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The low-orbit mega-constellation network (LMCN) is an important part of the space-air-ground integrated network system. An effective satellite-ground interconnection design can result in a stable constellation topology for LMCNs. A naive solution is accessing the satellite with the longest remaining service time (LRST), which is widely used in previous designs. The Coordinated Satellite-Ground Interconnecting (CSGI), the state-of-the-art algorithm, coordinates the establishment of ground-satellite links (GSLs). Compared with existing solutions, it reduces latency by 19% and jitter by 70% on average. However, CSGI only supports the scenario where terminals access only one satellite and cannot fully utilize the multi-access capabilities of terminals. Additionally, CSGI's high computational complexity poses deployment challenges. To overcome these problems, we propose the Classification-based Longest Remaining Service Time (C-LRST) algorithm. C-LRST supports the actual scenario with multi-access capabilities. It adds optional paths during routing with low computational complexity, improving end-to-end communications quality. We conduct our 1000s simulation from Brazil to Lithuania on the open-source platform Hypatia. Experiment results show that compared with CSGI, C-LRST reduces the latency and increases the throughput by approximately 60% and 40%, respectively. In addition, C-LRST's GSL switching number is 14, whereas CSGI is 23. C-LRST has better link stability than CSGI., Comment: 13 pages, 14 figures

Optical detection of magnetic resonance using quantum spin sensors (QSS) provides a spatially local and sensitive technique to probe spin dynamics in magnets. However, its utility as a probe of antiferromagnetic resonance (AFMR), wherein the characteristic resonant frequencies substantially exceed the QSS probing frequency, remains an open question. Here, using the nitrogen-vacancy center in diamond as a QSS, we report the first experimental demonstration of optically detected AFMR in layered van der Waals antiferromagnets up to frequencies of 24 GHz, significantly higher than the sensor's spin resonance frequency of 2.87 GHz. To achieve this, we leverage the enhancement of the QSS spin relaxation rate due to low-frequency magnetic field fluctuations that arise from collective late-time dynamics of finite-wavevector magnons excited by the driven uniform AFMR mode. Using optically detected AFMR, we first characterize the temperature and magnetic field dependence on the AFMR modes, which shed light on the intrinsic exchange fields and magnetic anisotropies. Second, we exploit the highly localized sensitivity of the QSS to demonstrate efficient magnon transport over tens of micrometers. Finally, we find that optical detection efficiency in fact increases with increasing frequency, enabling broadband detection of magnetization dynamics. Our work showcases the dual capabilities of QSS as detectors of both high frequency magnetization dynamics and magnon transport, paving the way for understanding and controlling magnetism in Neel states in antiferromagnets.

Role-Playing Agents (RPAs) have shown remarkable performance in various applications, yet they often struggle to recognize and appropriately respond to hard queries that conflict with their role-play knowledge. To investigate RPAs' performance when faced with different types of conflicting requests, we develop an evaluation benchmark that includes contextual knowledge conflicting requests, parametric knowledge conflicting requests, and non-conflicting requests to assess RPAs' ability to identify conflicts and refuse to answer appropriately without over-refusing. Through extensive evaluation, we find that most RPAs behave significant performance gaps toward different conflict requests. To elucidate the reasons, we conduct an in-depth representation-level analysis of RPAs under various conflict scenarios. Our findings reveal the existence of rejection regions and direct response regions within the model's forwarding representation, and thus influence the RPA's final response behavior. Therefore, we introduce a lightweight representation editing approach that conveniently shifts conflicting requests to the rejection region, thereby enhancing the model's refusal accuracy. The experimental results validate the effectiveness of our editing method, improving RPAs' refusal ability of conflicting requests while maintaining their general role-playing capabilities.

A significant limitation of wide-bandgap materials is their low hole mobility related to localized holes with heavy effective masses ($m_h^*$). We identify in low-symmetric wide-bandgap compounds an anion-anion antibonding coupling (AAAC) effect as the intrinsic factor behind hole localization, which explains the extremely heavy $m_h^*$ and self-trapped hole (STH) formation observed in gallium oxide ($\beta$-$Ga_{2}O_{3}$). We propose a design principle for achieving light holes by manipulating AAAC, demonstrating that specific strain conditions can reduce $m_h^*$ in $\beta$-$Ga_{2}O_{3}$ from 4.77 $m_0$ to 0.38 $m_0$, making it comparable to the electron mass (0.28 $m_0$), while also suppressing STH. The light holes show significant anisotropy, potentially enabling two-dimensional transport in bulk material. This study provides a fundamental understanding of hole mass enhancement and STH formation in novel wide-bandgap materials and suggest new pathways for engineering hole mobilities., Comment: 22 pages, 1 table, 5 figures

UWB ranging systems have been adopted in many critical and security sensitive applications due to its precise positioning and secure ranging capabilities. We present a practical jamming attack, namely UWBAD, against commercial UWB ranging systems, which exploits the vulnerability of the adoption of the normalized cross-correlation process in UWB ranging and can selectively and quickly block ranging sessions without prior knowledge of the configurations of the victim devices, potentially leading to severe consequences such as property loss, unauthorized access, or vehicle theft. UWBAD achieves more effective and less imperceptible jamming due to: (i) it efficiently blocks every ranging session by leveraging the field-level jamming, thereby exerting a tangible impact on commercial UWB ranging systems, and (ii) the compact, reactive, and selective system design based on COTS UWB chips, making it affordable and less imperceptible. We successfully conducted real attacks against commercial UWB ranging systems from the three largest UWB chip vendors on the market, e.g., Apple, NXP, and Qorvo. We reported our findings to Apple, related Original Equipment Manufacturers (OEM), and the Automotive Security Research Group, triggering internal security incident response procedures at Volkswagen, Audi, Bosch, and NXP. As of the writing of this paper, the related OEM has acknowledged this vulnerability in their automotive systems and has offered a $5,000 reward as a bounty., Comment: Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security

Backpropagation is a cornerstone algorithm in training neural networks for supervised learning, which uses a gradient descent method to update network weights by minimizing the discrepancy between actual and desired outputs. Despite its pivotal role in propelling deep learning advancements, the biological plausibility of backpropagation is questioned due to its requirements for weight symmetry, global error computation, and dual-phase training. To address this long-standing challenge, many studies have endeavored to devise biologically plausible training algorithms. However, a fully biologically plausible algorithm for training multilayer neural networks remains elusive, and interpretations of biological plausibility vary among researchers. In this study, we establish criteria for biological plausibility that a desirable learning algorithm should meet. Using these criteria, we evaluate a range of existing algorithms considered to be biologically plausible, including Hebbian learning, spike-timing-dependent plasticity, feedback alignment, target propagation, predictive coding, forward-forward algorithm, perturbation learning, local losses, and energy-based learning. Additionally, we empirically evaluate these algorithms across diverse network architectures and datasets. We compare the feature representations learned by these algorithms with brain activity recorded by non-invasive devices under identical stimuli, aiming to identify which algorithm can most accurately replicate brain activity patterns. We are hopeful that this study could inspire the development of new biologically plausible algorithms for training multilayer networks, thereby fostering progress in both the fields of neuroscience and machine learning.

Conventional federated learning primarily aims to secure the privacy of data distributed across multiple edge devices, with the global model dispatched to edge devices for parameter updates during the learning process. However, the development of large language models (LLMs) requires substantial data and computational resources, rendering them valuable intellectual properties for their developers and owners. To establish a mechanism that protects both data and model privacy in a federated learning context, we introduce a method that just needs to distribute a quantized version of the model's parameters during training. This method enables accurate gradient estimations for parameter updates while preventing clients from accessing a model whose performance is comparable to the centrally hosted one. Moreover, we combine this quantization strategy with LoRA, a popular and parameter-efficient fine-tuning method, to significantly reduce communication costs in federated learning. The proposed framework, named \textsc{FedLPP}, successfully ensures both data and model privacy in the federated learning context. Additionally, the learned central model exhibits good generalization and can be trained in a resource-efficient manner.

Low-orbit mega-constellation network, which utilize thousands of satellites to provide a variety of network services and collect a wide range of space information, is a rapidly growing field. Each satellite collects TB-level data daily, including delay-sensitive data used for crucial tasks, such as military surveillance, natural disaster monitoring, and weather forecasting. According to NASA's statement, these data need to be downloaded to the ground for processing within 3 to 5 hours. To reduce the time required for satellite data downloads, the state-of-the-art solution known as CoDld, which is only available for small constellations, uses an iterative method for cooperative downloads via inter-satellite links. However, in LMCN, the time required to download the same amount of data using CoDld will exponentially increase compared to downloading the same amount of data in a small constellation. We have identified and analyzed the reasons for this degradation phenomenon and propose a new satellite data download framework, named Hurry. By modeling and mapping satellite topology changes and data transmission to Time-Expanded Graphs, we implement our algorithm within the Hurry framework to avoid degradation effects. In the fixed data volume download evaluation, Hurry achieves 100% completion of the download task while the CoDld only reached 44% of download progress. In continuous data generation evaluation, the Hurry flow algorithm improves throughput from 11% to 66% compared to the CoDld in different scenarios., Comment: 15 pages, 7 figures

Strong AGN heating provides an alternative means for the disruption of cluster cool cores (CCs) to cluster mergers. In this work we present a systematic Chandra study of a sample of 108 nearby ($z<0.1$) galaxy clusters, to investigate the effect of AGN heating on CCs. About 40% of clusters with small offsets between the BCG and the X-ray centre ($\le50$ kpc) have small CCs. For comparison, 14 of 17 clusters with large offsets have small CCs, which suggests that mergers or sloshing can be efficient in reducing the CC size. Relaxed, small CC clusters generally have weak radio AGNs ($P_{1.4\rm GHz}<10^{23}$ W Hz$^{-1}$), and they show a lack of systems hosting a radio AGN with intermediate radio power ($2\times10^{23}

Reed-Solomon (RS) codes are constructed over a finite field that have been widely employed in storage and communication systems. Many fast encoding/decoding algorithms such as fast Fourier transform (FFT) and modular approach are designed for RS codes to reduce the encoding/decoding complexity defined as the number of XORs involved in the encoding/decoding procedure. In this paper, we present the construction of RS codes over the cyclic polynomial ring $ \mathbb{F}_2[x]/(1+x+\ldots+x^{p-1})$ and show that our codes are maximum distance separable (MDS) codes. Moreover, we propose the FFT and modular approach over the ring that can be employed in our codes for encoding/decoding complexity reduction. We show that our codes have 17.9\% encoding complexity reduction and 7.5\% decoding complexity reduction compared with RS codes over finite field, for $(n,k)=(2048,1984)$.

Spin-mechanical coupling is vital in diverse fields including spintronics, sensing and quantum transduction. Two-dimensional (2D) magnetic materials provide a unique platform for investigating spin-mechanical coupling, attributed to their mechanical flexibility and novel spin orderings. However, studying spin-mechanical coupling in 2D magnets presents challenges in probing mechanical deformation and thermodynamic properties change at nanoscale. Here we use nano opto-electro-mechanical interferometry to mechanically detect the phase transition and magnetostriction effect in multilayer CrSBr, an air-stable antiferromagnets with large magnon-exciton coupling. The transitions among antiferromagnetism, spin-canted ferromagnetism and paramagnetism are visualized by optomechanical frequency anomalies. Nontrivial magnetostriction coefficient 2.3x10^(-5) and magnetoelastic coupling strength on the order of 10^6 J/m^3 have been found. Moreover, we demonstrate the substantial tunability of the magnetoelastic constant by nearly 50% via gate-induced strain. Our findings demonstrate the strong spin-mechanical coupling in CrSBr and paves the way for developing sensitive magnetic sensing and efficient quantum transduction at atomically thin limit.

Excitons, bound electron-hole pairs, influence the optical properties in strongly interacting solid state systems. Excitons and their associated many-body physics are typically most stable and pronounced in monolayer materials. Bulk systems with large exciton binding energies, on the other hand, are rare and the mechanisms driving their stability are still relatively unexplored. Here, we report an exceptionally large exciton binding energy in single crystals of the bulk van der Waals antiferromagnet CrSBr. Utilizing state-of-the-art angle-resolved photoemission spectroscopy and self-consistent ab-initio GW calculations, we present direct spectroscopic evidence that robust electronic and structural anisotropy can significantly amplify the exciton binding energy within bulk crystals. Furthermore, the application of a vertical electric field enables broad tunability of the optical and electronic properties. Our results indicate that CrSBr is a promising material for the study of the role of anisotropy in strongly interacting bulk systems and for the development of exciton-based optoelectronics.

Growth parameters play a significant role in the crystal quality and physical properties of layered materials. Here we present a case study on a van der Waals magnetic NbFeTe2 material. Two different types of polymorphic NbFeTe2 phases, synthesized at different temperatures, display significantly different behaviors in crystal symmetry, electronic structure, electrical transport, and magnetism. While the phase synthesized at low temperature showing behavior consistent with previous reports, the new phase synthesized at high temperature, has completely different physical properties, such as metallic resistivity, long-range ferromagnetic order, anomalous Hall effect, negative magnetoresistance, and distinct electronic structures. Neutron diffraction reveals out-of-plane ferromagnetism below 70K, consistent with the electrical transport and magnetic susceptibility studies. Our work suggests that simply tuning synthetic parameters in a controlled manner could be an effective route to alter the physical properties of existing materials potentially unlocking new states of matter, or even discovering new materials., Comment: 22 Pages, 6 figures

The development of an integrated space-air-ground network (SAGIN) requires sophisticated satellite Internet emulation tools that can handle complex, dynamic topologies and offer in-depth analysis. Existing emulation platforms struggle with challenges like the need for detailed implementation across all network layers, real-time response, and scalability. This paper proposes a digital twin system based on microservices for satellite Internet emulation, namely Plotinus, which aims to solve these problems. Plotinus features a modular design, allowing for easy replacement of the physical layer to emulate different aerial vehicles and analyze channel interference. It also enables replacing path computation methods to simplify testing and deploying algorithms. In particular, Plotinus allows for real-time emulation with live network traffic, enhancing practical network models. The evaluation result shows Plotinus's effective emulation of dynamic satellite networks with real-world devices. Its adaptability for various communication models and algorithm testing highlights Plotinus's role as a vital tool for developing and analyzing SAGIN systems, offering a cross-layer, real-time and scalable digital twin system.

Parameter Efficient Fine-Tuning (PEFT) techniques have drawn significant attention due to their ability to yield competitive results while updating only a small portion of the adjustable parameters. However, existing PEFT methods pose challenges in hyperparameter selection, such as choosing the rank for LoRA or Adapter, or specifying the length of soft prompts. To address these challenges, we propose a novel fine-tuning approach for neural models, named Representation EDiting (RED), which modifies the representations generated at some layers through the application of scaling and biasing operations. While existing PEFT methods still demonstrate over-parameterization that could potentially undermine the generalization ability acquired from pre-training, RED can substantially reduce the number of trainable parameters by a factor of 25, 700 compared to full parameter fine-tuning and by a factor of 32 relative to LoRA. Remarkably, RED achieves results comparable or superior to both full parameter fine-tuning and other PEFT methods. Extensive experiments across various model architectures and scales, including RoBERTa, GPT-2, T5, and LLaMA-2, have demonstrated the effectiveness and efficiency of RED1, thereby positioning it as a promising PEFT strategy for large-scale neural models.

The recent surge in jailbreaking methods has revealed the vulnerability of Large Language Models (LLMs) to malicious inputs. While earlier research has primarily concentrated on increasing the success rates of jailbreaking attacks, the underlying mechanism for safeguarding LLMs remains underexplored. This study investigates the vulnerability of safety-aligned LLMs by uncovering specific activity patterns within the representation space generated by LLMs. Such ``safety patterns'' can be identified with only a few pairs of contrastive queries in a simple method and function as ``keys'' (used as a metaphor for security defense capability) that can be used to open or lock Pandora's Box of LLMs. Extensive experiments demonstrate that the robustness of LLMs against jailbreaking can be lessened or augmented by attenuating or strengthening the identified safety patterns. These findings deepen our understanding of jailbreaking phenomena and call for the LLM community to address the potential misuse of open-source LLMs., Comment: 21 pages, 20 figures, 6 tables

Dust attenuation in star-forming galaxies (SFGs), as parameterized by the infrared excess (IRX $\equiv L_{\rm IR}/L_{\rm UV}$), is found to be tightly correlated with star formation rate (SFR), metallicity and galaxy size, following a universal IRX relation up to $z=3$. This scaling relation can provide a fundamental constraint for theoretical models to reconcile galaxy star formation, chemical enrichment, and structural evolution across cosmic time. We attempt to reproduce the universal IRX relation over $0.1\leq z\leq 2.5$ using the EAGLE hydrodynamical simulations and examine sensitive parameters in determining galaxy dust attenuation. Our findings show that while the predicted universal IRX relation from EAGLE approximately aligns with observations at $z\leq 0.5$, noticeable disparities arise at different stellar masses and higher redshifts. Specifically, we investigate how modifying various galaxy parameters can affect the predicted universal IRX relation in comparison to the observed data. We demonstrate that the simulated gas-phase metallicity is the critical quantity for the shape of the predicted universal IRX relation. We find that the influence of the infrared luminosity and infrared excess is less important while galaxy size has virtually no significant effect. Overall, the EAGLE simulations are not able to replicate some of the observed characteristics between IRX and galaxy parameters of SFGs, emphasizing the need for further investigation and testing for our current state-of-the-art theoretical models., Comment: 19 pages, 15 figures, accepted for publication in MNRAS

Aligning large language models (LLMs) with human preferences is crucial for enhancing their utility in terms of helpfulness, truthfulness, safety, harmlessness, and interestingness. Existing methods for achieving this alignment often involves employing reinforcement learning from human feedback (RLHF) to fine-tune LLMs based on human labels assessing the relative quality of model responses. Nevertheless, RLHF is susceptible to instability during fine-tuning and presents challenges in implementation.Drawing inspiration from the emerging field of representation engineering (RepE), this study aims to identify relevant representations for high-level human preferences embedded in patterns of activity within an LLM, and achieve precise control of model behavior by transforming its representations. This novel approach, denoted as Representation Alignment from Human Feedback (RAHF), proves to be effective, computationally efficient, and easy to implement.Extensive experiments demonstrate the efficacy of RAHF in not only capturing but also manipulating representations to align with a broad spectrum of human preferences or values, rather than being confined to a singular concept or function (e.g. honesty or bias). RAHF's versatility in accommodating diverse human preferences shows its potential for advancing LLM performance.

Electronic topological phases are renowned for their unique properties, where conducting surface states exist on the boundary of an insulating three-dimensional bulk. While the transport response of the surface states has been extensively studied, the response of the topological hinge modes remains elusive. Here, we investigate a layered topological insulator $\alpha$-Bi$_4$Br$_4$, and provide the first evidence for quantum transport in gapless topological hinge states existing within the insulating bulk and surface energy gaps. Our magnetoresistance measurements reveal pronounced h/e periodic (where h denotes Planck's constant and e represents the electron charge) Aharonov-Bohm oscillation. The observed periodicity, which directly reflects the enclosed area of phase-coherent electron propagation, matches the area enclosed by the sample hinges, providing compelling evidence for the quantum interference of electrons circumnavigating around the hinges. Notably, the h/e oscillations evolve as a function of magnetic field orientation, following the interference paths along the hinge modes that are allowed by topology and symmetry, and in agreement with the locations of the hinge modes according to our scanning tunneling microscopy images. Remarkably, this demonstration of quantum transport in a topological insulator can be achieved using a flake geometry and we show that it remains robust even at elevated temperatures. Our findings collectively reveal the quantum transport response of topological hinge modes with both topological nature and quantum coherence, which can be directly applied to the development of efficient quantum electronic devices., Comment: Nature Physics, in press (2023)

Personality plays a pivotal role in shaping human expression patterns, thus regulating the personality of large language models (LLMs) holds significant potential in enhancing the user experience of LLMs. Previous methods either relied on fine-tuning LLMs on specific corpora or necessitated manually crafted prompts to elicit specific personalities from LLMs. However, the former approach is inefficient and costly, while the latter cannot precisely manipulate personality traits at a fine-grained level. To address the above challenges, we have employed a novel Unsupervisedly-Built Personalized Lexicons (UBPL) in a pluggable manner during the decoding phase of LLMs to manipulate their personality traits. UBPL is a lexicon built through an unsupervised approach from a situational judgment test dataset (SJTs4LLM). Users can utilize UBPL to adjust the probability vectors of predicted words in the decoding phase of LLMs, thus influencing the personality expression of LLMs. Extensive experimentation demonstrates the remarkable effectiveness and pluggability of our method for fine-grained manipulation of LLM's personality., Comment: Work in progress

Spiking Neural Networks (SNNs) have emerged as a promising alternative to conventional Artificial Neural Networks (ANNs), demonstrating comparable performance in both visual and linguistic tasks while offering the advantage of improved energy efficiency. Despite these advancements, the integration of linguistic and visual features into a unified representation through spike trains poses a significant challenge, and the application of SNNs to multimodal scenarios remains largely unexplored. This paper presents SpikeCLIP, a novel framework designed to bridge the modality gap in spike-based computation. Our approach employs a two-step recipe: an ``alignment pre-training'' to align features across modalities, followed by a ``dual-loss fine-tuning'' to refine the model's performance. Extensive experiments reveal that SNNs achieve results on par with ANNs while substantially reducing energy consumption across various datasets commonly used for multimodal model evaluation. Furthermore, SpikeCLIP maintains robust image classification capabilities, even when dealing with classes that fall outside predefined categories. This study marks a significant advancement in the development of energy-efficient and biologically plausible multimodal learning systems.

While the surface-bulk correspondence has been ubiquitously shown in topological phases, the relationship between surface and bulk in Landau-like phases is much less explored. Theoretical investigations since 1970s for semi-infinite systems have predicted the possibility of the surface order emerging at a higher temperature than the bulk, clearly illustrating a counterintuitive situation and greatly enriching phase transitions. But experimental realizations of this prediction remain missing. Here, we demonstrate the higher-temperature surface and lower-temperature bulk phase transitions in CrSBr, a van der Waals (vdW) layered antiferromagnet. We leverage the surface sensitivity of electric dipole second harmonic generation (SHG) to resolve surface magnetism, the bulk nature of electric quadrupole SHG to probe bulk spin correlations, and their interference to capture the two magnetic domain states. Our density functional theory calculations show the suppression of ferromagnetic-antiferromagnetic competition at the surface responsible for this enhanced surface magnetism. Our results not only show unexpected, richer phase transitions in vdW magnets, but also provide viable ways to enhance magnetism in their 2D form.

The Hot Universe Baryon Surveyor (HUBS) is a proposed space-based X-ray telescope for detecting X-ray emissions from the hot gas content in our universe. With its unprecedented spatially-resolved high-resolution spectroscopy and large field of view, the HUBS mission will be uniquely qualified to measure the physical and chemical properties of the hot gas in the interstellar medium, the circumgalactic medium, the intergalactic medium, and the intracluster medium. These measurements will be valuable for two key scientific goals of HUBS, namely to unravel the AGN and stellar feedback physics that governs the formation and evolution of galaxies, and to probe the baryon budget and multi-phase states from galactic to cosmological scales. In addition to these two goals, the HUBS mission will also help us solve some problems in the fields of galaxy clusters, AGNs, diffuse X-ray backgrounds, supernova remnants, and compact objects. This paper discusses the perspective of advancing these fields using the HUBS telescope., Comment: 52 pages, 22 figures. Accepted for publication in Science China: Physics, Mechanics and Astronomy

Anisotropy plays a key role in science and engineering. However, the interplay between the material and engineered photonic anisotropies has hardly been explored due to the vastly different length scales. Here we demonstrate a matter-light hybrid system, exciton-polaritons in a 2D antiferromagnet, CrSBr, coupled with an anisotropic photonic crystal (PC) cavity, where the spin, atomic lattice, and photonic lattices anisotropies are strongly correlated, giving rise to unusual properties of the hybrid system and new possibilities of tuning. We show exceptionally strong coupling between engineered anisotropic optical modes and anisotropic excitons in CrSBr, which is stable against excitation densities a few orders of magnitude higher than polaritons in isotropic materials. Moreover, the polaritons feature a highly anisotropic polarization tunable by tens of degrees by controlling the matter-light coupling via, for instance, spatial alignment between the material and photonic lattices, magnetic field, temperature, cavity detuning and cavity quality-factors. The demonstrated system provides a prototype where atomic- and photonic-scale orders strongly couple, opening opportunities of photonic engineering of quantum materials and novel photonic devices, such as compact, on-chip polarized light source and polariton laser.

The Wide Field Survey Telescope (WFST) is a dedicated photometric surveying facility being built jointly by the University of Science and Technology of China and the Purple Mountain Observatory. It is equipped with a 2.5-meter diameter primary mirror, an active optics system, and a mosaic CCD camera with 0.73 gigapixels on the primary focal plane for high-quality image capture over an FOV of 6.5-square-degree. It is anticipated that WFST will be set up at the Lenghu site in the summer of 2023 and begin to observe the northern sky in four optical bands (u, g, r, and i) with a range of cadences, from hourly/daily in the Deep High-Cadence Survey (DHS) program to semiweekly in the Wide-Field Survey (WFS) program, three months later. During a photometric night, a nominal 30 s exposure in the WFS program will reach a depth of 22.27, 23.32, 22.84, and 22.31 (AB magnitudes) in these four bands, respectively, allowing for the detection of a tremendous amount of transients in the low-z universe and a systematic investigation of the variability of Galactic and extragalactic objects. In the DHS program, intranight 90 s exposures as deep as 23 (u) and 24 mag (g), in combination with target of opportunity follow-ups, will provide a unique opportunity to explore energetic transients in demand for high sensitivities, including the electromagnetic counterparts of gravitational wave events, supernovae within a few hours of their explosions, tidal disruption events and fast, luminous optical transients even beyond a redshift of unity. In addition, the final 6-year co-added images, anticipated to reach g=25.8 mag in WFS or 1.5 mags deeper in DHS, will be of fundamental importance to general Galactic and extragalactic science. The highly uniform legacy surveys of WFST will serve as an indispensable complement to those of LSST that monitor the southern sky., Comment: 48 pages

We studied the spectral signature of different components of the Diffuse X-ray Background (DXB), including Local Hot Bubble (LHB), Solar Wind Charge Exchange (SWCX), Galactic Halo, and typically unresolved point sources (galaxies and AGN), in the direction of the Chandra Deep Field South (CDFS) using the 4 Ms XMM-Newton survey and Chandra 4 Ms Source Catalog. In this paper, we present our results showing how the different components contribute to the DXB below 1 keV. In particular, we have found that ~6% of the emission at 3/4 keV (all-sky average value ~ 3$\times10^{-3}$ cm$^{-6}$pc), which is typically associated with Galactic Halo (GH) and Circum-galactic medium (CGM) is, in fact, due to emission from typically unresolved galaxies. We will discuss the effect that this has on our understanding of GH and CGM, and to our understanding of the missing CGM baryons., Comment: 18 pages, 13 figures, 5 tables

Characterizing the structural properties of galaxies in high-redshift protoclusters is key to our understanding of the environmental effects on galaxy evolution in the early stages of galaxy and structure formation. In this study, we assess the structural properties of 85 and 87 Halpha emission-line candidates (HAEs) in the densest regions of two massive protoclusters, BOSS1244 and BOSS1542, respectively, using HST H-band imaging data. Our results show a true pair fraction of 22+-5 (33+-6) percent in BOSS1244 (BOSS1542), which yields a merger rate of 0.41+-0.09 (0.52+-0.04) per Gyr for massive HAEs with log (M_*/M_sun) > 10.3. This rate is 1.8 (2.8) times higher than that of the general fields at the same epoch. Our sample of HAEs exhibits half-light radii and Sersic indices that cover a broader range than field star-forming galaxies. Additionally, about 15 percent of the HAEs are as compact as the most massive (log(M_*/M_sun) > 11) spheroid-dominated population. These results suggest that the high galaxy density and cold dynamical state (i.e., velocity dispersion of <400 km/s) are key factors that drive galaxy mergers and promote structural evolution in the two protoclusters. Our findings also indicate that both the local environment (on group scales) and the global environment play essential roles in shaping galaxy morphologies in protoclusters. This is evident in the systematic differences observed in the structural properties of galaxies between BOSS1244 and BOSS1542., Comment: 20 pages, 5 figures, 1 table. Accepted for publication in MNRAS

Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks, but focus on conversational tasks has been rather limited. This is partly due to the high cost of obtaining non-English conversational data, which results in limited coverage. In this work, we introduce XSGD for cross-lingual alignment pretraining, a parallel and large-scale multilingual conversation dataset that we created by translating the English-only Schema-Guided Dialogue (SGD) dataset (Rastogi et al., 2020) into 105 other languages. XSGD contains approximately 330k utterances per language. To facilitate aligned cross-lingual representations, we develop an efficient prompt-tuning-based method for learning alignment prompts. We also investigate two different classifiers: NLI-based and vanilla classifiers, and test cross-lingual capability enabled by the aligned prompts. We evaluate our model's cross-lingual generalization capabilities on two conversation tasks: slot-filling and intent classification. Our results demonstrate the strong and efficient modeling ability of NLI-based classifiers and the large cross-lingual transfer improvements achieved by our aligned prompts, particularly in few-shot settings. In addition, we highlight the nice results of our approach compared to LLMs such as text-davinci-003 and ChatGPT in both zero-shot and few-shot settings. While LLMs exhibit impressive performance in English, their cross-lingual capabilities in other languages, particularly low-resource languages, are limited., Comment: Accepted to the Finding of the ACL: EACL 2024

In this work, we report multiple intercalation stages and universal Tc enhancement of superconductivity in 1T-SnSe2 through Li and organic molecules cointercalation. We observe significantly increased lattice parameters up to 40 {\AA} and dramatically enlarged interlayer distance up to ~11{\AA} in Li and N,N-dimethylformamide (DMF) cointercalated SnSe2. Well-separated cointercalation stages with different stacking patterns have been discovered by carefully controlled reaction time and concentration of solutions. These cointercalation stages are superconductors showing different superconducting signals. In addition, Li and various organic species such as Acetone, Dimethyl sulfoxide (DMSO) and Tetrahydrofuran (THF) have been cointercalated into SnSe2 crystals, all of which show enhanced superconducting Tc compared to solely Li intercalated SnSe2. Our findings may provide more insight to effectively tune electronic structure of the lamellar structure through organic molecules co-regulation, and open a new strategy to engineer the physical properties of these layered materials by controlling their different intercalation stages., Comment: 21 pages,5 figures

Quasi-one-dimensional electron systems display intrinsic instability towards long-range ordered phases at sufficiently low temperatures. The superconducting orders are of particular interest as they can possess either singlet or triplet pairing symmetry and frequently compete with magnetism. Here we report on muon spin rotation and relaxation ($\mathrm{\mu}$SR) study of Rb$_2$Mo$_3$As$_3$ characterised by one of the highest critical temperatures $T_{\rm c}=10.4\ \mathrm{K}$ among quasi-one-dimensional superconductors. The transverse-field $\mathrm{\mu}$SR signal shows enhanced damping below $T_{\rm c}$ due to the formation of vortex lattice. Comparison of vortex lattice broadening against single gap $s-$, $p-$ and $d-$wave models shows the best agreement for the $s-$wave scenario but with the anomalously small superconducting gap, $\Delta_0$, to $T_{\rm c}$ ratio of $2\Delta_0/k_{\rm B}T_{\rm c}=2.74(1)$. The alternative nodal $p-$wave or $d-$wave scenarios with marginally worse goodness of fit would yield more realistic $2\Delta_0/k_{\rm B}T_{\rm c}=3.50(2)$ and $2\Delta_0/k_{\rm B}T_{\rm c}=4.08(1)$, respectively, and thus they cannot be ruled out when accounting for the superconducting state in Rb$_2$Mo$_3$As$_3$., Comment: 6 pages

The paradigm of classifying three-dimensional (3D) topological insulators into strong and weak ones (STI and WTI) opens the door for the discovery of various topological phases of matter protected by different symmetries and defined in different dimensions. However, in contrast to the vast realization of STIs, very few materials have been experimentally identified as being close to WTI. Even amongst those identified, none exists with topological surface states (TSS) exposed in a global bulk band gap that is stable at all temperatures. Here we report the design and observation of an ideal WTI in a quasi-one-dimensional (quasi-1D) bismuth halide, Bi$_{4}$I$_{1.2}$Br$_{2.8}$ (BIB). Via angle-resolved photoemission spectroscopy (ARPES), we identify that BIB hosts TSS on the (100)$\prime$ side surface in the form of two anisotropic $\pi$-offset Dirac cones (DCs) separated in momentum while topologically dark on the (001) top surface. The ARPES data fully determine a unique side-surface Hamiltonian and thereby identify two pairs of non-degenerate helical saddle points and a series of four Lifshitz transitions. The fact that both the surface Dirac and saddle points are in the global bulk band gap of 195 meV, combined with the small Dirac velocities, nontrivial spin texture, and the near-gap chemical potential, qualifies BIB to be not only an ideal WTI but also a fertile ground for topological many-body physics.