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Changes in facial expression, head movement, body movement and gesture movement are remarkable cues in sign language recognition, and most of the current continuous sign language recognition(CSLR) research methods mainly focus on static images in video sequences at the frame-level feature extraction stage, while ignoring the dynamic changes in the images. In this paper, we propose a novel motor attention mechanism to capture the distorted changes in local motion regions during sign language expression, and obtain a dynamic representation of image changes. And for the first time, we apply the self-distillation method to frame-level feature extraction for continuous sign language, which improves the feature expression without increasing the computational resources by self-distilling the features of adjacent stages and using the higher-order features as teachers to guide the lower-order features. The combination of the two constitutes our proposed holistic model of CSLR Based on motor attention mechanism and frame-level Self-Distillation (MAM-FSD), which improves the inference ability and robustness of the model. We conduct experiments on three publicly available datasets, and the experimental results show that our proposed method can effectively extract the sign language motion information in videos, improve the accuracy of CSLR and reach the state-of-the-art level., Comment: 10 pages, 7 figures

Social media platforms are daily exhibiting millions of events. To preliminarily predict the mainstream public reaction to these events, we study trendy response prediction to automatically generate top-liked user replies to social media events. While previous works focus on generating responses without factoring in popularity, we propose Popularity-Aligned Language Models (PopALM) to distinguish responses liked by a larger audience through reinforcement learning. Recognizing the noisy labels from user "likes", we tailor-make curriculum learning in proximal policy optimization (PPO) to help models capture the essential samples for easy-to-hard training. In experiments, we build a large-scale Weibo dataset for trendy response prediction, and its results show that PopALM can help boost the performance of advanced language models., Comment: Accepted by COLING 2024

Multi-view clustering method based on anchor graph has been widely concerned due to its high efficiency and effectiveness. In order to avoid post-processing, most of the existing anchor graph-based methods learn bipartite graphs with connected components. However, such methods have high requirements on parameters, and in some cases it may not be possible to obtain bipartite graphs with clear connected components. To end this, we propose a label learning method based on tensor projection (LLMTP). Specifically, we project anchor graph into the label space through an orthogonal projection matrix to obtain cluster labels directly. Considering that the spatial structure information of multi-view data may be ignored to a certain extent when projected in different views separately, we extend the matrix projection transformation to tensor projection, so that the spatial structure information between views can be fully utilized. In addition, we introduce the tensor Schatten $p$-norm regularization to make the clustering label matrices of different views as consistent as possible. Extensive experiments have proved the effectiveness of the proposed method.

High-quality sentence embeddings are fundamental in many natural language processing (NLP) tasks, such as semantic textual similarity (STS) and retrieval-augmented generation (RAG). Nevertheless, most existing methods leverage fixed-length embeddings from full-layer language models, which lack the scalability to accommodate the diverse available resources across various applications. Viewing this gap, we propose a novel sentence embedding model $\mathrm{Espresso}$ $\mathrm{Sentence}$ $\mathrm{Embeddings}$ (ESE) with two learning processes. First, the learn-to-express process encodes more salient representations to lower layers. Second, the learn-to-compress process compacts essential features into the initial dimensions using Principal Component Analysis (PCA). This way, ESE can scale model depth via the former process and embedding size via the latter. Extensive experiments on STS and RAG suggest that ESE can effectively produce high-quality embeddings with less model depth and embedding size, enhancing embedding inference efficiency.

In this paper, we propose an adaptive finite element method for computing the first eigenpair of the $p$-Laplacian problem. We prove that starting from a fine initial mesh our proposed adaptive algorithm produces a sequence of discrete first eigenvalues that converges to the first eigenvalue of the continuous problem and the distance between discrete eigenfunctions and the normalized eigenfunction set corresponding to the first eigenvalue in $W^{1,p}$-norm also tends to zero. Extensive numerical examples are provided to show the effectiveness and efficiency., Comment: 29 pages, 9 tables, 14 figures, accepted by SIAM Journal on Scientific Computing

The opioid epidemic, referring to the growing hospitalizations and deaths because of overdose of opioid usage and addiction, has become a severe health problem in the United States. Many strategies have been developed by the federal and local governments and health communities to combat this crisis. Among them, improving our understanding of the epidemic through better health surveillance is one of the top priorities. In addition to direct testing, machine learning approaches may also allow us to detect opioid users by analyzing data from social media because many opioid users may choose not to do the tests but may share their experiences on social media anonymously. In this paper, we take advantage of recent advances in machine learning, collect and analyze user posts from a popular social network Reddit with the goal to identify opioid users. Posts from more than 1,000 users who have posted on three sub-reddits over a period of one month have been collected. In addition to the ones that contain keywords such as opioid, opiate, or heroin, we have also collected posts that contain slang words of opioid such as black or chocolate. We apply an attention-based bidirectional long short memory model to identify opioid users. Experimental results show that the approaches significantly outperform competitive algorithms in terms of F1-score. Furthermore, the model allows us to extract most informative words, such as opiate, opioid, and black, from posts via the attention layer, which provides more insights on how the machine learning algorithm works in distinguishing drug users from non-drug users.

The linear instability of Faraday waves in Hele-Shaw cells is investigated with consideration of the viscosity of fluids after gap-averaging the governing equations due to the damping from two lateral walls and the dynamic behavior of contact angle. A new hydrodynamic model is thus derived and solved semi-analytically. The contribution of viscosity to critical acceleration amplitude is slight compared to other factors associated with dissipation, and the potential flow theory is sufficient to describe onset based on the present study, but the rotational component of velocity can change the timing of onset largely, which paradoxically comes from the viscosity. The model degenerates into a novel damped Mathieu equation if the viscosity is dropped with two damping terms referring to the gap-averaged damping and dissipation from dynamic contact angle, respectively. The former increases when the gap size decreases, and the latter grows as frequency rises. When it comes to the dispersion relation of Faraday waves, an unusual detuning emerges due to the imaginary part of the gap-averaged damping.

Shortcuts to adiabaticity (STA) are powerful tools that can be used to control quantum systems with high fidelity. They work particularly well for single particle and non-interacting systems which can be described exactly and which possess invariant or self-similar dynamics. However, finding an exact STA for strongly correlated many-body systems can be difficult, as their complex dynamics may not be easily described, especially for larger systems that do not possess self-similar solutions. Here, we design STAs for one-dimensional bosonic gas in the Tonks--Girardeau limit by using a mean-field approach that succinctly captures the strong interaction effects through a quintic nonlinear term in the Schr\"odinger equation. We show that for the case of the harmonic oscillator with a time-dependent trap frequency the mean-field approach works exactly and recovers the well-known STA from literature. To highlight the robustness of our approach we also show that it works effectively for anharmonic potentials, achieving higher fidelities than other typical control techniques., Comment: 8 pages, 5 figures

This study focuses on media bias detection, crucial in today's era of influential social media platforms shaping individual attitudes and opinions. In contrast to prior work that primarily relies on training specific models tailored to particular datasets, resulting in limited adaptability and subpar performance on out-of-domain data, we introduce a general bias detection framework, IndiVec, built upon large language models. IndiVec begins by constructing a fine-grained media bias database, leveraging the robust instruction-following capabilities of large language models and vector database techniques. When confronted with new input for bias detection, our framework automatically selects the most relevant indicator from the vector database and employs majority voting to determine the input's bias label. IndiVec excels compared to previous methods due to its adaptability (demonstrating consistent performance across diverse datasets from various sources) and explainability (providing explicit top-k indicators to interpret bias predictions). Experimental results on four political bias datasets highlight IndiVec's significant superiority over baselines. Furthermore, additional experiments and analysis provide profound insights into the framework's effectiveness.

Visually Impaired Assistance (VIA) aims to automatically help the visually impaired (VI) handle daily activities. The advancement of VIA primarily depends on developments in Computer Vision (CV) and Natural Language Processing (NLP), both of which exhibit cutting-edge paradigms with large models (LMs). Furthermore, LMs have shown exceptional multimodal abilities to tackle challenging physically-grounded tasks such as embodied robots. To investigate the potential and limitations of state-of-the-art (SOTA) LMs' capabilities in VIA applications, we present an extensive study for the task of VIA with LMs (VIALM). In this task, given an image illustrating the physical environments and a linguistic request from a VI user, VIALM aims to output step-by-step guidance to assist the VI user in fulfilling the request grounded in the environment. The study consists of a survey reviewing recent LM research and benchmark experiments examining selected LMs' capabilities in VIA. The results indicate that while LMs can potentially benefit VIA, their output cannot be well environment-grounded (i.e., 25.7% GPT-4's responses) and lacks fine-grained guidance (i.e., 32.1% GPT-4's responses)., Comment: under review

The exquisite angular resolution and sensitivity of JWST is opening a new window for our understanding of the Universe. In nearby galaxies, JWST observations are revolutionizing our understanding of the first phases of star formation and the dusty interstellar medium. Nineteen local galaxies spanning a range of properties and morphologies across the star-forming main sequence have been observed as part of the PHANGS-JWST Cycle 1 Treasury program at spatial scales of $\sim$5-50pc. Here, we describe pjpipe, an image processing pipeline developed for the PHANGS-JWST program that wraps around and extends the official JWST pipeline. We release this pipeline to the community as it contains a number of tools generally useful for JWST NIRCam and MIRI observations. Particularly for extended sources, pjpipe products provide significant improvements over mosaics from the MAST archive in terms of removing instrumental noise in NIRCam data, background flux matching, and calibration of relative and absolute astrometry. We show that slightly smoothing F2100W MIRI data to 0.9" (degrading the resolution by about 30 percent) reduces the noise by a factor of $\approx$3. We also present the first public release (DR1.1.0) of the pjpipe processed eight-band 2-21 $\mu$m imaging for all nineteen galaxies in the PHANGS-JWST Cycle 1 Treasury program. An additional 55 galaxies will soon follow from a new PHANGS-JWST Cycle 2 Treasury program., Comment: 49 pages (27 in Appendices), 54 Figures (39 in Appendices), 3 Tables. Accepted for publication in ApJS. Updated to match accepted version. Data available at https://archive.stsci.edu/hlsp/phangs/phangs-jwst

The Mixtures-of-Experts (MoE) model is a widespread distributed and integrated learning method for large language models (LLM), which is favored due to its ability to sparsify and expand models efficiently. However, the performance of MoE is limited by load imbalance and high latency of All-to-All communication, along with relatively redundant computation owing to large expert capacity. Load imbalance may result from existing routing policies that consistently tend to select certain experts. The frequent inter-node communication in the All-to-All procedure also significantly prolongs the training time. To alleviate the above performance problems, we propose a novel routing strategy that combines load balance and locality by converting partial inter-node communication to that of intra-node. Notably, we elucidate that there is a minimum threshold for expert capacity, calculated through the maximal angular deviation between the gating weights of the experts and the assigned tokens. We port these modifications on the PanGu-Sigma model based on the MindSpore framework with multi-level routing and conduct experiments on Ascend clusters. The experiment results demonstrate that the proposed LocMoE reduces training time per epoch by 12.68% to 22.24% compared to classical routers, such as hash router and switch router, without impacting the model accuracy., Comment: 1. Update the font size of all figures. 2. Update the name of the proposed layer Grouped Average Pooling (GrAP). 3. Change the order of the Section Contribution Statement

Emergent collective modes in lattices give birth to many intriguing physical phenomena in condensed matter physics. Among these collective modes, large-area modes typically feature small-level spacings, while a mode with stable frequency tends to be spatially tightly confined. Here, we theoretically propose and experimentally demonstrate a spectral-isolated photonic topological corner mode with a tunable mode area and stable frequency in a two-dimensional photonic crystal. This mode emerges from hybridizing the large-area homogeneous mode and in-gap topological corner modes. Remarkably, this large-area homogeneous mode possesses unique chirality and has a tunable mode area under the change of the mass term of the inner topological non-trivial lattice. We experimentally observe such topological large-area corner modes(TLCM) in a 2D photonic system and demonstrate the robustness by introducing disorders in the structure. Our findings have propelled the forefront of higher-order topology research, transitioning it from single-lattice systems to multi-lattice systems. They may support promising potential applications, particularly in vertical-cavity surface-emitting lasers., Comment: 5 pages, 4 figures

Cancer remains one of the most challenging diseases to treat in the medical field. Machine learning has enabled in-depth analysis of rich multi-omics profiles and medical imaging for cancer diagnosis and prognosis. Despite these advancements, machine learning models face challenges stemming from limited labeled sample sizes, the intricate interplay of high-dimensionality data types, the inherent heterogeneity observed among patients and within tumors, and concerns about interpretability and consistency with existing biomedical knowledge. One approach to surmount these challenges is to integrate biomedical knowledge into data-driven models, which has proven potential to improve the accuracy, robustness, and interpretability of model results. Here, we review the state-of-the-art machine learning studies that adopted the fusion of biomedical knowledge and data, termed knowledge-informed machine learning, for cancer diagnosis and prognosis. Emphasizing the properties inherent in four primary data types including clinical, imaging, molecular, and treatment data, we highlight modeling considerations relevant to these contexts. We provide an overview of diverse forms of knowledge representation and current strategies of knowledge integration into machine learning pipelines with concrete examples. We conclude the review article by discussing future directions to advance cancer research through knowledge-informed machine learning., Comment: 41 pages, 4 figures, 2 tables

An emotional support conversation system aims to alleviate users' emotional distress and assist them in addressing their challenges. To generate supportive responses, it is critical to consider multiple factors such as empathy, support strategies, and response coherence, as established in prior methods. Nonetheless, previous models occasionally generate unhelpful responses, which intend to provide support but display counterproductive effects. According to psychology and communication theories, poor performance in just one contributing factor might cause a response to be unhelpful. From the model training perspective, since these models have not been exposed to unhelpful responses during their training phase, they are unable to distinguish if the tokens they generate might result in unhelpful responses during inference. To address this issue, we introduce a novel model-agnostic framework named mitigating unhelpfulness with multifaceted AI feedback for emotional support (Muffin). Specifically, Muffin employs a multifaceted AI feedback module to assess the helpfulness of responses generated by a specific model with consideration of multiple factors. Using contrastive learning, it then reduces the likelihood of the model generating unhelpful responses compared to the helpful ones. Experimental results demonstrate that Muffin effectively mitigates the generation of unhelpful responses while slightly increasing response fluency and relevance., Comment: ACL 2024 Findings

Social media data exhibits severe redundancy caused by its noisy nature. It leads to increased training time and model bias in its processing. To address this issue, we propose a novel Generative Deduplication framework for social media data selection by removing semantically duplicate data. While related work involves data selection in task-specific training, our model acts as an efficient pre-processing method to universally enhance social media NLP pipelines. Specifically, we train a generative model via self-supervised learning to predict a keyword to capture the semantics of noisy social media text for deduplication. Meanwhile, time-dimensional Gaussian noise is added to improve training complexity and avoid learning trivial features. Extensive experiments suggest that our model can better reduce training samples while improving performance than baselines. The results show our model's potential to broadly advance social media language understanding in effectiveness and efficiency., Comment: Accepted by Findings of EMNLP 2024

In recent years, research on the interaction between Orbital Angular Momentum (OAM) and matter has seen a continuous influx of investigations. OAM possesses distinct properties, such as additional degrees of freedom, vortex characteristics, and topological properties, which expand its applications in optical communication, optical sensing, and optical force. Through experiments involving the interaction of a chiral metal swastika structure with a SAM-OAM beam generated by a q-plate, we have observed a phenomenon does not present in pure SAM beams. Fourier back focal plane (FBP) imaging under SAM beam excitation easily identifies the chirality and geometric properties of the structure. When the SAM-OAM beam excites the structure, FBP not only identifies its chirality and geometric properties but also distinguishes different OAM topological charges and signs, as well as the degree of elliptic polarization. The stokes parametric FBP imaging reveals asymmetric polarization distribution resulting from the interaction between a vortex beam and the chiral structure. Moreover, it clearly reflects the conversion process of SAM to OAM. The experimental results align well with simulation results. These findings hold valuable insights for the advancement of optical information storage and communication using OAM, opening up new possibilities for further exploration in this field., Comment: 19 pages, 6 figures and 3 pages supporting information

Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcomes. We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA, and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. The classification accuracy of each gene was compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity. This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology., Comment: 36 pages, 8 figures, 3 tables

In this paper, we aim to align large language models with the ever-changing, complex, and diverse human values (e.g., social norms) across time and locations. This presents a challenge to existing alignment techniques, such as supervised fine-tuning, which internalize values within model parameters. To overcome this, we propose an On-the-fly Preference Optimization (OPO) method, which is a real-time alignment that works in a streaming way. It employs an external memory to store established rules for alignment, which can constrain LLMs' behaviors without further training, allowing for convenient updates and customization of human values. We also introduce a scalable evaluation to assess the proposed method more effectively. Experimental results on both human-annotated and auto-generated questions from legal and moral domains indicate the effectiveness of the proposed OPO method. Our code and data are released at https://github.com/GAIR-NLP/OPO.

Deciphering oracle bone scripts plays an important role in Chinese archaeology and philology. However, a significant challenge remains due to the scarcity of oracle character images. To overcome this issue, we propose Diff-Oracle, a novel approach based on diffusion models to generate a diverse range of controllable oracle characters. Unlike traditional diffusion models that operate primarily on text prompts, Diff-Oracle incorporates a style encoder that utilizes style reference images to control the generation style. This encoder extracts style prompts from existing oracle character images, where style details are converted into a text embedding format via a pretrained language-vision model. On the other hand, a content encoder is integrated within Diff-Oracle to capture specific content details from content reference images, ensuring that the generated characters accurately represent the intended glyphs. To effectively train Diff-Oracle, we pre-generate pixel-level paired oracle character images (i.e., style and content images) by an image-to-image translation model. Extensive qualitative and quantitative experiments are conducted on datasets Oracle-241 and OBC306. While significantly surpassing present generative methods in terms of image generation, Diff-Oracle substantially benefits downstream oracle character recognition, outperforming all existing SOTAs by a large margin. In particular, on the challenging OBC306 dataset, Diff-Oracle leads to an accuracy gain of 7.70% in the zero-shot setting and is able to recognize unseen oracle character images with the accuracy of 84.62%, achieving a new benchmark for deciphering oracle bone scripts.

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.