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As a promising dark matter candidate, primordial black holes (PBHs) lighter than $\sim10^{-18}M_{\odot}$ are supposed to have evaporated by today through Hawking radiation. This scenario is challenged by the memory burden effect, which suggests that the evaporation of black holes may slow down significantly after they have emitted about half of their initial mass. We explore the astrophysical implications of the memory burden effect on the PBH abundance by today and the possibility for PBHs lighter than $\sim10^{-18}M_{\odot}$ to persist as dark matter. Our analysis utilizes current LIGO-Virgo-KAGRA data to constrain the primordial power spectrum and infer the PBH abundance. We find a null detection of scalar-induced gravitational waves that accompanied the formation of the PBHs. Then we find that PBHs are ruled out within the mass range $\sim[10^{-24},10^{-19}]M_{\odot}$. Furthermore, we expect that next-generation gravitational wave detectors, such as the Einstein Telescope and the Cosmic Explorer, will provide even more stringent constraints. Our results indicate that future detectors can reach sensitivities that could rule out PBH as dark matter within $\sim[10^{-29}M_{\odot},10^{-16}M_{\odot}]$ in the null detection of scalar-induced gravitational waves., Comment: 9 pages, 2 figures

We propose an analytical approximation for the energy spectrum of the scalar induced gravitational waves (SIGWs) generated by a broken power-law power spectrum, and find that both the asymptotic power-law tails and the intermediate peak contribute distinct features to the SIGW spectrum. Moreover, the broken power-law power spectrum has abundant near-peak features and our results can be used as a near-peak approximation that covers a wide range of models. Our analytical approximation is useful in the rapid generation of the SIGW energy spectrum, which is beneficial for gravitational wave data analysis., Comment: 18 pages, 6 figures, first version

In this paper, we establish the existence of one solution for a Schr\"{o}dinger equation with jumping nonlinearities: $-\Delta u+V(x)u=f(x,u)$, $x\in \mathbb {R}^N$, and $u(x)\to 0$, $|x|\to +\infty$, where $V$ is a potential function on which we make hypotheses, and in particular allow $V$ which is unbounded below, and $f(x,u)=au^-+bu^++g(x,u)$. No restriction on $b$ is required, which implies that $f(x,s)s^{-1}$ may interfere with the essential spectrum of $ -\Delta+V$ for $s\to +\infty$. Using the truncation method and the Morse theory, we can compute critical groups of the corresponding functional at zero and infinity, then prove the existence of one negative solution.

Controlling the size and distribution of potential barriers within a medium of interacting particles can unveil unique collective behaviors and innovative functionalities. In this study, we introduce a unique superconducting hybrid device using a novel artificial spin ice structure composed of asymmetric nanomagnets. This structure forms a distinctive superconducting pinning potential that steers unconventional motion of superconducting vortices, thereby inducing a magnetic nonreciprocal effect, in contrast to the electric nonreciprocal effect commonly observed in superconducting diodes. Furthermore, the polarity of the magnetic nonreciprocity is in-situ reversible through the tunable magnetic patterns of artificial spin ice. Our findings demonstrate that artificial spin ice not only precisely modulates superconducting characteristics but also opens the door to novel functionalities, offering a groundbreaking paradigm for superconducting electronics.

Large language models respond well in high-resource languages like English but struggle in low-resource languages. It may arise from the lack of high-quality instruction following data in these languages. Directly translating English samples into these languages can be a solution but unreliable, leading to responses with translation errors and lacking language-specific or cultural knowledge. To address this issue, we propose a novel method to construct cross-lingual instruction following samples with instruction in English and response in low-resource languages. Specifically, the language model first learns to generate appropriate English instructions according to the natural web texts in other languages as responses. The candidate cross-lingual instruction tuning samples are further refined and diversified. We have employed this method to build a large-scale cross-lingual instruction tuning dataset on 10 languages, namely X-Instruction. The instruction data built using our method incorporate more language-specific knowledge compared with the naive translation method. Experimental results have shown that the response quality of the model tuned on X-Instruction greatly exceeds the model distilled from a powerful teacher model, reaching or even surpassing the ones of ChatGPT. In addition, we find that models tuned on cross-lingual instruction following samples can follow the instruction in the output language without further tuning., Comment: ACL 2024. Our codes, data and model weights are available at https://github.com/ZNLP/X-Instruction

Frequency combs, specialized laser sources emitting multiple equidistant frequency lines, have revolutionized science and technology with unprecedented precision and versatility. Recently, integrated frequency combs are emerging as scalable solutions for on-chip photonics. Here, we demonstrate a fully integrated superconducting microcomb that is easy to manufacture, simple to operate, and consumes ultra-low power. Our turnkey apparatus comprises a basic nonlinear superconducting device, a Josephson junction, directly coupled to a superconducting microstrip resonator. We showcase coherent comb generation through self-started mode-locking. Therefore, comb emission is initiated solely by activating a DC bias source, with power consumption as low as tens of picowatts. The resulting comb spectrum resides in the microwave domain and spans multiple octaves. The linewidths of all comb lines can be narrowed down to 1 Hz through a unique coherent injection-locking technique. Our work represents a critical step towards fully integrated microwave photonics and offers the potential for integrated quantum processors.

We introduce VASA, a framework for generating lifelike talking faces with appealing visual affective skills (VAS) given a single static image and a speech audio clip. Our premiere model, VASA-1, is capable of not only producing lip movements that are exquisitely synchronized with the audio, but also capturing a large spectrum of facial nuances and natural head motions that contribute to the perception of authenticity and liveliness. The core innovations include a holistic facial dynamics and head movement generation model that works in a face latent space, and the development of such an expressive and disentangled face latent space using videos. Through extensive experiments including evaluation on a set of new metrics, we show that our method significantly outperforms previous methods along various dimensions comprehensively. Our method not only delivers high video quality with realistic facial and head dynamics but also supports the online generation of 512x512 videos at up to 40 FPS with negligible starting latency. It paves the way for real-time engagements with lifelike avatars that emulate human conversational behaviors., Comment: Tech Report. Project webpage: https://www.microsoft.com/en-us/research/project/vasa-1/

Symmetry-breaking plays a pivotal role in unlocking intriguing properties and functionalities in material systems. For example, the breaking of spatial and temporal symmetries leads to a fascinating phenomenon of superconducting diode effect. However, generating and precisely controlling the superconducting diode effect poses significant challenges. Here, we take a novel route with deliberate manipulation of magnetic charge potentials to realize unconventional superconducting flux-quantum diode effects. We achieve this through suitably tailored nanoengineered arrays of nanobar magnets on top of a superconducting thin film. We demonstrate the vital roles of inversion antisymmetry and its breaking in evoking unconventional superconducting effects-a magnetically symmetric diode effect and an odd-parity magnetotransport effect. These effects are non-volatilely controllable through in-situ magnetization switching of the nanobar magnets. Our findings promote the use of antisymmetry (breaking) for initiating unconventional superconducting properties, paving the way for exciting prospects and innovative functionalities in superconducting electronics.

Materials with in-plane electrical anisotropy have great potential for designing artificial synaptic devices. However, natural materials with strong intrinsic in-plane electrical anisotropy are rare. We introduce a simple strategy to produce extremely large electrical anisotropy via grating gating of a semiconductor two-dimensional electron gas (2DEG) of AlGaN/GaN. We show that periodically modulated electric potential in the 2DEG induces in-plane electrical anisotropy, which is significantly enhanced in a magnetic field, leading to an ultra large electrical anisotropy. This is induced by a giant positive magnetoresistance and a giant negative magnetoresistance under two orthogonally oriented in-plane current flows, respectively. This giant electrical anisotropy is in-situ tunable by tailoring both the grating gate voltage and the magnetic field. Our semiconductor device with controllable giant electrical anisotropy will stimulate new device applications, such as multi-terminal memtransistors and bionic synapses.

Recent fMRI-to-image approaches mainly focused on associating fMRI signals with specific conditions of pre-trained diffusion models. These approaches, while producing high-quality images, capture only a limited aspect of the complex information in fMRI signals and offer little detailed control over image creation. In contrast, this paper proposes to directly modulate the generation process of diffusion models using fMRI signals. Our approach, NeuroPictor, divides the fMRI-to-image process into three steps: i) fMRI calibrated-encoding, to tackle multi-individual pre-training for a shared latent space to minimize individual difference and enable the subsequent multi-subject training; ii) fMRI-to-image multi-subject pre-training, perceptually learning to guide diffusion model with high- and low-level conditions across different individuals; iii) fMRI-to-image single-subject refining, similar with step ii but focus on adapting to particular individual. NeuroPictor extracts high-level semantic features from fMRI signals that characterizing the visual stimulus and incrementally fine-tunes the diffusion model with a low-level manipulation network to provide precise structural instructions. By training with about 67,000 fMRI-image pairs from various individuals, our model enjoys superior fMRI-to-image decoding capacity, particularly in the within-subject setting, as evidenced in benchmark datasets. Our code and model are available at https://jingyanghuo.github.io/neuropictor/., Comment: Accepted by ECCV 2024

Pre-trained computational language models have recently made remarkable progress in harnessing the language abilities which were considered unique to humans. Their success has raised interest in whether these models represent and process language like humans. To answer this question, this paper proposes MulCogBench, a multi-modal cognitive benchmark dataset collected from native Chinese and English participants. It encompasses a variety of cognitive data, including subjective semantic ratings, eye-tracking, functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG). To assess the relationship between language models and cognitive data, we conducted a similarity-encoding analysis which decodes cognitive data based on its pattern similarity with textual embeddings. Results show that language models share significant similarities with human cognitive data and the similarity patterns are modulated by the data modality and stimuli complexity. Specifically, context-aware models outperform context-independent models as language stimulus complexity increases. The shallow layers of context-aware models are better aligned with the high-temporal-resolution MEG signals whereas the deeper layers show more similarity with the high-spatial-resolution fMRI. These results indicate that language models have a delicate relationship with brain language representations. Moreover, the results between Chinese and English are highly consistent, suggesting the generalizability of these findings across languages.

Automatic pavement crack detection is an important task to ensure the functional performances of pavements during their service life. Inspired by deep learning (DL), the encoder-decoder framework is a powerful tool for crack detection. However, these models are usually open-loop (OL) systems that tend to treat thin cracks as the background. Meanwhile, these models can not automatically correct errors in the prediction, nor can it adapt to the changes of the environment to automatically extract and detect thin cracks. To tackle this problem, we embed closed-loop feedback (CLF) into the neural network so that the model could learn to correct errors on its own, based on generative adversarial networks (GAN). The resulting model is called CrackCLF and includes the front and back ends, i.e. segmentation and adversarial network. The front end with U-shape framework is employed to generate crack maps, and the back end with a multi-scale loss function is used to correct higher-order inconsistencies between labels and crack maps (generated by the front end) to address open-loop system issues. Empirical results show that the proposed CrackCLF outperforms others methods on three public datasets. Moreover, the proposed CLF can be defined as a plug and play module, which can be embedded into different neural network models to improve their performances.

Multilingual generative models obtain remarkable cross-lingual in-context learning capabilities through pre-training on large-scale corpora. However, they still exhibit a performance bias toward high-resource languages and learn isolated distributions of multilingual sentence representations, which may hinder knowledge transfer across languages. To bridge this gap, we propose a simple yet effective cross-lingual alignment framework exploiting pairs of translation sentences. It aligns the internal sentence representations across different languages via multilingual contrastive learning and aligns outputs by following cross-lingual instructions in the target language. Experimental results show that even with less than 0.1 {\textperthousand} of pre-training tokens, our alignment framework significantly boosts the cross-lingual abilities of generative language models and mitigates the performance gap. Further analyses reveal that it results in a better internal multilingual representation distribution of multilingual models., Comment: NAACL 2024; Our code is available at https://github.com/chongli17/CrossLingualAlignment

A triangle-path in a graph $G$ is a sequence of distinct triangles $T_1,T_2,\ldots,T_m$ in $G$ such that for any $i, j$ with $1\leq i < j \leq m$, $|E(T_i)\cap E(T_{i+1})|=1$ and $E(T_i)\cap E(T_j)=\emptyset$ if $j > i+1$. A connected graph $G$ is triangularly connected if for any two nonparallel edges $e$ and $e'$ there is a triangle-path $T_1T_2\cdots T_m$ such that $e\in E(T_1)$ and $e'\in E(T_m)$. For ordinary graphs, Fan {\it et al.}~(J. Combin. Theory Ser. B 98 (2008) 1325-1336) characterize all triangularly connected graphs that admit nowhere-zero $3$-flows or $4$-flows. Corollaries of this result include integer flow of some families of ordinary graphs, such as, locally connected graphs due to Lai (J. Graph Theory 42 (2003) 211-219) and some types of products of graphs due to Imrich et al.(J. Graph Theory 64 (2010) 267-276). In this paper, Fan's result for triangularly connected graphs is further extended to signed graphs. We proved that every flow-admissible triangularly connected signed graph admits a nowhere-zero $4$-flow if and only if it is not the wheel $W_5$ associated with a specific signature. Moreover, this result is sharp since there are infinitely many unbalanced triangularly connected signed graphs admitting a nowhere-zero $4$-flow but not $3$-flow.

Transformer-based models, even though achieving super-human performance on several downstream tasks, are often regarded as a black box and used as a whole. It is still unclear what mechanisms they have learned, especially their core module: multi-head attention. Inspired by functional specialization in the human brain, which helps to efficiently handle multiple tasks, this work attempts to figure out whether the multi-head attention module will evolve similar function separation under multi-tasking training. If it is, can this mechanism further improve the model performance? To investigate these questions, we introduce an interpreting method to quantify the degree of functional specialization in multi-head attention. We further propose a simple multi-task training method to increase functional specialization and mitigate negative information transfer in multi-task learning. Experimental results on seven pre-trained transformer models have demonstrated that multi-head attention does evolve functional specialization phenomenon after multi-task training which is affected by the similarity of tasks. Moreover, the multi-task training strategy based on functional specialization boosts performance in both multi-task learning and transfer learning without adding any parameters., Comment: Accepted to EMNLP 2023 main conference. Our code is available at https://github.com/ZNLP/FunctionalSpecializationInMHA

Rotation group $\mathcal{SO}(d)$ synchronization is an important inverse problem and has attracted intense attention from numerous application fields such as graph realization, computer vision, and robotics. In this paper, we focus on the least-squares estimator of rotation group synchronization with general additive noise models, which is a nonconvex optimization problem with manifold constraints. Unlike the phase/orthogonal group synchronization, there are limited provable approaches for solving rotation group synchronization. First, we derive improved estimation results of the least-squares/spectral estimator, illustrating the tightness and validating the existing relaxation methods of solving rotation group synchronization through the optimum of relaxed orthogonal group version under near-optimal noise level for exact recovery. Moreover, departing from the standard approach of utilizing the geometry of the ambient Euclidean space, we adopt an intrinsic Riemannian approach to study orthogonal/rotation group synchronization. Benefiting from a quotient geometric view, we prove the positive definite condition of quotient Riemannian Hessian around the optimum of orthogonal group synchronization problem, and consequently the Riemannian local error bound property is established to analyze the convergence rate properties of various Riemannian algorithms. As a simple and feasible method, the sequential convergence guarantee of the (quotient) Riemannian gradient method for solving orthogonal/rotation group synchronization problem is studied, and we derive its global linear convergence rate to the optimum with the spectral initialization. All results are deterministic without any probabilistic model.

Large language models (LLMs) demonstrate promising translation performance among various natural languages. However, many LLMs especially the open-sourced ones, such as BLOOM and LLaMA, are English-dominant and support only dozens of natural languages, making the potential of LLMs on language translation less explored. In this work, we present BigTranslate which adapts LLaMA that covers only 20 languages and enhances it with multilingual translation capability on more than 100 languages. BigTranslate is built upon LLaMA-13B and it is optimized in three steps. First, we continue training LLaMA with massive Chinese monolingual data. Second, we continue training the model with a large-scale parallel dataset that covers 102 natural languages. Third, we instruct-tune the foundation model with multilingual translation instructions, leading to our BigTranslate model. The preliminary experiments on multilingual translation show that BigTranslate performs comparably with ChatGPT and Google Translate in many languages and even outperforms ChatGPT in 8 language pairs. We release the BigTranslate model and hope it can advance the research progress., Comment: 16 pages, 4 figures. Our model is available at https://github.com/ZNLP/BigTranslate

In this paper we present the distribution of molecular gas in the Milky Way Galactic plane from $l$ = [59.75, 74.75]$^{\circ}$ and $b$ = [${-}$5.25, +5.25]$^{\circ}$, using the MWISP $^{12}$CO/$^{13}$CO/$\rm {C}^{18}{O}$ emission line data. The molecular gas in this region can be mainly attributed to the Local spur, Local arm, Perseus arm, and Outer arm. Statistics of the physical properties of the molecular gas in each arm, such as excitation temperature, optical depth, and column density, are presented. Using the DBSCAN algorithm, we identified 15 extremely distant molecular clouds with kinematic distances of 14.72$-$17.77 kpc and masses of 363$-$520 M$_{\odot}$, which we find could be part of the Outer Scutum-Centaurus (OSC) arm identified by \cite{2011ApJ...734L..24D} and \cite{2015ApJ...798L..27S}. It is also possible that, 12 of these 15 extremely distant molecular clouds constitute an independent structure between the Outer and the OSC arms or a spur. There exist two Gaussian components in the vertical distribution of the molecular gas in the Perseus spiral arm. These two Gaussian components correspond to two giant filaments parallel to the Galactic plane. We find an upward warping of the molecular gas in the Outer spiral arm with a displacement of around 270 pc with respect to the Galactic mid-plane., Comment: 33 pages, 19 figures

Using the HI self-absorption data from the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we perform a study of the cold atomic gas in the Cygnus-X North region. The most remarkable HI cloud is characterized by a filamentary structure, associated in space and in velocity with the principle molecular filament in the Cygnus-X North region. We investigate the transition from the atomic filament to the molecular filament. We find that the HII regions Cygnus OB2 and G081.920+00.138 play a critical role in compressing and shaping the atomic Cygnus-X North filament, where the molecular filament subsequently forms. The cold HI in the DR21 filament has a much larger column density (N(HI) $\sim$ 1 $\times$ 10$^{20}$ cm$^{-2}$) than the theoretical value of the residual atomic gas ($\sim$ 1 $\times$ 10$^{19}$ cm$^{-2}$), suggesting that the HI-to-H$_2$ transition is still in progress. The timescale of the HI-to-H$_2$ transition is estimated to be 3 $\times$ 10$^{5}$ yr, which approximates the ages of massive protostars in the Cygnus-X North region. This implies that the formation of molecular clouds and massive stars may occur almost simultaneously in the DR21 filament, in accord with a picture of rapid and dynamic cloud evolution., Comment: 10 pages, 7 figures

Motivated by current status of dark matter (DM) search, a new type of DM production mechanism is proposed based on thedynamical process of a strong first-order phase transition in the early universe, namely, the filtered DM mechanism. We study the hydrodynamic effects on the DM relic density. By detailed calculations, we demonstrate that the hydrodynamic modes with the corresponding hydrodynamic heating effects play essential roles in determining the DM relic density. The corresponding phase transition gravitational wave could help to probe this new mechanism., Comment: Published version in Physical Review D, 39 pages, 13 figures, 4 tables

Existing dehazing approaches struggle to process real-world hazy images owing to the lack of paired real data and robust priors. In this work, we present a new paradigm for real image dehazing from the perspectives of synthesizing more realistic hazy data and introducing more robust priors into the network. Specifically, (1) instead of adopting the de facto physical scattering model, we rethink the degradation of real hazy images and propose a phenomenological pipeline considering diverse degradation types. (2) We propose a Real Image Dehazing network via high-quality Codebook Priors (RIDCP). Firstly, a VQGAN is pre-trained on a large-scale high-quality dataset to obtain the discrete codebook, encapsulating high-quality priors (HQPs). After replacing the negative effects brought by haze with HQPs, the decoder equipped with a novel normalized feature alignment module can effectively utilize high-quality features and produce clean results. However, although our degradation pipeline drastically mitigates the domain gap between synthetic and real data, it is still intractable to avoid it, which challenges HQPs matching in the wild. Thus, we re-calculate the distance when matching the features to the HQPs by a controllable matching operation, which facilitates finding better counterparts. We provide a recommendation to control the matching based on an explainable solution. Users can also flexibly adjust the enhancement degree as per their preference. Extensive experiments verify the effectiveness of our data synthesis pipeline and the superior performance of RIDCP in real image dehazing., Comment: Acceptted by CVPR 2023

Recent research advance has significantly improved the visual realism of immersive 3D video communication. In this work we present a method to further enhance this immersive experience by adding the hand touch capability ("remote hand clapping"). In our system, each meeting participant sits in front of a large screen with haptic feedback. The local participant can reach his hand out to the screen and perform hand clapping with the remote participant as if the two participants were only separated by a virtual glass. A key challenge in emulating the remote hand touch is the realistic rendering of the participant's hand and arm as the hand touches the screen. When the hand is very close to the screen, the RGBD data required for realistic rendering is no longer available. To tackle this challenge, we present a dual representation of the user's hand. Our dual representation not only preserves the high-quality rendering usually found in recent image-based rendering systems but also allows the hand to reach the screen. This is possible because the dual representation includes both an image-based model and a 3D geometry-based model, with the latter driven by a hand skeleton tracked by a side view camera. In addition, the dual representation provides a distance-based fusion of the image-based and 3D geometry-based models as the hand moves closer to the screen. The result is that the image-based and 3D geometry-based models mutually enhance each other, leading to realistic and seamless rendering. Our experiments demonstrate that our method provides consistent hand contact experience between remote users and improves the immersive experience of 3D video communication., Comment: IEEE VR 2023

Recent years have witnessed significant growth of face alignment. Though dense facial landmark is highly demanded in various scenarios, e.g., cosmetic medicine and facial beautification, most works only consider sparse face alignment. To address this problem, we present a framework that can enrich landmark density by existing sparse landmark datasets, e.g., 300W with 68 points and WFLW with 98 points. Firstly, we observe that the local patches along each semantic contour are highly similar in appearance. Then, we propose a weakly-supervised idea of learning the refinement ability on original sparse landmarks and adapting this ability to enriched dense landmarks. Meanwhile, several operators are devised and organized together to implement the idea. Finally, the trained model is applied as a plug-and-play module to the existing face alignment networks. To evaluate our method, we manually label the dense landmarks on 300W testset. Our method yields state-of-the-art accuracy not only in newly-constructed dense 300W testset but also in the original sparse 300W and WFLW testsets without additional cost., Comment: AAAI 2023

The antenna is one of the key building blocks of many wearable electronic device, and its functions include wireless communications, energy harvesting and radiative wireless power transfer (WPT). In an effort to realise lightweight, autonomous and battery-less wearable devices, we demonstrate a reconfigurable antenna design for 5G wearable applications that require ultra-low driving voltages ($0.4$-$0.6\,$V) and operate over a high frequency range ($3.3$-$3.8\,$GHz). For smart glasses application, previous antenna designs were `fixed' and mounted on the eyeglass frame itself. Here, we demonstrate a reconfigurable design that could be achieved on the lens itself, using an anisotropic liquid crystal (LC) material. We demonstrate how LC alignment and electric field patterns strongly influence the tuning capabilities of these antennas in the gigahertz range and present a smart, reconfigurable spiral antenna system with a LC substrate.

Ideal deterministic quantum communication tasks require maximally entangled channels. The reality is that the maximally entangled channel is inevitably degraded to a non-maximally entangled one because of various decoherence mechanisms, seriously deteriorating the performance of quantum communication. Instead of adopting traditional entanglement purification or distillation to rebuild maximally entangled channels, we have designed a novel deterministic joint remote state preparation scheme using the degenerated non-maximally entangled state directly. A protocol for deterministic joint remote preparation of a two-dimensional quantum state via a non-maximally hyperentangled quantum channel has been devised with the help of auxiliary qudits. Then we generalize it to prepare a high-dimensional quantum state faithfully. No matter how weak the shared entanglement is, the success probability of communication is maintained at 100% as soon as it exists. This investigation provides an ideology for the construction of practical quantum communication networks.

Bouchet conjectured in 1983 that every flow-admissible signed graph admits a nowhere-zero 6-flow which is equivalent to the restriction to cubic signed graphs. In this paper, we proved that every flow-admissible $3$-edge-colorable cubic signed graph admits a nowhere-zero $10$-flow. This together with the 4-color theorem implies that every flow-admissible bridgeless planar signed graph admits a nowhere-zero $10$-flow. As a byproduct, we also show that every flow-admissible hamiltonian signed graph admits a nowhere-zero $8$-flow.