhancing nuanced report generation. SERPENT-VLM outperforms existing baselines such as LLaVA-Med, BiomedGPT, etc., achieving SoTA performance on the IU X-ray and Radiology Objects in COntext (ROCO) datasets, and also proves to be robust against noisy images. A qualitative case study emphasizes the significant advancements towards more sophisticated MLLM frameworks for R2Gen, opening paths for further research into self-supervised refinement in the medical imaging domain., Comment: 8 pages, 3 figures, 4 tables, Accepted as oral at Clinical NLP workshop at NAACL 2024

We analyze the exclusive two-body nonleptonic decays of $B_s$ meson to ground as well as radially excited $2S$ charmonium state with a light meson $K_s$, induced by the $b\to c\bar{c}d$ transition. Within the framework of relativistic independent quark (RIQ) model based on a flavor-independent interaction potential in scalar-vector harmonic form, we calculate the weak form factors from the overlapping integrals of meson wave function obtained in this model. Using the factorization approximation, we predict the branching fractions for the $B_s \to \psi(1S,2S) K_s$ and $B_s \to \eta_c(1S,2S) K_s$, which can be compared with future theoretical predictions. Branching fraction for $B_s\to J/\psi K_s$ decay is found to be in good agreement with the data from LHCb Collaboration, whereas for $B_s\to \psi(2S) K_s$, it is found to be within the detection ability of the CMS Collaboration. We also predict the ratios of branching fractions $(\cal{R})$, which are in broad agreement with the data from LHCb Collaboration. These results indicate that the present approach works well in the description of exclusive nonleptonic $B_s$ decays within the framework of the RIQ model., Comment: 10 pages; 2 figures. arXiv admin note: text overlap with arXiv:2202.01167

Single crystals of Mn0.03Bi1.97Se3 were synthesized by modified Bridgman technique and phase purity was confirmed via XRD analysis. EDAX analysis has verified the stoichiometric ratio of elements in the sample. Sample flakes were transferred to the SiO2/Si n-type substrate by mechanical exfoliation technique. Four probe gold contacts were etched with the help of e-beam lithography by masking and lift off process. Resistivity measurement was performed in four probe configurations in 2-300 K temperature range. We report evidence for Kon-do effect in Mn0.03Bi1.97Se3 micro-flakes with Tmin of 14.4 K.

Machine learning has shown great promise in addressing several critical hardware security problems. In particular, researchers have developed novel graph neural network (GNN)-based techniques for detecting intellectual property (IP) piracy, detecting hardware Trojans (HTs), and reverse engineering circuits, to name a few. These techniques have demonstrated outstanding accuracy and have received much attention in the community. However, since these techniques are used for security applications, it is imperative to evaluate them thoroughly and ensure they are robust and do not compromise the security of integrated circuits. In this work, we propose AttackGNN, the first red-team attack on GNN-based techniques in hardware security. To this end, we devise a novel reinforcement learning (RL) agent that generates adversarial examples, i.e., circuits, against the GNN-based techniques. We overcome three challenges related to effectiveness, scalability, and generality to devise a potent RL agent. We target five GNN-based techniques for four crucial classes of problems in hardware security: IP piracy, detecting/localizing HTs, reverse engineering, and hardware obfuscation. Through our approach, we craft circuits that fool all GNNs considered in this work. For instance, to evade IP piracy detection, we generate adversarial pirated circuits that fool the GNN-based defense into classifying our crafted circuits as not pirated. For attacking HT localization GNN, our attack generates HT-infested circuits that fool the defense on all tested circuits. We obtain a similar 100% success rate against GNNs for all classes of problems., Comment: To appear in USENIX Security Symposium, 2024

Kagome metals gain attention as they manifest a spectrum of quantum phenomena, including superconductivity, charge order, frustrated magnetism, and intertwined correlated states of condensed matter. With regard to electronic band structure, several of the them exhibit non-trivial topological characteristics. Here, we present a thorough investigation on the growth and the physical properties of single crystals of Ni3In2S2 which is established to be a Dirac nodal line Kagome metal. Extensive characterization is attained through temperature and field-dependent resistivity, angle-dependent magnetoresistance and specific heat measurements. In most metals, the Fermi liquid behaviour is mostly restricted to a narrow range of temperature. In Ni3In2S2, this characteristic feature has been observed for an extensive temperature range of 82 K. This is attributed to the strong electron-electron correlation in the material. Specific heat measurements reveal a high Kadowaki-Woods ratio which is in good agreement with strongly correlated systems. Almost linear positive magnetoresistance follows the conventional Kohler scaling which depicts the applicability of semi-classical theories. The angle-dependent magneto-resistance been explained using the Voigt-Thomson formula. Furthermore, de-Haas van Alphen oscillations are observed in magnetization vs. magnetic field measurement which shed light on the topological features in the Shandite Ni3In2S2.

Table understanding capability of Large Language Models (LLMs) has been extensively studied through the task of question-answering (QA) over tables. Typically, only a small part of the whole table is relevant to derive the answer for a given question. The irrelevant parts act as noise and are distracting information, resulting in sub-optimal performance due to the vulnerability of LLMs to noise. To mitigate this, we propose CABINET (Content RelevAnce-Based NoIse ReductioN for TablE QuesTion-Answering) - a framework to enable LLMs to focus on relevant tabular data by suppressing extraneous information. CABINET comprises an Unsupervised Relevance Scorer (URS), trained differentially with the QA LLM, that weighs the table content based on its relevance to the input question before feeding it to the question-answering LLM (QA LLM). To further aid the relevance scorer, CABINET employs a weakly supervised module that generates a parsing statement describing the criteria of rows and columns relevant to the question and highlights the content of corresponding table cells. CABINET significantly outperforms various tabular LLM baselines, as well as GPT3-based in-context learning methods, is more robust to noise, maintains outperformance on tables of varying sizes, and establishes new SoTA performance on WikiTQ, FeTaQA, and WikiSQL datasets. We release our code and datasets at https://github.com/Sohanpatnaik106/CABINET_QA., Comment: Accepted at ICLR 2024 (spotlight)

In this paper we determine a sufficient condition for the quasinilpotency of a commutator of compact operators via block-tridiagonal matrix form associated with a compact operator. We also prove that every compact operator is unitarily equivalent to the sum of a compact quasinilpotent operator and a triangularizable compact operator., Comment: 10 pages

In this work, we investigate angular observables such as the longitudinal polarization of charged leptons, $\tau$-polarization, and forward-backward asymmetry in semileptonic $B_c$ decays. Additionally, we provide predictions for lepton flavor violating observables, the $\mathcal{R}$ ratios in the decay channels $B_c \rightarrow \eta_c (J/\psi) l \nu_l$ and $B_c \rightarrow D (D^*) l \nu_l$ across the entire $q^2$ region. Our analysis is conducted within the Relativistic Independent Quark Model, focusing on the potential model-dependent aspects of these observables. We compare our model predictions with existing lattice predictions, highlighting the strong applicability of our framework in describing $B_c$ decays. Considering the forthcoming experimental upgrades and the Run 3 data results on $B_c$ meson decays, rapid confirmation of these quantities could indicate significant discoveries of physics beyond the Standard Model. This will open up new avenues for understanding the complex flavor dynamics in heavy meson decays., Comment: Accepted version in Physical Review D

In AI research, the optimization of Large Language Models (LLMs) remains a significant challenge, crucial for advancing the field's practical applications and sustainability. Building upon the foundational work of Professor Song Han's lab at MIT, this paper introduces a novel approach in developing Mini-GPTs via contextual pruning. Our methodology strategically prunes the computational architecture of traditional LLMs, like Phi-1.5, focusing on retaining core functionalities while drastically reducing model sizes. We employ the technique across diverse and complex datasets, including US law, Medical Q&A, Skyrim dialogue, English-Taiwanese translation, and Economics articles. The results underscore the efficiency and effectiveness of contextual pruning, not merely as a theoretical concept but as a practical tool in developing domain-specific, resource-efficient LLMs. Contextual pruning is a promising method for building domain-specific LLMs, and this research is a building block towards future development with more hardware compute, refined fine-tuning, and quantization., Comment: 7 pages, 4 figures, Neurips 2023 styling

A canonical feature of the constraint satisfaction problems in NP is approximation hardness, where in the worst case, finding sufficient-quality approximate solutions is exponentially hard for all known methods. Fundamentally, the lack of any guided local minimum escape method ensures both exact and approximate classical approximation hardness, but the equivalent mechanism(s) for quantum algorithms are poorly understood. For algorithms based on Hamiltonian time evolution, we explore this question through the prototypically hard MAX-3-XORSAT problem class. We conclude that the mechanisms for quantum exact and approximation hardness are fundamentally distinct. We review known results from the literature, and identify mechanisms that make conventional quantum methods (such as Adiabatic Quantum Computing) weak approximation algorithms in the worst case. We construct a family of spectrally filtered quantum algorithms that escape these issues, and develop analytical theories for their performance. We show that, for random hypergraphs in the approximation-hard regime, if we define the energy to be $E = N_{\mathrm{unsat}}-N_{\mathrm{sat}}$, spectrally filtered quantum optimization will return states with $E \leq q_m E_{\mathrm{GS}}$ (where $E_{\rm GS}$ is the ground state energy) in sub-quadratic time, where conservatively, $q_m \simeq 0.59$. This is in contrast to $q_m \to 0$ for the hardest instances with classical searches. We test all of these claims with extensive numerical simulations. We do not claim that this approximation guarantee holds for all possible hypergraphs, though our algorithm's mechanism can likely generalize widely. These results suggest that quantum computers are more powerful for approximate optimization than had been previously assumed., Comment: NOTE: arxiv METADATA abstract abridged compared to the manuscript; 43 pages, 18 figures, 1 table

The magnetization control problem for the Landau-Lifshitz-Gilbert (LLG) equation $m_t= m \times (\Delta m +u)- m \times (m \times (\Delta m +u)),\ (x,t) \in \Omega\times (0,T] $ with zero Neumann boundary data on a two-dimensional bounded domain $\Omega$ is studied when the control energy $u$ is applied on the effective field. First, we show the existence of a weak solution, and the magnetization vector field $m$ satisfies an energy inequality. If a weak solution $m$ obeys the condition that $\nabla m\in L^4(0,T;L^4(\Omega)),$ then we show that it is a regular solution. The classical cost functional is modified by incorporating $L^4(0,T;L^4(\Omega))$-norm of $\nabla m$ so that a rigorous study of the optimal control problem is established. Then, we justified the existence of an optimal control and derived first-order necessary optimality conditions using an adjoint problem approach. We have established the continuous dependency and Fr\'echet differentiability of the control-to-state and control-to-costate operators and shown the Lipschitz continuity of their Fr\'echet derivatives. Using these postulates, we derived a local second-order sufficient optimality condition when a control belongs to a critical cone. Finally, we also obtain another remarkable global optimality condition posed only in terms of the adjoint state associated with the control problem.

The exponential suppression of macroscopic quantum tunneling (MQT) in the number of elements to be reconfigured is an essential element of broken symmetry phases. This suppression is also a core bottleneck in quantum algorithms, such as traversing an energy landscape in optimization, and adiabatic state preparation more generally. In this work, we demonstrate exponential acceleration of MQT through Floquet engineering with the application of a uniform, high frequency transverse drive field. Using the ferromagnetic phase of the transverse field Ising model in one and two dimensions as a prototypical example, we identify three phenomenological regimes as a function of drive strength. For weak drives, the system exhibits exponentially decaying tunneling rates but robust magnetic order; in the crossover regime at intermediate drive strength, we find polynomial decay of tunnelling alongside vanishing magnetic order; and at very strong drive strengths both the Rabi frequency and time-averaged magnetic order are approximately constant with increasing system size. We support these claims with extensive full wavefunction and tensor network numerical simulations, and theoretical analysis. An experimental test of these results presents a technologically important and novel scientific question accessible on NISQ-era quantum computers., Comment: 4.5+7 pages, 10 figs, improved presentation throughout

Evaluating the risk level of adversarial images is essential for safely deploying face authentication models in the real world. Popular approaches for physical-world attacks, such as print or replay attacks, suffer from some limitations, like including physical and geometrical artifacts. Recently, adversarial attacks have gained attraction, which try to digitally deceive the learning strategy of a recognition system using slight modifications to the captured image. While most previous research assumes that the adversarial image could be digitally fed into the authentication systems, this is not always the case for systems deployed in the real world. This paper demonstrates the vulnerability of face authentication systems to adversarial images in physical world scenarios. We propose AdvGen, an automated Generative Adversarial Network, to simulate print and replay attacks and generate adversarial images that can fool state-of-the-art PADs in a physical domain attack setting. Using this attack strategy, the attack success rate goes up to 82.01%. We test AdvGen extensively on four datasets and ten state-of-the-art PADs. We also demonstrate the effectiveness of our attack by conducting experiments in a realistic, physical environment., Comment: 10 pages, 9 figures, Accepted to the International Joint Conference on Biometrics (IJCB 2023)

Choosing the right person for the right job makes the personnel interview process a cognitively demanding task. Psychometric tests, followed by an interview, have often been used to aid the process although such mechanisms have their limitations. While psychometric tests suffer from faking or social desirability of responses, the interview process depends on the way the responses are analyzed by the interviewers. We propose the use of behaviometry as an assistive tool to facilitate an objective assessment of the interviewee without increasing the cognitive load of the interviewer. Behaviometry is a relatively little explored field of study in the selection process, that utilizes inimitable behavioral characteristics like facial expressions, vocalization patterns, pupillary reactions, proximal behavior, body language, etc. The method analyzes thin slices of behavior and provides unbiased information about the interviewee. The current study proposes the methodology behind this tool to capture facial expressions, in terms of facial asymmetry and micro-expressions. Hemi-facial composites using a structural similarity index was used to develop a progressive time graph of facial asymmetry, as a test case. A frame-by-frame analysis was performed on three YouTube video samples, where Structural similarity index (SSID) scores of 75% and more showed behavioral congruence. The research utilizes open-source computer vision algorithms and libraries (python-opencv and dlib) to formulate the procedure for analysis of the facial asymmetry.

We report on the structural, electrical and magnetic measurements in as-grown polycrystalline samples of Pb10-xCux(PO4)6O. This compound has been recently reported to be a room temperature superconductor. Our as-grown specimen has excellent XRD matching with the original submission of Lee et al. This sample has 1.5% of Cu2S as an impurity phase. A resistive transition around 380 K, possibly corresponding to structural transitions of Cu2S, is observed. No evidence of superconducting to normal state transitions in I-V characteristics at room temperature is obtained. Magnetization measurements show linear diamagnetic behavior that cannot be associated to the superconducting state. Hall measurements provide evidence of hole doping through Cu substitution. In summary, we find no evidence for room temperature ambient pressure superconductivity in Cu doped lead apatite Pb9Cu(PO4)6O.

The beta Mn type Co-Zn-Mn alloys have seized significant attention due to their ability to host skyrmions at room temperature. Here we analyse the unconventional magneto-transport properties of Co8Zn8Mn4 single crystals with a Curie temperature of 275 K. A negative magnetoresistance is obtained over a wide temperature range of 50K to 300K. The deviation of the isothermal magnetoresistance (MR) curves from linearity to non-linearity as one approaches higher temperatures points towards the transition from the dominance of magnons to spin fluctuations. In the paramagnetic phase, the change in the shape of the MR curve has been explained using the Khosla and Fischer model. The relationship between the anomalous Hall effect (AHE) and longitudinal resistivity reveals the dominance of the skew-scattering mechanism, which is inexplicable based on the theories of semi-classical magneto-transport. We experimentally determine that the spin fluctuation is the source of the skew-scattering mechanism in Co8Zn8Mn4. In general skew-scattering mechanisms predominate in compounds with high conductivity, but our findings demonstrate that this is not always the case and that other aspects also require equal consideration. Our work throws new light on the predominant scattering mechanism in chiral magnets with skyrmionics phase at low conductivity.