arXiv subjects

Sheng Shen

Publications and source records attributed to Sheng Shen.

At least 37 records · Page 2Linked to original sources

arXiv2024

An Exploration of Parallel Imaging System for Very-low Field (50mT) MRI Scanner

Reducing the scanning time of very-low field (VLF) magnetic resonance imaging (MRI) scanners, commonly employed for stroke diagnosis, can enhance patient comfort and operational efficiency. The conventional parallel imaging (PI) technique for high-field MRI should be tailored to apply here, considering the differences in the direction of the main magnetic field and the presence of noise. A VLF-specific PI algorithm and phased-array coil are proposed, marking the first application of PI in VLF MRI. Reconstruction quality is enhanced by denoising undersampled k-space data using a linear-prediction based Kalman filter. Subsequently, the denoised k-space data are nonlinearly mapped from the original space onto a high-dimensional feature space, utilizing a polynomial feature mapping defined nonlinear frame. Frame parameters are calculated using auto-calibration signals (ACS) from the center k-space, and missing phase-encoding lines in the original space are estimated using acquired lines in the feature space. An 8-channel phased-array coil, designed for a vertical main magnetic field, is decoupled using geometric overlap and a low input impedance (LII) preamplifier. Healthy volunteer head imaging experiments using the proposed PI technique exhibit the lowest mean-squared-error (MSE) value and the highest peak-signal-to-noise (PSNR) and structural similarity index (SSIM) values compared to two widely used PI methods. The proposed PI technique enables the VLF MRI scanner to achieve similar image quality and a 72.5% improvement in signal-to-noise ratio (SNR) compared to fully sampled images while requiring less than 50% of the scan time. We present a PI technique tailored for VLF MRI scanner for the first time, along with potential research direction to achieve greater reduction factor.

physics.med-ph↗

arXiv2024

Accelerating Low-field MRI: From Compressed Sensing to Deep Learning Reconstruction with CNNs and Transformers

Portable, low-field Magnetic Resonance Imaging (MRI) scanners are increasingly being deployed in clinical settings. However, key barriers to their widespread use include low signal-to-noise ratio (SNR), generally low image quality, and long scan durations. Hence, methods for accelerating acquisition and boosting image quality are critically important to enable clinically actionable, high-quality imaging in these systems. Despite the role that compressed sensing (CS) and deep learning (DL)-based methods have played in improving image quality for high-field MRI, their adoption for low-field imaging is still in its infancy, and it remains unclear how robust these methods are in low-SNR regimes. Here, we propose, investigate, and compare four reconstruction approaches: (i) L1-wavelet CS; (ii) a data-driven network; (iii) an unrolled network; and (iv) a Swin Transformer Cascade. We evaluate their performance across a range of SNR values using publicly available datasets and ultra-low field (6.5 mT) MRI data. Our results show that the unrolled network and Swin Transformer cascade outperform CS and data-driven models. While transformer-based models achieve the highest performance at high SNR, unrolled convolution-based networks are more robust in ultra-low SNR settings and often outperform transformers, indicating that simpler DL architectures may be better suited to low-field MRI. This work highlights both the potential and limitations of advanced reconstruction techniques in low-field MRI and pinpoints effective DL strategies for addressing SNR challenges.

physics.med-ph↗

arXiv2024

Reimagining Linear Probing: Kolmogorov-Arnold Networks in Transfer Learning

This paper introduces Kolmogorov-Arnold Networks (KAN) as an enhancement to the traditional linear probing method in transfer learning. Linear probing, often applied to the final layer of pre-trained models, is limited by its inability to model complex relationships in data. To address this, we propose substituting the linear probing layer with KAN, which leverages spline-based representations to approximate intricate functions. In this study, we integrate KAN with a ResNet-50 model pre-trained on ImageNet and evaluate its performance on the CIFAR-10 dataset. We perform a systematic hyperparameter search, focusing on grid size and spline degree (k), to optimize KAN's flexibility and accuracy. Our results demonstrate that KAN consistently outperforms traditional linear probing, achieving significant improvements in accuracy and generalization across a range of configurations. These findings indicate that KAN offers a more powerful and adaptable alternative to conventional linear probing techniques in transfer learning.

cs.LG↗

arXiv2024

MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens

Multimodal interleaved datasets featuring free-form interleaved sequences of images and text are crucial for training frontier large multimodal models (LMMs). Despite the rapid progression of open-source LMMs, there remains a pronounced scarcity of large-scale, diverse open-source multimodal interleaved datasets. In response, we introduce MINT-1T, the most extensive and diverse open-source Multimodal INTerleaved dataset to date. MINT-1T comprises one trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. As scaling multimodal interleaved datasets requires substantial engineering effort, sharing the data curation process and releasing the dataset greatly benefits the community. Our experiments show that LMMs trained on MINT-1T rival the performance of models trained on the previous leading dataset, OBELICS. Our data and code will be released at https://github.com/mlfoundations/MINT-1T.

cs.CV↗

arXiv2024

Enhancing Large Vision Language Models with Self-Training on Image Comprehension

Large vision language models (LVLMs) integrate large language models (LLMs) with pre-trained vision encoders, thereby activating the perception capability of the model to understand image inputs for different queries and conduct subsequent reasoning. Improving this capability requires high-quality vision-language data, which is costly and labor-intensive to acquire. Self-training approaches have been effective in single-modal settings to alleviate the need for labeled data by leveraging model's own generation. However, effective self-training remains a challenge regarding the unique visual perception and reasoning capability of LVLMs. To address this, we introduce Self-Training on Image Comprehension (STIC), which emphasizes a self-training approach specifically for image comprehension. First, the model self-constructs a preference dataset for image descriptions using unlabeled images. Preferred responses are generated through a step-by-step prompt, while dis-preferred responses are generated from either corrupted images or misleading prompts. To further self-improve reasoning on the extracted visual information, we let the model reuse a small portion of existing instruction-tuning data and append its self-generated image descriptions to the prompts. We validate the effectiveness of STIC across seven different benchmarks, demonstrating substantial performance gains of 4.0% on average while using 70% less supervised fine-tuning data than the current method. Further studies investigate various components of STIC and highlight its potential to leverage vast quantities of unlabeled images for self-training. Code and data are made publicly available.

cs.CV↗

arXiv2024

Physical Vapor Deposition of High Mobility P-type Tellurium and its Applications for Gate-tunable van der Waals PN Photodiodes

Recently tellurium (Te) has attracted resurgent interests due to its p-type characteristics and outstanding ambient environmental stability. Here we present a substrate engineering based physical vapor deposition method to synthesize high-quality Te nanoflakes and achieved a field-effect hole mobility of 1500 cm2/Vs, which is, to the best of our knowledge, the highest among the existing synthesized van der Waals p-type semiconductors. The high mobility Te enables the fabrication of Te/MoS2 pn diodes with highly gate-tunable electronic and optoelectronic characteristics. The Te/MoS2 heterostructure can be used as a visible range photodetector with a current responsivity up to 630 A/W, which is about one order of magnitude higher than the one achieved using p-type Si-MoS2 PN photodiodes. The photo response of the Te/MoS2 heterojunction also exhibits strong gate tunability due to their ultrathin thickness and unique band structures. The successful synthesis of high mobility Te and the enabled Te/MoS2 photodiodes show promise for the development of highly tunable and ultrathin photodetectors.

physics.app-ph↗

arXiv2024

DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable Diffusion

Diffusion models, known for their powerful generative capabilities, play a crucial role in addressing real-world super-resolution challenges. However, these models often focus on improving local textures while neglecting the impacts of global degradation, which can significantly reduce semantic fidelity and lead to inaccurate reconstructions and suboptimal super-resolution performance. To address this issue, we introduce a novel two-stage, degradation-aware framework that enhances the diffusion model's ability to recognize content and degradation in low-resolution images. In the first stage, we employ unsupervised contrastive learning to obtain representations of image degradations. In the second stage, we integrate a degradation-aware module into a simplified ControlNet, enabling flexible adaptation to various degradations based on the learned representations. Furthermore, we decompose the degradation-aware features into global semantics and local details branches, which are then injected into the diffusion denoising module to modulate the target generation. Our method effectively recovers semantically precise and photorealistic details, particularly under significant degradation conditions, demonstrating state-of-the-art performance across various benchmarks. Codes will be released at https://github.com/bichunyang419/DeeDSR.

cs.CV↗

arXiv2024

LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement

Pretrained large language models (LLMs) are currently state-of-the-art for solving the vast majority of natural language processing tasks. While many real-world applications still require fine-tuning to reach satisfactory levels of performance, many of them are in the low-data regime, making fine-tuning challenging. To address this, we propose LLM2LLM, a targeted and iterative data augmentation strategy that uses a teacher LLM to enhance a small seed dataset by augmenting additional data that can be used for fine-tuning on a specific task. LLM2LLM (1) fine-tunes a baseline student LLM on the initial seed data, (2) evaluates and extracts data points that the model gets wrong, and (3) uses a teacher LLM to generate synthetic data based on these incorrect data points, which are then added back into the training data. This approach amplifies the signal from incorrectly predicted data points by the LLM during training and reintegrates them into the dataset to focus on more challenging examples for the LLM. Our results show that LLM2LLM significantly enhances the performance of LLMs in the low-data regime, outperforming both traditional fine-tuning and other data augmentation baselines. LLM2LLM reduces the dependence on labor-intensive data curation and paves the way for more scalable and performant LLM solutions, allowing us to tackle data-constrained domains and tasks. We achieve improvements up to 24.2% on the GSM8K dataset, 32.6% on CaseHOLD, 32.0% on SNIPS, 52.6% on TREC and 39.8% on SST-2 over regular fine-tuning in the low-data regime using a Llama-2-7B student model. Our code is available at https://github.com/SqueezeAILab/LLM2LLM .

cs.CL↗

arXiv2024

RAFT: Adapting Language Model to Domain Specific RAG

Pretraining Large Language Models (LLMs) on large corpora of textual data is now a standard paradigm. When using these LLMs for many downstream applications, it is common to additionally bake in new knowledge (e.g., time-critical news, or private domain knowledge) into the pretrained model either through RAG-based-prompting, or fine-tuning. However, the optimal methodology for the model to gain such new knowledge remains an open question. In this paper, we present Retrieval Augmented FineTuning (RAFT), a training recipe that improves the model's ability to answer questions in a "open-book" in-domain settings. In RAFT, given a question, and a set of retrieved documents, we train the model to ignore those documents that don't help in answering the question, which we call, distractor documents. RAFT accomplishes this by citing verbatim the right sequence from the relevant document that would help answer the question. This coupled with RAFT's chain-of-thought-style response helps improve the model's ability to reason. In domain-specific RAG, RAFT consistently improves the model's performance across PubMed, HotpotQA, and Gorilla datasets, presenting a post-training recipe to improve pre-trained LLMs to in-domain RAG. RAFT's code and demo are open-sourced at github.com/ShishirPatil/gorilla.

cs.CL↗

arXiv2024

Electrically Programmable Pixelated Graphene-Integrated Plasmonic Metasurfaces for Coherent Mid-Infrared Emission

Active metasurfaces have recently emerged as compact, lightweight, and efficient platforms for dynamic control of electromagnetic fields and optical responses. However, the complexities associated with their post-fabrication tunability significantly hinder their widespread applications, especially for the mid-infrared range due to material scarcity and design intricacy. Here, we experimentally demonstrate highly dynamic, pixelated modulations of coherent mid-infrared emission based on an electrically programmable plasmonic metasurface integrated with graphene field effect transistors (Gr-FETs). The ultrabroad infrared transparency of graphene allows for free-form control over plasmonic meta-atoms, thus achieving coherent mid-infrared states across a broad range of wavelengths and polarizations. The spatial temperature modulation generated by Gr-FETs is effectively synergized with the emissivity control by the localized surface plasmon polaritons from gold nanoantennas. This integrated temperature-emissivity modulation of metasurfaces is systematically extended to form a pixelated 2D array, envisioning new approaches toward scalable 2D electrical wiring for densely packed, independently controlled pixels.

physics.optics↗

arXiv2023

Reinforcement Unlearning

Machine unlearning refers to the process of mitigating the influence of specific training data on machine learning models based on removal requests from data owners. However, one important area that has been largely overlooked in the research of unlearning is reinforcement learning. Reinforcement learning focuses on training an agent to make optimal decisions within an environment to maximize its cumulative rewards. During the training, the agent tends to memorize the features of the environment, which raises a significant concern about privacy. As per data protection regulations, the owner of the environment holds the right to revoke access to the agent's training data, thus necessitating the development of a novel and pressing research field, known as \emph{reinforcement unlearning}. Reinforcement unlearning focuses on revoking entire environments rather than individual data samples. This unique characteristic presents three distinct challenges: 1) how to propose unlearning schemes for environments; 2) how to avoid degrading the agent's performance in remaining environments; and 3) how to evaluate the effectiveness of unlearning. To tackle these challenges, we propose two reinforcement unlearning methods. The first method is based on decremental reinforcement learning, which aims to erase the agent's previously acquired knowledge gradually. The second method leverages environment poisoning attacks, which encourage the agent to learn new, albeit incorrect, knowledge to remove the unlearning environment. Particularly, to tackle the third challenge, we introduce the concept of ``environment inference attack'' to evaluate the unlearning outcomes.

cs.CR↗

arXiv2023

Brochosome-inspired binary metastructures for pixel-by-pixel thermal signature control

In nature, nano/micro-structured materials are adopted by various species to generate colors or achieve camouflage. Here, inspired by leafhopper-generated brochosomes, we design binary metastructures composed of pixel twins to achieve pixelated thermal signature control at the microscale. In the infrared range, the pixel twins exhibit distinct emissivities, creating thermal counterparts of '0-1' binary states for storing and displaying information. In the visible range, the engineered surface morphology of the pixel twins enables similar scattering behaviors, rendering the pixel twins visually indistinguishable, which achieves the camouflage of stored information. Compared to the previous work based on plasmonic holographic metasurfaces, the brochosome-like pixel twins are thermally driven, and their structure-enabled functions do not rely on permittivities of any specific material. The unique combination of visible camouflage and infrared display provides a systemic solution to the spatial control of thermal signatures, and has important implications for optical security, anticounterfeiting, and data encryption.

physics.optics↗

arXiv2023

Large Language Models are Visual Reasoning Coordinators

Visual reasoning requires multimodal perception and commonsense cognition of the world. Recently, multiple vision-language models (VLMs) have been proposed with excellent commonsense reasoning ability in various domains. However, how to harness the collective power of these complementary VLMs is rarely explored. Existing methods like ensemble still struggle to aggregate these models with the desired higher-order communications. In this work, we propose Cola, a novel paradigm that coordinates multiple VLMs for visual reasoning. Our key insight is that a large language model (LLM) can efficiently coordinate multiple VLMs by facilitating natural language communication that leverages their distinct and complementary capabilities. Extensive experiments demonstrate that our instruction tuning variant, Cola-FT, achieves state-of-the-art performance on visual question answering (VQA), outside knowledge VQA, visual entailment, and visual spatial reasoning tasks. Moreover, we show that our in-context learning variant, Cola-Zero, exhibits competitive performance in zero and few-shot settings, without finetuning. Through systematic ablation studies and visualizations, we validate that a coordinator LLM indeed comprehends the instruction prompts as well as the separate functionalities of VLMs; it then coordinates them to enable impressive visual reasoning capabilities.

cs.CV↗

arXiv2023

AvalonBench: Evaluating LLMs Playing the Game of Avalon

In this paper, we explore the potential of Large Language Models (LLMs) Agents in playing the strategic social deduction game, Resistance Avalon. Players in Avalon are challenged not only to make informed decisions based on dynamically evolving game phases, but also to engage in discussions where they must deceive, deduce, and negotiate with other players. These characteristics make Avalon a compelling test-bed to study the decision-making and language-processing capabilities of LLM Agents. To facilitate research in this line, we introduce AvalonBench - a comprehensive game environment tailored for evaluating multi-agent LLM Agents. This benchmark incorporates: (1) a game environment for Avalon, (2) rule-based bots as baseline opponents, and (3) ReAct-style LLM agents with tailored prompts for each role. Notably, our evaluations based on AvalonBench highlight a clear capability gap. For instance, models like ChatGPT playing good-role got a win rate of 22.2% against rule-based bots playing evil, while good-role bot achieves 38.2% win rate in the same setting. We envision AvalonBench could be a good test-bed for developing more advanced LLMs (with self-playing) and agent frameworks that can effectively model the layered complexities of such game environments.

cs.AI↗

arXiv2023

HallE-Control: Controlling Object Hallucination in Large Multimodal Models

Current Large Multimodal Models (LMMs) achieve remarkable progress, yet there remains significant uncertainty regarding their ability to accurately apprehend visual details, that is, in performing detailed captioning. To address this, we introduce $\textit{CCEval}$, a GPT-4 assisted evaluation method for detailed captioning. Interestingly, while LMMs demonstrate minimal object existence hallucination in existing VQA benchmarks, our proposed evaluation reveals continued susceptibility to such hallucinations. In this paper, we make the first attempt to investigate such hallucination from different aspects, including image resolution, the language decoder size, and instruction data amount, quality, granularity. Our findings underscore the unwarranted inference when the language description includes details at a finer object granularity than what the vision module can ground or verify, thus inducing hallucination. To control such hallucinations, we further attribute the reliability of captioning to contextual knowledge (involving only contextually grounded objects) and parametric knowledge (containing inferred objects by the model). Thus, we introduce $\textit{HallE-Control}$, a controllable LMM in terms of $\textbf{Hall}$ucination in object $\textbf{E}$xistence. HallE-Control can condition the captioning to shift between (i) exclusively depicting contextual knowledge for grounded objects and (ii) blending it with parametric knowledge to imagine inferred objects. Our method reduces hallucination by 44% compared to LLaVA$_{7B}$ and maintains the object coverage.

cs.CV↗

arXiv2023

Aligning Large Multimodal Models with Factually Augmented RLHF

Large Multimodal Models (LMM) are built across modalities and the misalignment between two modalities can result in "hallucination", generating textual outputs that are not grounded by the multimodal information in context. To address the multimodal misalignment issue, we adapt the Reinforcement Learning from Human Feedback (RLHF) from the text domain to the task of vision-language alignment, where human annotators are asked to compare two responses and pinpoint the more hallucinated one, and the vision-language model is trained to maximize the simulated human rewards. We propose a new alignment algorithm called Factually Augmented RLHF that augments the reward model with additional factual information such as image captions and ground-truth multi-choice options, which alleviates the reward hacking phenomenon in RLHF and further improves the performance. We also enhance the GPT-4-generated training data (for vision instruction tuning) with previously available human-written image-text pairs to improve the general capabilities of our model. To evaluate the proposed approach in real-world scenarios, we develop a new evaluation benchmark MMHAL-BENCH with a special focus on penalizing hallucinations. As the first LMM trained with RLHF, our approach achieves remarkable improvement on the LLaVA-Bench dataset with the 94% performance level of the text-only GPT-4 (while previous best methods can only achieve the 87% level), and an improvement by 60% on MMHAL-BENCH over other baselines. We opensource our code, model, data at https://llava-rlhf.github.io.

cs.CV↗

arXiv2023

Nanodevice-Enabled Near-Field Thermal Radiation between Sub-Wavelength Surfaces

With the continuous advancement of nanotechnology, nanodevices have become crucial components in computing, sensing and energy conversion applications. However, the structures of nanodevices typically possess sub-wavelength dimensions and separations, which pose significant challenges for understanding energy transport phenomena in nanodevices. Here, based on a judiciously designed thermal nanodevice, we report the first measurement of near-field energy transport between two coplanar sub-wavelength structures over temperature bias up to ~190 K. Our experimental results demonstrate a remarkable 20-fold enhancement in heat transfer beyond blackbody radiation. In contrast with the well-established near-field interactions between two semi-infinite bodies, the sub-wavelength confinements in nanodevices lead to the increased polariton scattering and the reduction of supporting modes and therefore a lower heat flow at a given separation. Our work unveils exciting opportunities for the rational design of nanodevices, particularly for on-chip near-field energy transport, with important implications for the development of efficient nanodevices for energy harvesting and thermal management.

physics.optics↗

arXiv2023

AgentBench: Evaluating LLMs as Agents

The potential of Large Language Model (LLM) as agents has been widely acknowledged recently. Thus, there is an urgent need to quantitatively \textit{evaluate LLMs as agents} on challenging tasks in interactive environments. We present AgentBench, a multi-dimensional benchmark that consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities. Our extensive test over \num API-based and open-sourced (OSS) LLMs shows that, while top commercial LLMs present a strong ability of acting as agents in complex environments, there is a significant disparity in performance between them and many OSS competitors that are no larger than 70B. We identify the typical reasons of failures in environments and LLMs, showing that poor long-term reasoning, decision-making, and instruction following abilities are the main obstacles for developing usable LLM agents. Improving instruction following and training on high quality multi-round alignment data could improve agent performance. And different from existing assumptions, training on code present ambivalent impacts on different agent tasks. Datasets, environments, and an integrated evaluation package for AgentBench are released at https://github.com/THUDM/AgentBench.

cs.AI↗