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With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for effective detection methods. Unlike traditional deepfake audio generation, which often involves multi-step processes culminating in vocoder usage, ALM directly utilizes neural codec methods to decode discrete codes into audio. Moreover, driven by large-scale data, ALMs exhibit remarkable robustness and versatility, posing a significant challenge to current audio deepfake detection (ADD) models. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially construct the Codecfake dataset, an open-source large-scale dataset, including two languages, millions of audio samples, and various test conditions, tailored for ALM-based audio detection. Additionally, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original SAM, we propose the CSAM strategy to learn a domain balanced and generalized minima. Experiment results demonstrate that co-training on Codecfake dataset and vocoded dataset with CSAM strategy yield the lowest average Equal Error Rate (EER) of 0.616% across all test conditions compared to baseline models.

Image aesthetic evaluation is a highly prominent research domain in the field of computer vision. In recent years, there has been a proliferation of datasets and corresponding evaluation methodologies for assessing the aesthetic quality of photographic works, leading to the establishment of a relatively mature research environment. However, in contrast to the extensive research in photographic aesthetics, the field of aesthetic evaluation for paintings and Drawings has seen limited attention until the introduction of the BAID dataset in March 2023. This dataset solely comprises overall scores for high-quality artistic images. Our research marks the pioneering introduction of a multi-attribute, multi-category dataset specifically tailored to the field of painting: Aesthetics of Paintings and Drawings Dataset (APDD). The construction of APDD received active participation from 28 professional artists worldwide, along with dozens of students specializing in the field of art. This dataset encompasses 24 distinct artistic categories and 10 different aesthetic attributes. Each image in APDD has been evaluated by six professionally trained experts in the field of art, including assessments for both total aesthetic scores and aesthetic attribute scores. The final APDD dataset comprises a total of 4985 images, with an annotation count exceeding 31100 entries. Concurrently, we propose an innovative approach: Art Assessment Network for Specific Painting Styles (AANSPS), designed for the assessment of aesthetic attributes in mixed-attribute art datasets. Through this research, our goal is to catalyze advancements in the field of aesthetic evaluation for paintings and drawings, while enriching the available resources and methodologies for its further development and application.

We simulate the the Su-Schrieffer-Heeger (SSH) model using Rydberg-atom synthetic dimensions constructed, in a single atom, from a ladder of six neighboring $n\:^3S_1$ Rydberg states in which adjacent states are coupled with two-photon transitions using microwave fields. Alternating strong/weak tunneling rates, controlled by adjusting the microwave amplitudes, are varied to map out the topological phase transition as a function of the ratio of the tunneling rates. For each ratio, quench dynamics experiments, in which the system is initially prepared in one of the bulk Rydberg states and then subjected to the microwave fields, are performed to measure the population evolution of the system. From the dynamics measurements, we extract the mean chiral displacement and verify that its long-time average value converges towards the system winding number. The topological phase transition is also examined by probing the energy spectrum of the system in steady state and observing the disappearance of the zero-energy edge states. The results show that even a system with as few as six levels can demonstrate the essential characteristics of the SSH Hamiltonian., Comment: 7 pages, 7 figures

This paper develops a generalized framework for identifying causal impacts in a reduced-form manner under kinked settings when agents can manipulate their choices around the threshold. The causal estimation using a bunching framework was initially developed by Diamond and Persson (2017) under notched settings. Many empirical applications of bunching designs involve kinked settings. We propose a model-free causal estimator in kinked settings with sharp bunching and then extend to the scenarios with diffuse bunching, misreporting, optimization frictions, and heterogeneity. The estimation method is mostly non-parametric and accounts for the interior response under kinked settings. Applying the proposed approach, we estimate how medical subsidies affect outpatient behaviors in China.

Telepresence robots can be used to support users to navigate an environment remotely and share the visiting experience with their social partners. Although such systems allow users to see and hear the remote environment and communicate with their partners via live video feed, this does not provide enough awareness of the environment and their remote partner's activities. In this paper, we introduce an awareness framework for collaborative locomotion in scenarios of onsite and remote users visiting a place together. From an observational study of small groups of people visiting exhibitions, we derived four design goals for enhancing the environmental and social awareness between social partners, and developed a set of awareness-enhancing techniques to add to a standard telepresence robot - named TeleAware robot. Through a controlled experiment simulating a guided exhibition visiting task, TeleAware robot showed the ability to lower the workload, facilitate closer social proximity, and improve mutual awareness and social presence compared with the standard one. We discuss the impact of mobility and roles of local and remote users, and provide insights for the future design of awareness-enhancing telepresence robot systems that facilitate collaborative locomotion., Comment: 33 pages, 12 figures

Large language models (LLMs) have shown promising abilities of in-context learning (ICL), adapting swiftly to new tasks with only few-shot demonstrations. However, current few-shot methods heavily depend on high-quality, query-specific demos, which are often lacking. When faced with out-of-demonstration (OOD) queries, methods that rely on hand-crafted demos or external retrievers might fail. To bridge the gap between limited demos and OOD queries, we propose Self-Demos, a novel prompting method that elicits the inherent generalizability in LLMs by query-aware demo generation. The generated demos strategically interpolate between existing demos and the given query, transforming the query from OOD to ID. To evaluate the effectiveness of our approach, we manually constructed OOD-Toolset, a dataset in the tool-using scenario with over 300 real-world APIs and 1000 instances, each consisting of three tool-use cases as demos and an OOD query. Thorough experiments on our dataset and two public math benchmarks have shown that our method can outperform state-of-the-art baselines in the OOD setting. Moreover, we conduct a range of analyses to validate Self-Demos's generalization and provide more insights., Comment: Accepted to NAACL 2024 Findings

This is one of a series of papers which aim towards a classification of edge-transitive maps of which the Euler characteristic and the edge number are coprime. This one carries out the classification work for arc-transitive maps with nonsolvable automorphism groups, which illustrates how the edge number impacts on the Euler characteristic for maps. The classification is involved with the construction of some new and interesting arc-regular maps.

We solve the coupled Dyson-Schwinger equations for quark propagator and quark gluon vertex in the Nambu-Gorkov basis which is widely applied to study the color superconductivity. After considering the non-Abelian feature in the off-diagonal part of quark gluon vertex, we acquire a quark pairing gap in chiral limit above the chiral phase transition temperature $T_c$. The gap persists up to $2-3\,T_c$ and vanishes at higher temperature. Such a quark pairing characterizes the strongly coupled quark gluon plasma phase as a new phase and distinct from the phase with quasi quarks and gluons. Its new features can be disclosed in the heavy ion collision experiments., Comment: 8 pages, 5 figures

In this paper, we set out to construct a set of reference mock galaxy redshift surveys (MGRSs) for the future Chinese Space-station Survey Telescope (CSST) observation, where subsequent survey selection effects can be added and evaluated. This set of MGRSs is generated using the dark matter subhalos extracted from a high-resolution Jiutian $N$-body simulation of the standard $\Lambda$CDM cosmogony with $\Omega_m=0.3111$, $\Omega_{\Lambda}=0.6889$, and $\sigma_8=0.8102$. The simulation has a boxsize of $1~h^{-1} {\rm Gpc}$, and consists of $6144^3$ particles with mass resolution $3.723 \times 10^{8} h^{-1} M_\odot $. In order to take into account the effect of redshift evolution, we first use all 128 snapshots in the Jiutian simulation to generate a light-cone halo/subhalo catalog. Next, galaxy luminosities are assigned to the main and subhalo populations using the subhalo abundance matching (SHAM) method with the DESI $z$-band luminosity functions at different redshifts. Multi-band photometries, as well as images, are then assigned to each mock galaxy using a 3-dimensional parameter space nearest neighbor sampling of the DESI LS observational galaxies and groups. Finally, the CSST and DESI LS survey geometry and magnitude limit cuts are applied to generate the required MGRSs. As we have checked, this set of MGRSs can generally reproduce the observed galaxy luminosity/mass functions within 0.1 dex for galaxies with $L > 10^8 L_\odot$ (or $M_* > 10^{8.5} M_\odot$) and within 1-$\sigma$ level for galaxies with $L < 10^8L_\odot$ (or $M_* < 10^{8.5} M_\odot$). Together with the CSST slitless spectra and redshifts for our DESI LS seed galaxies that are under construction, we will set out to test various slitless observational selection effects in subsequent probes., Comment: 13 pages, 9 figures, accepted for publication in MNRAS

Large language models (LLMs) have demonstrated impressive performance in understanding language and executing complex reasoning tasks. However, LLMs with long context windows have been notorious for their expensive training costs and high inference latency. Even the most advanced models such as GPT-4 and Claude2 often make mistakes when processing inputs of over $100k$ tokens, a phenomenon also known as \textit{lost in the middle}. In this paper, we propose \textsc{LongAgent}, a method based on multi-agent collaboration, which scales LLMs (e.g., LLaMA) to a context of 128K and demonstrates potential superiority in long-text processing compared to GPT-4. In \textsc{LongAgent}, a leader is responsible for understanding user intent and directing team members to acquire information from documents. Due to members' hallucinations, it is non-trivial for a leader to obtain accurate information from the responses of dozens to hundreds of members. To address this, we develop an \textit{inter-member communication} mechanism to resolve response conflicts caused by hallucinations through information sharing. Our experimental results indicate that \textsc{LongAgent} offers a promising alternative for long-text processing. The agent team instantiated with LLaMA-7B achieves significant improvements in tasks such as 128k-long text retrieval, multi-hop question answering, compared to GPT-4.

Large language models (LLMs) have achieved impressive performance in numerous domains but often struggle to process lengthy inputs effectively and efficiently due to limited length generalization and attention's quadratic computational demands. Many sought to mitigate this by restricting the attention window within the pre-trained length. However, these methods introduce new issues such as ignoring the middle context and requiring additional training. To address these problems, we propose LongHeads, a training-free framework that enhances LLM's long context ability by unlocking multi-head attention's untapped potential. Instead of allowing each head to attend to the full sentence, which struggles with generalizing to longer sequences due to out-of-distribution (OOD) issues, we allow each head to process in-distribution length by selecting and attending to important context chunks. To this end, we propose a chunk selection strategy that relies on the inherent correlation between the query and the key representations, efficiently distributing context chunks to different heads. In this way, each head ensures it can effectively process attended tokens within the trained length, while different heads in different layers can collectively process longer contexts. LongHeads works efficiently in linear time, fits seamlessly with many LLMs that use relative positional encoding. LongHeads achieves 100% accuracy at the 128k length on passkey retrieval task, verifying LongHeads's efficacy in extending the usable context window for existing models. We release our code at https://github.com/LuLuLuyi/LongHeads .

Recently, DNA storage has surfaced as a promising alternative for data storage, presenting notable benefits in terms of storage capacity, cost-effectiveness in maintenance, and the capability for parallel replication. Mathematically, the DNA storage process can be conceptualized as an insertion, deletion, and substitution (IDS) channel. Due to the mathematical complexity associated with the Levenshtein distance, creating a code that corrects for IDS remains a challenging task. In this paper, we propose a bottom-up generation approach to grow the required codebook based on the computation of Edit Computational Graph (ECG) which differs from the algebraic constructions by incorporating the Derivative-Free Optimization (DFO) method. Specifically, this approach is regardless of the type of errors. Compared the results with the work for 1-substitution-1-deletion and 2-deletion, the redundancy is reduced by about 30-bit and 60-bit, respectively. As far as we know, our method is the first IDS-correcting code designed using classical Natural Language Process (NLP) techniques, marking a turning point in the field of error correction code research. Based on the codebook generated by our method, there may be significant breakthroughs in the complexity of encoding and decoding algorithms., Comment: 6 pages, 5 figures, this paper is submitted to the 2024 IEEE International Symposium on Information Theory (ISIT 2024)

The striking discovery of high-temperature superconductivity (HTSC) of 80 K in a bilayer nickelate La$_3$Ni$_2$O$_7$ under a moderately high pressure of about 14 GPa ignited a new wave of studying HTSC in nickelates. The properties of the parental phase at ambient pressure may contain key information on basic interactions therein and bosons that may mediate pairing giving birth to superconductivity. Moreover, the bilayer structure of La$_3$Ni$_2$O$_7$ may suggest a distinct minimal model in comparison to cuprate superconductors. Here using X-ray absorption spectroscopy and resonant inelastic X-ray scattering, we studied La$_3$Ni$_2$O$_7$ at ambient pressure, and found that Ni 3$d_{x^2-y^2}$, Ni 3$d_{z^2}$, and ligand oxygen 2$p$ orbitals dominate the low-energy physics with a small charge-transfer energy. Remarkably, well-defined optical-like magnetic excitations were found to soften into a quasi-static spin-density-wave ordering, evidencing the strong electronic correlations and rich magnetic properties. Based on a Heisenberg spin model, we found that the inter-layer effective magnetic superexchange interaction is much larger than the intra-layer ones, and proposed two viable magnetic structures. Our results set the foundation for further exploration of La$_3$Ni$_2$O$_7$ superconductor.

We revisit the relationship between attention mechanisms and large kernel ConvNets in visual transformers and propose a new spatial attention named Large Kernel Convolutional Attention (LKCA). It simplifies the attention operation by replacing it with a single large kernel convolution. LKCA combines the advantages of convolutional neural networks and visual transformers, possessing a large receptive field, locality, and parameter sharing. We explained the superiority of LKCA from both convolution and attention perspectives, providing equivalent code implementations for each view. Experiments confirm that LKCA implemented from both the convolutional and attention perspectives exhibit equivalent performance. We extensively experimented with the LKCA variant of ViT in both classification and segmentation tasks. The experiments demonstrated that LKCA exhibits competitive performance in visual tasks. Our code will be made publicly available at https://github.com/CatworldLee/LKCA.

We take the Dyson-Schwinger Equation approach of QCD for the quark propagator at complex chemical potential to study the QCD phase transition. The phase transition line of the $(2+1)$ flavor QCD matter in the imaginary chemical potential region is computed via a simplified truncation scheme, which curvature is found to be consistent with the one at real chemical potential. Moreover, the computation in the complex chemical potential plane allows us to determine the location of the Lee-Yang edge singularities. We show explicitly that the critical end point coincides with the Lee-Yang edge singularities on the real $\mu_{B}^{}$ axis. We also investigate the scaling behavior of the singularities and discuss the possibility of extrapolating the CEP from a certain range of chemical potential., Comment: 8 pages, 8 figures

The recent discovery of superconductivity in La3Ni2O7-{\delta} under high pressure with a transition temperature around 80 K has sparked extensive experimental and theoretical efforts. Several key questions regarding the pairing mechanism remain to be answered, such as the most relevant atomic orbitals and the role of atomic deficiencies. Here, we develop a new energy-filtered multislice electron ptychography technique, assisted with electron energy loss spectroscopy, to address these critical issues. Oxygen vacancies are directly visualized and are found to primarily occupy the inner apical sites, which have been proposed to be crucial to superconductivity. We precisely determine the nanoscale stoichiometry and its correlation to the oxygen K edge spectra, which reveals a significant inhomogeneity in the oxygen content and electronic structure within the sample. The spectroscopic results also unveil that stoichiometric La3Ni2O7 is in the charge-transfer regime, with holes that are self-doped from Ni sites into O sites. The outer apical oxygen is found to be less relevant to the low-energy physics and can be safely disregarded in theoretical models. These observations will assist in further development and understanding of superconducting nickelate materials. Our imaging technique for quantifying atomic deficiencies can also be widely applied in materials science and condensed matter physics., Comment: 24 pages, 4 figures, and 10 supplementary figures

We propose a method to determine the effective potential of QCD from the gap equation, by introducing the homotopy method between the solutions of the equation of motion. Via this method, the effective potential beyond the bare vertex approximation is obtained, which then generalizes the Cornwall, Jackiw and Tomboulis (CJT) effective potential for the bilocal composite operators. Moreover, the extended effective potential is set to be a function of self energy instead of the propagator, which is the key point for the potential to be bounded from below. We then investigate the extended effective potential in the cases of phase transition of the QCD vacuum with a small current quark mass, and the first-order phase transition of QCD at finite temperature and high baryon chemical potential. In the former case, the effective potential shows as an inflection point at the critical mass where the multiple solutions of the Dyson-Schwinger equation (DSE) vanishes, which is consistent with that obtained by solving the DSE directly. For the latter case, the in-medium properties, such as the latent heat and the difference of trace anomaly, of QCD is obtained.

We present an approach to tame the growth of entanglement during time evolution by tensor network methods. It combines time evolution in the complex plane with a perturbative and controlled reconstruction of correlation functions on the real-time axis. We benchmark our approach on the single impurity Anderson model. Compared to purely real-time evolution, the complex time evolution significantly reduces the required bond dimension to obtain the spectral function. Notably, our approach yields self-energy results with high precision at low frequencies, comparable to numerical renormalization group (NRG) results, and it successfully captures the exponentially small Kondo energy scale.

Large language models (LLMs) have shown great potential as general-purpose AI assistants in various domains. To meet the requirements of different applications, LLMs are often customized by further fine-tuning. However, the powerful learning ability of LLMs not only enables them to acquire new tasks but also makes them susceptible to learning undesired behaviors. For example, even safety-aligned LLMs can be easily fine-tuned into harmful assistants as the fine-tuning data often contains implicit or explicit harmful content. Can we train LLMs on harmful data without learning harmful behaviors? This paper proposes a controllable training framework that makes harmful behaviors unlearnable during the fine-tuning process. Specifically, we introduce ``security vectors'', a few new parameters that can be separated from the LLM, to ensure LLM's responses are consistent with the harmful behavior. Security vectors are activated during fine-tuning, the consistent behavior makes LLM believe that such behavior has already been learned, there is no need to further optimize for harmful data. During inference, we can deactivate security vectors to restore the LLM's normal behavior. The experimental results show that the security vectors generated by 100 harmful samples are enough to prevent LLM from learning 1000 harmful samples, while preserving the ability to learn other useful information., Comment: work in process

We study the QCD equation of state and other thermodynamic observables including the isentropic trajectories and the speed of sound. These observables are of eminent importance for the understanding of experimental results in heavy ion collisions and also provide a QCD input for studies of the timeline of heavy-ion-collisions with hydrodynamical simulations. They can be derived from the quark propagator whose gap equation is solved within a minimal approximation to the Dyson-Schwinger equations of QCD at finite temperature and density. This minimal approximation aims at a combination of computational efficiency and simplification of the truncation scheme while maintaining quantitative precision. This minimal DSE scheme is confronted and benchmarked with results for correlation functions and observables from first principles QCD lattice at vanishing density and quantitative functional approaches at finite density., Comment: 14 pages, 13 figures

We construct the equation of state (EoS) of QCD based on the finite chemical potential information from the functional QCD approaches, with the assistance of the lattice QCD EoS. The obtained EoS is consistent with the up-to-date estimations of the QCD phase diagram, including a phase transition temperature at zero chemical potential of $T=155$ MeV, the curvature of the transition line $\kappa=0.016$ and also a critical end point at $(T,\mu_B)=(118, 600)$ MeV. In specific, the phase diagram mapping is achieved by incorporating the order parameters into the EoS, namely the dynamical quark mass for the chiral phase transition together with the Polyakov loop parameter for the deconfinement phase transition. We also implement the EoS in hydrodynamic simulations to compute the particle yields, ratios and collective flow, and find that our obtained EoS agrees well with the commonly used one based on the combination of lattice QCD simulation and hadron resonance gas model., Comment: 8 pages, 12 figures

Smart glasses that support augmented reality (AR) have the potential to become the consumer's primary medium of connecting to the future internet. For the best quality of user experience, AR glasses must have a small form factor and long battery life, while satisfying the data rate and latency requirements of AR applications. To extend the AR glasses' battery life, the computation and processing involved in AR may be offloaded to a companion device, such as a smartphone, through a wireless connection. Sidelink (SL), i.e., the D2D communication interface of 5G NR, is a potential candidate for this wireless link. In this paper, we use system-level simulations to analyze the feasibility of NR SL for supporting AR. Our simulator incorporates the PHY layer structure and MAC layer resource scheduling of 3GPP SL, standard 3GPP channel models, and MCS configurations. Our results suggest that the current SL standard specifications are insufficient for high-end AR use cases with heavy interaction but can support simpler previews and file transfers. We further propose two enhancements to SL resource allocation, which have the potential to offer significant performance improvements for AR applications., Comment: 7 pages, 7 figures, accepted for publication in 2023 IEEE Global Communications Conference: Mobile and Wireless Networks (Globecom 2023 MWN), Kuala Lumpur, Malaysia, Dec. 2023

A large primordial lepton asymmetry is capable of explaining the baryon asymmetry of the Universe (BAU) through suppression of the electroweak sphaleron rates (``sphaleron freeze-in") which can lead to a first-order cosmic QCD transition with an observable gravitational wave (GW) signal. With next-to-leading order dimensional reduction and the exact 1-loop fluctuation determinant, we accurately compute the lepton asymmetry needed to realize this paradigm, finding it to be an order of magnitude smaller than previous estimates. Further, we apply an improved QCD equation of state capable of describing the phase transition line together with the critical endpoint leading to better agreement with lattice and functional QCD results. Based on this, we identify the range of lepton flavor asymmetries inducing a first-order cosmic QCD transition. We then extract the parameters relevant to the prediction of GW signal from a first-order cosmic QCD transition. Our result showcases the possibility of probing the sphaleron freeze-in paradigm as an explanation of BAU by future gravitational wave experiments like $\mu$Ares., Comment: 7 pages, 3 figures

Motivated by the recent discovery of superconductivity in La$_3$Ni$_2$O$_7$ under high pressure, we explore its potential charge and spin instabilities through combined model analysis and first-principles calculations. Taking into account the negative charge-transfer nature of high valence nickel, a fully correlated two-cluster model identifies a lattice-coupled rocksalt-type charge instability characterized by substantial fluctuations of oxygen holes. This instability is corroborated by density-functional-theory plus $U$ calculations that also reveal a strong tendency towards concurrent antiferromagnetic ordering. The charge, spin, and associated lattice instabilities are significantly suppressed with increasing external pressure, contributing to the emergence of superconductivity in pressurized La$_3$Ni$_2$O$_7$. Carrier doping is found to effectively suppress these instabilities, suggesting a viable strategy to stabilize a superconducting phase under ambient pressure.

The discovery of superconductivity with a critical temperature of about 80~K in La$_{3}$Ni$_{2}$O$_{7}$ single crystals under pressure has received enormous attention. La$_{3}$Ni$_{2}$O$_{7}$ is not superconducting under ambient pressure but exhibits a transition at $T^{\ast} \simeq 115$~K. Understanding the electronic correlations and charge dynamics is an important step towards the origin of superconductivity and other instabilities. Here, our optical study shows that La$_{3}$Ni$_{2}$O$_{7}$ features strong electronic correlations which significantly reduce the electron's kinetic energy and place this system in the proximity of the Mott phase. The low-frequency optical conductivity reveals two Drude components arising from multiple bands at the Fermi level. The transition at $T^{\ast}$ removes the Drude component exhibiting non-Fermi liquid behavior, whereas the one with Fermi-liquid behavior is barely affected. These observations in combination with theoretical results suggest that the Fermi surface dominated by the Ni-$d_{3z^{2}-r^{2}}$ orbital is removed due to the transition at $T^{\ast}$. Our experimental results provide pivotal information for understanding the transition at $T^{\ast}$ and superconductivity in La$_{3}$Ni$_{2}$O$_{7}$., Comment: 26 pages, 4 figures, Comments are welcome and appreciated