Search

An approach to supervised learning in spiking neural networks is presented using a gradient-free method combined with spike-timing-dependent plasticity for image recognition. The proposed network architecture is scalable to multiple layers, enabling the development of more complex and deeper SNN models. The effectiveness of this method is demonstrated by its application to the MNIST dataset, showing good learning accuracy. The proposed method provides a robust and efficient alternative to the backpropagation-based method in supervised learning.

Despite their proficiency in math tasks, the mechanisms underlying LLMs' mathematical reasoning abilities remain a subject of debate. Recent studies suggest that chain-of-thought (CoT) prompts can bolster mathematical reasoning by encouraging LLMs to employ human-like logical reasoning (System 2), enabling them to excel on the Cognitive Reflection Test (CRT). To assess whether LLMs genuinely possess System 2-like logical reasoning, we introduced targeted modifications to CRT problems. Our findings reveal that, despite the use of CoT prompts, mainstream LLMs, including the latest o1-preview model, continue to exhibit a significant error rate. Further analysis indicates that they predominantly rely on System 1-like intuitive reasoning and pattern matching derived from training data, rather than demonstrating mastery of mathematical thinking. This discovery challenges the prevailing notion that LLMs possess genuine logical reasoning abilities and that CoT can enhance them. Consequently, this work may temper overly optimistic projections regarding LLMs' advancement toward artificial general intelligence.

We present our optical observations and multi-wavelength analysis of the GRB\,200613A detected by \texttt{Fermi} satellite. Time-resolved spectral analysis of the prompt $\gamma$-ray emission was conducted utilizing the Bayesian block method to determine statistically optimal time bins. Based on the Bayesian Information Criterion (BIC), the data generally favor the Band+Blackbody (short as BB) model. We speculate that the main Band component comes from the Blandford-Znajek mechanism, while the additional BB component comes from the neutrino annihilation process. The BB component becomes significant for a low-spin, high-accretion rate black hole central engine, as evidenced by our model comparison with the data. The afterglow light curve exhibits typical power-law decay, and its behavior can be explained by the collision between the ejecta and constant interstellar medium (ISM). Model fitting yields the following parameters: $E_{K,iso} = (2.04^{+11.8}_{-1.50})\times 10^{53}$ erg, $\Gamma_0=354^{+578}_{-217}$, $p=2.09^{+0.02}_{-0.03}$, $n_{18}=(2.04^{+9.71}_{-1.87})\times 10^{2}$ cm$^{-3}$, $\theta_j=24.0^{+6.50}_{-5.54}$ degree, $\epsilon_e=1.66^{+4.09}_{-1.39})\times 10^{-1}$ and $\epsilon_B=(7.76^{+48.5}_{-5.9})\times 10^{-6}$. In addition, we employed the public Python package \texttt{Prospector} perform a spectral energy distribution (SED) modeling of the host galaxy. The results suggest that the host galaxy is a massive galaxy ($\log(M_\ast / M_\odot)=11.75^{+0.10}_{-0.09}$) with moderate star formation rate ($\mbox{SFR}=22.58^{+13.63}_{-7.22} M_{\odot}$/yr). This SFR is consistent with the SFR of $\sim 34.2 M_{\odot}$ yr$^{-1}$ derived from the [OII] emission line in the observed spectrum., Comment: 30 pages, 16 figures, accepted by ApJ

The Einstein Probe (EP) achieved its first detection and localization of a bright X-ray flare, EP240219a, on February 19, 2024, during its commissioning phase. Subsequent targeted searches triggered by the EP240219a alert identified a faint, untriggered gamma-ray burst (GRB) in the archived data of Fermi/GBM, Swift/BAT, Insight-HXMT/HE and INTEGRAL/SPI-ACS. The EP/WXT light curve reveals a long duration of approximately 160 seconds with a slow decay, whereas the Fermi/GBM light curve shows a total duration of approximately 70 seconds. The peak in the Fermi/GBM light curve occurs slightly later with respect to the peak seen in the EP/WXT light curve. Our spectral analysis shows that a single cutoff power-law model effectively describes the joint EP/WXT-Fermi/GBM spectra in general, indicating coherent broad emission typical of GRBs. The model yielded a photon index of $\sim -1.70 \pm 0.05$ and a peak energy of $\sim 257 \pm 134$ keV. After detection of GRB 240219A, long-term observations identified several candidates in optical and radio wavelengths, none of which was confirmed as the afterglow counterpart during subsequent optical and near-infrared follow-ups. The analysis of GRB 240219A classifies it as an X-ray rich GRB with a high peak energy, presenting both challenges and opportunities for studying the physical origins of X-ray flashes (XRFs), X-ray rich GRBs (XRRs), and classical GRBs (C-GRBs). Furthermore, linking the cutoff power-law component to non-thermal synchrotron radiation suggests that the burst is driven by a Poynting flux-dominated outflow., Comment: 14 pages, 8 figures, 3 tables

Motivated by the pressing challenges in the digital twin development for biomanufacturing systems, we introduce an adjoint sensitivity analysis (SA) approach to expedite the learning of mechanistic model parameters. In this paper, we consider enzymatic stochastic reaction networks representing a multi-scale bioprocess mechanistic model that allows us to integrate disparate data from diverse production processes and leverage the information from existing macro-kinetic and genome-scale models. To support forward prediction and backward reasoning, we develop a convergent adjoint SA algorithm studying how the perturbations of model parameters and inputs (e.g., initial state) propagate through enzymatic reaction networks and impact on output trajectory predictions. This SA can provide a sample efficient and interpretable way to assess the sensitivities between inputs and outputs accounting for their causal dependencies. Our empirical study underscores the resilience of these sensitivities and illuminates a deeper comprehension of the regulatory mechanisms behind bioprocess through sensitivities., Comment: 11 pages, 2 figures

Biomanufacturing innovation relies on an efficient Design of Experiments (DoEs) to optimize processes and product quality. Traditional DoE methods, ignoring the underlying bioprocessing mechanisms, often suffer from a lack of interpretability and sample efficiency. This limitation motivates us to create a new optimal learning approach for digital twin model calibration. In this study, we consider the cell culture process multi-scale mechanistic model, also known as Biological System-of-Systems (Bio-SoS). This model with a modular design, composed of sub-models, allows us to integrate data across various production processes. To calibrate the Bio-SoS digital twin, we evaluate the mean squared error of model prediction and develop a computational approach to quantify the impact of parameter estimation error of individual sub-models on the prediction accuracy of digital twin, which can guide sample-efficient and interpretable DoEs., Comment: 11 pages, 5 figures

To support mechanism online learning and facilitate digital twin development for biomanufacturing processes, this paper develops an efficient Bayesian inference approach for partially observed enzymatic stochastic reaction network (SRN), a fundamental building block of multi-scale bioprocess mechanistic model. To tackle the critical challenges brought by the nonlinear stochastic differential equations (SDEs)-based mechanistic model with partially observed state and having measurement errors, an interpretable Bayesian updating linear noise approximation (LNA) metamodel, incorporating the structure information of the mechanistic model, is proposed to approximate the likelihood of observations. Then, an efficient posterior sampling approach is developed by utilizing the gradients of the derived likelihood to speed up the convergence of Markov Chain Monte Carlo (MCMC). The empirical study demonstrates that the proposed approach has a promising performance., Comment: 11 pages, 2 figures

In this paper, we develop a general framework for multicontinuum homogenization in perforated domains. The simulations of problems in perforated domains are expensive and, in many applications, coarse-grid macroscopic models are developed. Many previous approaches include homogenization, multiscale finite element methods, and so on. In our paper, we design multicontinuum homogenization based on our recently proposed framework. In this setting, we distinguish different spatial regions in perforations based on their sizes. For example, very thin perforations are considered as one continua, while larger perforations are considered as another continua. By differentiating perforations in this way, we are able to predict flows in each of them more accurately. We present a framework by formulating cell problems for each continuum using appropriate constraints for the solution averages and their gradients. These cell problem solutions are used in a multiscale expansion and in deriving novel macroscopic systems for multicontinuum homogenization. Our proposed approaches are designed for problems without scale separation. We present numerical results for two continuum problems and demonstrate the accuracy of the proposed methods.

In this paper, we propose a novel multiscale model reduction strategy tailored to address the Poisson equation within heterogeneous perforated domains. The numerical simulation of this intricate problem is impeded by its multiscale characteristics, necessitating an exceptionally fine mesh to adequately capture all relevant details. To overcome the challenges inherent in the multiscale nature of the perforations, we introduce a coarse space constructed using the Constraint Energy Minimizing Generalized Multiscale Finite Element Method (CEM-GMsFEM). This involves constructing basis functions through a sequence of local energy minimization problems over eigenspaces containing localized information pertaining to the heterogeneities. Through our analysis, we demonstrate that the oversampling layers depend on the local eigenvalues, thereby implicating the local geometry as well. Additionally, we provide numerical examples to illustrate the efficacy of the proposed scheme.

By measuring magnetizations of pristine and Ta-doped CsV$_{3}$Sb$_{5}$ single crystals, we have carried out systematic studies on the lower critical field, critical current density, and equilibrium magnetization of this kagome system. The lower critical field has been investigated in the two typical samples, and the temperature dependent lower critical field obtained in Ta-doped sample can be fitted by using the model with two $s$-wave superconducting gaps yielding the larger gap of $2\Delta_{s1}/k_\mathrm{B}T_\mathrm{c}=7.9\;(\pm1.8)$. This indicates a strong-coupling feature of the V-based superconductors. The measured magnetization hysteresis loops allow us to calculate the critical current density, which shows a very weak bulk vortex pinning. The magnetization hysteresis loops measured in these two kinds of samples can be well described by a recently proposed generalized phenomenological model, which leads to the determination of many fundamental parameters for these superconductors. Our systematic results and detailed analysis conclude that this V-based kagome system has features of strong-coupling superconductivity, relatively large Ginzburg-Landau parameter and weak vortex coupling., Comment: 11 pages, 6 figures

Large Language Models (LLMs) have gradually become the gateway for people to acquire new knowledge. However, attackers can break the model's security protection ("jail") to access restricted information, which is called "jailbreaking." Previous studies have shown the weakness of current LLMs when confronted with such jailbreaking attacks. Nevertheless, comprehension of the intrinsic decision-making mechanism within the LLMs upon receipt of jailbreak prompts is noticeably lacking. Our research provides a psychological explanation of the jailbreak prompts. Drawing on cognitive consistency theory, we argue that the key to jailbreak is guiding the LLM to achieve cognitive coordination in an erroneous direction. Further, we propose an automatic black-box jailbreaking method based on the Foot-in-the-Door (FITD) technique. This method progressively induces the model to answer harmful questions via multi-step incremental prompts. We instantiated a prototype system to evaluate the jailbreaking effectiveness on 8 advanced LLMs, yielding an average success rate of 83.9%. This study builds a psychological perspective on the explanatory insights into the intrinsic decision-making logic of LLMs.

The prompt emission mechanism of gamma-ray bursts (GRBs) is still unclear, and the time-resolved spectral analysis of GRBs is a powerful tool for studying their underlying physical processes. We performed a detailed time-resolved spectral analysis of 78 bright long GRB samples detected by Fermi/Gamma-ray Burst Monitor (GBM). A total of 1490 spectra were obtained and their properties were studied using a typical Band-shape model. Firstly, the parameter distribution of the time-resolved spectrum given as follows: the low-energy spectral index $\alpha \sim -0.72$, high-energy spectral index $\beta \sim -2.42$, the peak energy $E_{\rm p} \sim 221.69 \,\rm{keV}$, and the energy flux $F \sim 7.49\times 10^{-6} \rm{\, erg\,cm^{-2}\,s^{-1}}$. More than 80\% of the bursts exhibit the hardest low-energy spectral index $\alpha_{\rm max}$ exceeding the synchrotron limit (-2/3). Secondly, the evolution patterns of $\alpha$ and $E_{\rm p}$ were statistically analyzed. The results show that for multi-pulse GRBs the intensity-tracking pattern is more common than the hard-to-soft pattern in the evolution of both $E_{\rm p}$ and $\alpha$. The hard-to-soft pattern is generally shown in single-pulse GRBs or in the initial pulse of multi-pulse GRBs. Finally, we found a significant positive correlation between $F$ and $E_{\rm p}$, with half of the samples exhibiting a positive correlation between $F$ and $\alpha$. We discussed the spectral evolution of different radiation models. The diversity of spectral evolution patterns indicates that there may be more than one radiation mechanism occurring in the gamma-ray burst radiation process, including photospheric radiation and synchrotron radiation. However, it may also involve only one radiation mechanism, but more complicated physical details need to be considered., Comment: 19 pages, 7 figures, 4 tables, accepted and published in RAA

By measuring the dynamical and conventional magnetization relaxation of the Ba$_{0.64}$K$_{0.36}$Fe$_2$As$_2$ single crystals, we found strong second peak effect on the magnetization hysteresis loops. It is found that there is a kink of magnetization at a field between the valley and maximum magnetization. Interestingly, the magnetization relaxation rate has a deep minimum at the field with the kink, indicating a diminished vortex creep. The relaxation rate at this field is clearly smaller than the so-called universal lower limit of the relaxation rate characterized by ($S_0\approx \ Gi^{1/2}(T/T_\mathrm{c}$). This diminished vortex creep is associated with the origin of the SMP effect and attributed to the strongly hindered flux motion when experiencing the transition from the quasi-ordered to disordered vortex phases., Comment: 5 figures

Dilepton production provides a unique probe of the strong electromagnetic field produced in heavy-ion collisions. To map out the behavior of its transverse momentum broadening, we present a theoretical model based on the equivalent photon approximation, and then we update it to make direct comparisons with the recent experimental measurements. We find that the model calculations can describe well, not only the average transverse momentum squared of $e^{+}e^{-}$ pairs in Au--Au collisions at $\sqrt{s_{\rm NN}}=200$ GeV, but also the acoplanarity of $\mu^{+}\mu^{-}$ pairs in Pb--Pb collisions at$\sqrt{s_{\rm NN}}=5.02$ TeV. Furthermore, the model predictions are also able to reproduce the measured dependencies of the pair mass and the transverse momentum squared., Comment: 10 pages, 9 figures

At leading order in QCD coupling constant, we compute the energy loss per traveling distance of a heavy quark $dE/dz$ from elastic scattering off thermal quarks and gluons at a temperature $T$, including the thermal perturbative description of soft scatterings ($-t<-t^{\ast}$) and a perturbative QCD-based calculation for hard collisions ($-t>-t^{\ast}$). Within this soft-hard factorization model, we find that the full results of $dE/dz$ behaves a mild sensitivity to the intermediate cutoff $t^{\ast}$, supporting the validity of the soft-hard approach within the temperature region of interest. We re-derive the analytic formula for $dE/dz$ in the high-energy approximation, $E_{1}\gg m^{2}_{1}/T$, where $E_{1}$ is the injected heavy quark energy and $m_{1}$ is its mass. It is realized that the soft logarithmic contribution, $dE/dz\propto ln(-t^{\ast}/m^{2}_{D})$, arises from the $t$-channel scattering off thermal partons, while the hard logarithmic term, $dE/dz\propto ln[E_{1}T/(-t^{\ast})]$, stems from the $t$-channel scattering off thermal partons, and the one $dE/dz\propto ln(E_{1}T/m^{2}_{1})$ comes from the $s$- and $u$-channel scattering off gluons. The sum of these contributions cancels the $t^{\ast}$-dependence as observed in the full result. The mass hierarchy is observed $dE/dz(charm)>dE/dz(bottom)$. Our full results are crucial for a better description of heavy quark transport in QCD medium, in particular at low and moderate energy. We also calculate the energy loss by imposing the Einstein's relationship. The related results appear to be systematically larger than that without imposing the Einstein's relationship., Comment: 16 pages, 8 figures

Video moment retrieval (MR) and highlight detection (HD) based on natural language queries are two highly related tasks, which aim to obtain relevant moments within videos and highlight scores of each video clip. Recently, several methods have been devoted to building DETR-based networks to solve both MR and HD jointly. These methods simply add two separate task heads after multi-modal feature extraction and feature interaction, achieving good performance. Nevertheless, these approaches underutilize the reciprocal relationship between two tasks. In this paper, we propose a task-reciprocal transformer based on DETR (TR-DETR) that focuses on exploring the inherent reciprocity between MR and HD. Specifically, a local-global multi-modal alignment module is first built to align features from diverse modalities into a shared latent space. Subsequently, a visual feature refinement is designed to eliminate query-irrelevant information from visual features for modal interaction. Finally, a task cooperation module is constructed to refine the retrieval pipeline and the highlight score prediction process by utilizing the reciprocity between MR and HD. Comprehensive experiments on QVHighlights, Charades-STA and TVSum datasets demonstrate that TR-DETR outperforms existing state-of-the-art methods. Codes are available at \url{https://github.com/mingyao1120/TR-DETR}., Comment: Accepted by AAAI-24

As the leading candidate of quantum error correction codes, surface code suffers from significant overhead, such as execution time. Reducing the circuit's execution time not only enhances its execution efficiency but also improves fidelity. However, finding the shortest execution time is NP-hard. In this work, we study the surface code mapping and scheduling problem. To reduce the execution time of a quantum circuit, we first introduce two novel metrics: Circuit Parallelism Degree and Chip Communication Capacity to quantitatively characterize quantum circuits and chips. Then, we propose a resource-adaptive mapping and scheduling method, named Ecmas, with customized initialization of chip resources for each circuit. Ecmas can dramatically reduce the execution time in both double defect and lattice surgery models. Furthermore, we provide an additional version Ecmas-ReSu for sufficient qubits, which is performance-guaranteed and more efficient. Extensive numerical tests on practical datasets show that Ecmas outperforms the state-of-the-art methods by reducing the execution time by 51.5% on average for double defect model. Ecmas can reach the optimal result in most benchmarks, reducing the execution time by up to 13.9% for lattice surgery model., Comment: 12 pages, Accepted to IEEE/ACM International Symposium on Code Generation and Optimization

We consider the quantum memory assisted quantum state verification task, where an adversary prepare independent multipartite entangled states and send to the local verifiers, who then store several copies in the quantum memory and measure them collectively to make decision. We establish an exact analytic formula for optimizing two-copy state verification, where the verifiers store two copies, and give a globally optimal two-copy strategy for multi-qubit graph states involving only Bell measurements. When the verifiers can store arbitrarily many copies, we present a dimension expansion technique that designs efficient verification strategies for this case, showcasing its application to efficiently verifying GHZ-like states. These strategies become increasingly advantageous with growing memory resources, ultimately approaching the theoretical limit of efficiency. Our findings demonstrate that quantum memories enhance state verification efficiency, sheding light on error-resistant strategies and practical applications of large-scale quantum memory-assisted verification., Comment: 23 pages, 6 figures, comments are welcome!

We present a study of electrical and thermal transport in Weyl semimetal WTe$_2$ down to 0.3 K. The Wiedemann-Franz law holds below 2 K and a downward deviation starts above. The deviation is more pronounced in cleaner samples, as expected in the hydrodynamic picture of electronic transport, where a fraction of electron-electron collisions conserve momentum. Phonons are the dominant heat carriers and their mean-free-path do not display a Knudsen minimum. This is presumably a consequence of weak anharmonicity, as indicated by the temperature dependence of the specific heat. Frequent momentum exchange between phonons and electrons leads to quantum oscillations of the phononic thermal conductivity. Bloch-Gr\"uneisen picture of electron-phonon scattering breaks down at low temperature when Umklapp ph-ph collisions cease to be a sink for electronic flow of momentum. Comparison with semi-metallic Sb shows that normal ph-ph collisions are amplified by anharmonicity. In both semimetals, at cryogenic temperature, e-ph collisions degrade the phononic flow of energy but not the electronic flow of momentum., Comment: 6 pages, 4 figures, Supplemental Materials included

In this study, we investigate the impact of rotation on the thermodynamic characteristics of QCD matter using the three-flavor NJL model. We examine the temperature, quark chemical potential, and angular velocity dependencies of key thermodynamic quantities, such as the trace anomaly, specific heat, speed of sound, angular momentum, and moment of inertia. As the main finding of our analysis, we observe that the speed of sound exhibits a nonmonotonic behavior as the angular velocity changes., Comment: 18 pages, 19 figures

A supermassive black hole can launch a relativistic jet when it violently disrupts a star that passes too close. Such jetted tidal disruption events (TDEs) are rare and unique tools to investigate quiescent supermassive black holes, jet physics, and circumnuclear environment at high redshift. The newly discovered TDE AT2022cmc ($z\sim 1.193$) providing rich multi-band (X-ray, UV, optical, sub-millimeter, and radio) data, has been interpreted as the fourth on-axis jetted TDE. In this work, we constrain the circumnuclear medium (CNM) density profile with both closure relation (CR) test and detailed forward shock model fit with Markov chain Monte Carlo (MCMC) approach to the multi-band (optical, sub-millimeter, and radio) data of AT2022cmc.We find that the CNM density profile of AT2022cmc is $n\propto R^{-k}$ with $k \sim 1.68$, implying a Bondi accretion in history. Furthermore, our model fit result suggests a two-component jet in AT2022cmc, indicating a similar jet physics to well-studied jetted TDE Sw J1644+57., Comment: accepted in ApJ

Exciton-polaritons are composite bosonic quasiparticles arising from the strong coupling of excitonic transitions and optical modes. Exciton-polaritons have triggered wide exploration in the past decades not only due to their rich quantum phenomena such as superfluidity, superconductivity and quantized vortices but also due to their potential applications for unconventional coherent light sources and all-optical control elements. Here, we report the observation of Bose-Einstein condensation of the upper polariton branch in a transferrable WS$_2$ monolayer microcavity. Near the condensation threshold, we observe a nonlinear increase in upper polariton intensity. This sharp increase in intensity is accompanied by a decrease of the linewidth and an increase of the upper polariton temporal coherence, all of which are hallmarks of Bose-Einstein condensation. By simulating the quantum Boltzmann equation, we show that the upper polariton condensation only occurs for a particular range of particle density. We can attribute the creation of Bose condensation of the upper polariton to the following requirements: 1) the upper polariton is more excitonic than the lower one; 2) there is relatively more pumping in the upper branch; and 3) the conversion time from the upper to the lower polariton branch is long compared to the lifetime of the upper polaritons.

Reconstructing interacting hands from monocular images is indispensable in AR/VR applications. Most existing solutions rely on the accurate localization of each skeleton joint. However, these methods tend to be unreliable due to the severe occlusion and confusing similarity among adjacent hand parts. This also defies human perception because humans can quickly imitate an interaction pattern without localizing all joints. Our key idea is to first construct a two-hand interaction prior and recast the interaction reconstruction task as the conditional sampling from the prior. To expand more interaction states, a large-scale multimodal dataset with physical plausibility is proposed. Then a VAE is trained to further condense these interaction patterns as latent codes in a prior distribution. When looking for image cues that contribute to interaction prior sampling, we propose the interaction adjacency heatmap (IAH). Compared with a joint-wise heatmap for localization, IAH assigns denser visible features to those invisible joints. Compared with an all-in-one visible heatmap, it provides more fine-grained local interaction information in each interaction region. Finally, the correlations between the extracted features and corresponding interaction codes are linked by the ViT module. Comprehensive evaluations on benchmark datasets have verified the effectiveness of this framework. The code and dataset are publicly available at https://github.com/binghui-z/InterPrior_pytorch, Comment: Accepted by ICCV2023

Acquiring contact patterns between hands and nonrigid objects is a common concern in the vision and robotics community. However, existing learning-based methods focus more on contact with rigid ones from monocular images. When adopting them for nonrigid contact, a major problem is that the existing contact representation is restricted by the geometry of the object. Consequently, contact neighborhoods are stored in an unordered manner and contact features are difficult to align with image cues. At the core of our approach lies a novel hand-object contact representation called RUPs (Region Unwrapping Profiles), which unwrap the roughly estimated hand-object surfaces as multiple high-resolution 2D regional profiles. The region grouping strategy is consistent with the hand kinematic bone division because they are the primitive initiators for a composite contact pattern. Based on this representation, our Regional Unwrapping Transformer (RUFormer) learns the correlation priors across regions from monocular inputs and predicts corresponding contact and deformed transformations. Our experiments demonstrate that the proposed framework can robustly estimate the deformed degrees and deformed transformations, which makes it suitable for both nonrigid and rigid contact., Comment: Accepted by ICCV2023

Robust obstacle avoidance is one of the critical steps for successful goal-driven indoor navigation tasks.Due to the obstacle missing in the visual image and the possible missed detection issue, visual image-based obstacle avoidance techniques still suffer from unsatisfactory robustness. To mitigate it, in this paper, we propose a novel implicit obstacle map-driven indoor navigation framework for robust obstacle avoidance, where an implicit obstacle map is learned based on the historical trial-and-error experience rather than the visual image. In order to further improve the navigation efficiency, a non-local target memory aggregation module is designed to leverage a non-local network to model the intrinsic relationship between the target semantic and the target orientation clues during the navigation process so as to mine the most target-correlated object clues for the navigation decision. Extensive experimental results on AI2-Thor and RoboTHOR benchmarks verify the excellent obstacle avoidance and navigation efficiency of our proposed method. The core source code is available at https://github.com/xwaiyy123/object-navigation., Comment: 9 pages, 7 figures, 43 references. This paper has been accepted for ACM MM 2023

Large language models (LLMs), such as ChatGPT, have emerged with astonishing capabilities approaching artificial general intelligence. While providing convenience for various societal needs, LLMs have also lowered the cost of generating harmful content. Consequently, LLM developers have deployed semantic-level defenses to recognize and reject prompts that may lead to inappropriate content. Unfortunately, these defenses are not foolproof, and some attackers have crafted "jailbreak" prompts that temporarily hypnotize the LLM into forgetting content defense rules and answering any improper questions. To date, there is no clear explanation of the principles behind these semantic-level attacks and defenses in both industry and academia. This paper investigates the LLM jailbreak problem and proposes an automatic jailbreak method for the first time. We propose the concept of a semantic firewall and provide three technical implementation approaches. Inspired by the attack that penetrates traditional firewalls through reverse tunnels, we introduce a "self-deception" attack that can bypass the semantic firewall by inducing LLM to generate prompts that facilitate jailbreak. We generated a total of 2,520 attack payloads in six languages (English, Russian, French, Spanish, Chinese, and Arabic) across seven virtual scenarios, targeting the three most common types of violations: violence, hate, and pornography. The experiment was conducted on two models, namely the GPT-3.5-Turbo and GPT-4. The success rates on the two models were 86.2% and 67%, while the failure rates were 4.7% and 2.2%, respectively. This highlighted the effectiveness of the proposed attack method. All experimental code and raw data will be released as open-source to inspire future research. We believe that manipulating AI behavior through carefully crafted prompts will become an important research direction in the future., Comment: Serious errors were found in the experiment, which may lead to the overturning of the overall conclusions of the paper

The study of quasi-periodic oscillations (QPOs) and power density spectra (PDS) continuum properties can help shed light on the still illusive emission physics of magnetars and as a window into the interiors of neutron stars using asteroseismology. In this work, we employ a Bayesian method to search for the QPOs in the hundreds of X-ray bursts from SGR J1935+2154 observed by {\it Insight}-HXMT, GECAM and Fermi/GBM from July 2014 to January 2022. Although no definitive QPO signal (significance $>3\sigma$) is detected in individual bursts or the averaged periodogram of the bursts grouped by duration, we identify several bursts exhibiting possible QPO at $\sim$ 40 Hz, which is consistent with that reported in the X-ray burst associated with FRB 200428. We investigate the PDS continuum properties and find that the distribution of the PDS slope in the simple power-law model peaks $\sim$ 2.5, which is consistent with other magnetars but higher than 5/3 commonly seen in gamma-ray bursts. Besides, the distribution of the break frequency in the broken power-law model peaks at $\sim$ 60 Hz. Finally, we report that the power-law index of PDS has an anti-correlation and power-law dependence on the burst duration as well as the minimum variation timescale., Comment: comments welcome

Quantum autoencoder is a quantum neural network model for compressing information stored in quantum states. However, one needs to process information stored in quantum circuits for many tasks in the emerging quantum information technology. In this work, generalizing the ideas of classical and quantum autoencoder, we introduce the model of Quantum Circuit AutoEncoder (QCAE) to compress and encode information within quantum circuits. We provide a comprehensive protocol for QCAE and design a variational quantum algorithm, varQCAE, for its implementation. We theoretically analyze this model by deriving conditions for lossless compression and establishing both upper and lower bounds on its recovery fidelity. Finally, we apply varQCAE to three practical tasks and numerical results show that it can effectively (1) compress the information within quantum circuits, (2) detect anomalies in quantum circuits, and (3) mitigate the depolarizing noise in quantum devices. This suggests that our algorithm is potentially applicable to other information processing tasks for quantum circuits., Comment: 13 pages, 7 figures

Directed grey-box fuzzing (DGF) is a target-guided fuzzing intended for testing specific targets (e.g., the potential buggy code). Despite numerous techniques proposed to enhance directedness, the existing DGF techniques still face challenges, such as taking into account the difficulty of reaching different basic blocks when designing the fitness metric, and promoting the effectiveness of symbolic execution (SE) when solving the complex constraints in the path to the target. In this paper, we propose a directed hybrid fuzzer called HyperGo. To address the challenges, we introduce the concept of path probability and combine the probability with distance to form an adaptive fitness metric called probability-based distance. By combining the two factors, probability-based distance can adaptively guide DGF toward paths that are closer to the target and have more easy-to-satisfy path constraints. Then, we put forward an Optimized Symbolic Execution Complementary (OSEC) scheme to combine DGF and SE in a complementary manner. The OSEC would prune the unreachable branches and unsolvable branches, and prioritize symbolic execution of the seeds whose paths are closer to the target and have more branches that are difficult to be covered by DGF. We evaluated HyperGo on 2 benchmarks consisting of 21 programs with a total of 100 target sites. The experimental results show that HyperGo achieves 38.47$\times$, 30.89$\times$, 28.52$\times$, 106.09$\times$ and 143.22$\times$ speedup compared to AFLGo, AFLGoSy, BEACON, WindRanger, and ParmeSan, respectively in reaching target sites, and 3.44$\times$, 3.63$\times$, 4.10$\times$, 3.26$\times$, and 3.00$\times$ speedup in exposing known vulnerabilities. Moreover, HyperGo discovered 37 undisclosed vulnerabilities from 7 real-world programs., Comment: 16 pages

The minimum variation timescale (MVT) of soft gamma-ray repeaters can be an important probe to estimate the emission region in pulsar-like models, as well as the Lorentz factor and radius of the possible relativistic jet in gamma-ray burst (GRB)-like models, thus revealing their progenitors and physical mechanisms. In this work, we systematically study the MVTs of hundreds of X-ray bursts (XRBs) from SGR J1935+2154 observed by {\it Insight}-HXMT, GECAM and Fermi/GBM from July 2014 to Jan 2022 through the Bayesian Block algorithm. We find that the MVTs peak at $\sim$ 2 ms, corresponding to a light travel time size of about 600 km, which supports the magnetospheric origin in pulsar-like models. The shock radius and the Lorentz factor of the jet are also constrained in GRB-like models. Interestingly, the MVT of the XRB associated with FRB 200428 is $\sim$ 70 ms, which is longer than that of most bursts and implies its special radiation mechanism. Besides, the median of MVTs is 7 ms, shorter than the median MVTs of 40 ms and 480 ms for short GRBs or long GRBs, respectively. However, the MVT is independent of duration, similar to GRBs. Finally, we investigate the energy dependence of MVT and suggest that there is a marginal evidence for a power-law relationship like GRBs but the rate of variation is at least about an order of magnitude smaller. These features may provide an approach to identify bursts with a magnetar origin., Comment: accepted for publication in ApJS

We investigate the elastic photon-proton and photon-photon scattering in a holographic QCD model, focusing on the Regge regime. Considering contributions of the Pomeron and Reggeon exchange, the total and differential cross sections are calculated. While our model involves several parameters, by virtue of the universality of the Pomeron and Reggeon, for most of them the values determined in the preceding study on the proton-proton and proton-antiproton scattering can be employed. Once the two adjustable parameters, the Pomeron-photon and Reggeon-photon coupling constant, are determined with the experimental data of the total cross sections, predicting the both cross sections in a wide kinematic region, from the GeV to TeV scale, becomes possible. We show that the total cross section data can be well described within the model, and our predictions for the photon-proton differential cross section are consistent with the data., Comment: 13 pages, 3 figures, typos corrected. arXiv admin note: text overlap with arXiv:2306.00564

The research of high energy and nuclear physics requires high power accelerators, and the superconducting radio-frequency (SRF) cavity is regarded as their engine. Up to now, the widely used practical and effective material for making the SRF cavity is pure Nb. The key parameter that governs the efficiency and the accelerating field (E_acc) of a SRF cavity is the lower critical field Hc1. Here, we report a significant improvement of Hc1 for a new type of alloy, Nb_{1-x}Y_x fabricated by the arc melting technique. Experimental investigations with multiple tools including x-ray diffraction, scanning electron microscopy, resistivity and magnetization are carried out, showing that the samples have good quality and a 30%-60% enhancement of Hc1. First principle calculations indicate that this improvement is induced by the delicate tuning of a Lifshitz transition of a Nb derivative band near the Fermi energy, which increases the Ginzburg-Landau parameter and Hc1. Our results may trigger a replacement of the basic material and thus a potential revolution for manufacturing the SRF cavity., Comment: 12 pages, 5 figures, 1 table

To investigate whether bullying and psychological conditions are correlated, this study analyzed a survey of primary and secondary school students from Zigong City, Sichuan Province. A total of 95,545 students completed a personal information questionnaire, the Multidimensional Peer-Victimization Scale (MPVS), and eight other scales pertaining to various psychological problems. The data showed that 68,315 (71.5\%) participants experienced school bullying at varying degrees, indicating the prevalence of bullying among adolescents. The chi-square tests revealed a strong correlation between school bullying and psychological conditions. This correlation was further explored through multivariate logistic regression, showing that students who experienced mild bullying had a 3.10 times higher probability of emotional and behavioral problems, 4.06 times higher probability of experiencing prodromal symptoms of mental illness, 4.72 times higher probability of anxiety, 3.28 times higher probability of developing post-traumatic stress disorder (PTSD) , 4.07 times higher probability of poor sleep quality, 3.13 times higher probability of internet addiction, 2.18 times higher probability of poor mental health, and 3.64 times higher probability of depression than students who did not experience bullying. The corresponding probabilities for students who experienced severe bullying were 11.35, 17.35, 18.52, 12.59, 11.67, 12.03, 4.64, and 5.34 times higher, respectively. In conclusion, school bullying and psychological conditions are significantly correlated among primary and secondary school students, and the more severe the bullying, the higher the probability to suffer from psychological problems., Comment: 13 pages, 1 figure, 7 tables

We present a work on low-complexity acoustic scene classification (ASC) with multiple devices, namely the subtask A of Task 1 of the DCASE2021 challenge. This subtask focuses on classifying audio samples of multiple devices with a low-complexity model, where two main difficulties need to be overcome. First, the audio samples are recorded by different devices, and there is mismatch of recording devices in audio samples. We reduce the negative impact of the mismatch of recording devices by using some effective strategies, including data augmentation (e.g., mix-up, spectrum correction, pitch shift), usages of multi-patch network structure and channel attention. Second, the model size should be smaller than a threshold (e.g., 128 KB required by the DCASE2021 challenge). To meet this condition, we adopt a ResNet with both depthwise separable convolution and channel attention as the backbone network, and perform model compression. In summary, we propose a low-complexity ASC method using data augmentation and a lightweight ResNet. Evaluated on the official development and evaluation datasets, our method obtains classification accuracy scores of 71.6% and 66.7%, respectively; and obtains Log-loss scores of 1.038 and 1.136, respectively. Our final model size is 110.3 KB which is smaller than the maximum of 128 KB., Comment: 5 pages, 5 figures, 4 tables. Accepted for publication in the 16th IEEE International Conference on Signal Processing (IEEE ICSP)

It is generally assumed that number of classes is fixed in current audio classification methods, and the model can recognize pregiven classes only. When new classes emerge, the model needs to be retrained with adequate samples of all classes. If new classes continually emerge, these methods will not work well and even infeasible. In this study, we propose a method for fewshot class-incremental audio classification, which continually recognizes new classes and remember old ones. The proposed model consists of an embedding extractor and a stochastic classifier. The former is trained in base session and frozen in incremental sessions, while the latter is incrementally expanded in all sessions. Two datasets (NS-100 and LS-100) are built by choosing samples from audio corpora of NSynth and LibriSpeech, respectively. Results show that our method exceeds four baseline ones in average accuracy and performance dropping rate. Code is at https://github.com/vinceasvp/meta-sc., Comment: 5 pages, 3 figures, 4 tables. Accepted for publication in INTERSPEECH 2023