OpenRaiser/Intern-Atlas huggingface dataset:
Fluid Antennas-Enabled Multiuser Uplink: A Low-Complexity Gradient Descent for Total Transmit Power Minimization
Venue: arXiv cs.IT 2024
Year: 2024

We investigate multiuser uplink communication from multiple single-antenna users to a base station (BS), which is equipped with a movable-antenna (MA) array and adopts zero-forcing receivers to decode multiple signals. We aim to optimize the MAs' positions at the BS, to minimize the total transmit power of all users subject to the minimum rate requirement. After applying transformations, we show that the problem is equivalent to minimizing the sum of each eigenvalue's reciprocal of a matrix, which is a function of all MAs' positions. Subsequently, the projected gradient descent (PGD) method is utilized to find a locally optimal solution. In particular, different from the latest related work, we exploit the eigenvalue decomposition to successfully derive a closed-form gradient for the PGD, which facilitates the practical implementation greatly. We demonstrate by simulations that via careful optimization for all MAs' positions in our proposed design, the total transmit power of all users can be decreased significantly as compared to competitive benchmarks.
Neural Speech Embeddings for Speech Synthesis Based on Deep Generative Networks
Venue: arXiv cs.AI 2024
Year: 2024

Brain-to-speech technology represents a fusion of interdisciplinary applications encompassing fields of artificial intelligence, brain-computer interfaces, and speech synthesis. Neural representation learning based intention decoding and speech synthesis directly connects the neural activity to the means of human linguistic communication, which may greatly enhance the naturalness of communication. With the current discoveries on representation learning and the development of the speech synthesis technologies, direct translation of brain signals into speech has shown great promise. Especially, the processed input features and neural speech embeddings which are given to the neural network play a significant role in the overall performance when using deep generative models for speech generation from brain signals. In this paper, we introduce the current brain-to-speech technology with the possibility of speech synthesis from brain signals, which may ultimately facilitate innovation in non-verbal communication. Also, we perform comprehensive analysis on the neural features and neural speech embeddings underlying the neurophysiological activation while performing speech, which may play a significant role in the speech synthesis works.
Banyan: Fast Rotating Leader BFT
Venue: arXiv cs.DC 2024
Year: 2024

This paper presents Banyan, the first rotating leader state machine replication (SMR) protocol that allows transactions to be confirmed in just a single round-trip time in the Byzantine fault tolerance (BFT) setting. Based on minimal alterations to the Internet Computer Consensus (ICC) protocol and with negligible communication overhead, we introduce a novel dual mode mechanism that enables optimal block finalization latency in the fast path. Crucially, the modes of operation are integrated, such that even if the fast path is not effective, no penalties are incurred. Moreover, our algorithm maintains the core attributes of the ICC protocol it is based on, including optimistic responsiveness and rotating leaders without the necessity for a view-change protocol. We prove the correctness of our protocol and provide an open-source implementation of it. Banyan is compared to its predecessor ICC, as well as other well known BFT protocols, in a globally distributed wide-area network. Our evaluation reveals that Banyan reduces latency by up to 30% compared to state-of-the-art protocols, without requiring additional security assumptions.
Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs
Venue: arXiv cs.AI 2024
Year: 2024

Large language models (LLMs) encapsulate a vast amount of factual information within their pre-trained weights, as evidenced by their ability to answer diverse questions across different domains. However, this knowledge is inherently limited, relying heavily on the characteristics of the training data. Consequently, using external datasets to incorporate new information or refine the capabilities of LLMs on previously seen information poses a significant challenge. In this study, we compare two common approaches: unsupervised fine-tuning and retrieval-augmented generation (RAG). We evaluate both approaches on a variety of knowledge-intensive tasks across different topics. Our findings reveal that while unsupervised fine-tuning offers some improvement, RAG consistently outperforms it, both for existing knowledge encountered during training and entirely new knowledge. Moreover, we find that LLMs struggle to learn new factual information through unsupervised fine-tuning, and that exposing them to numerous variations of the same fact during training could alleviate this problem.
A Note on the Convergence of Denoising Diffusion Probabilistic Models
Venue: arXiv cs.LG 2024
Year: 2024

Diffusion models are one of the most important families of deep generative models. In this note, we derive a quantitative upper bound on the Wasserstein distance between the data-generating distribution and the distribution learned by a diffusion model. Unlike previous works in this field, our result does not make assumptions on the learned score function. Moreover, our bound holds for arbitrary data-generating distributions on bounded instance spaces, even those without a density w.r.t. the Lebesgue measure, and the upper bound does not suffer from exponential dependencies. Our main result builds upon the recent work of Mbacke et al. (2023) and our proofs are elementary.
ReshapeIT: Reliable Shape Interaction with Implicit Template for Anatomical Structure Reconstruction
Venue: arXiv cs.CV 2024
Year: 2024

Shape modeling of volumetric medical images is crucial for quantitative analysis and surgical planning in computer-aided diagnosis. To alleviate the burden of expert clinicians, reconstructed shapes are typically obtained from deep learning models, such as Convolutional Neural Networks (CNNs) or transformer-based architectures, followed by the marching cube algorithm. However, automatic shape reconstruction often falls short of perfection due to the limited resolution of images and the absence of shape prior constraints. To overcome these limitations, we propose the Reliable Shape Interaction with Implicit Template (ReShapeIT) network, which models anatomical structures in continuous space rather than discrete voxel grids. ReShapeIT represents an anatomical structure with an implicit template field shared within the same category, complemented by a deformation field. It ensures the implicit template field generates valid templates by strengthening the constraint of the correspondence between the instance shape and the template shape. The valid template shape can then be utilized for implicit generalization. A Template Interaction Module (TIM) is introduced to reconstruct unseen shapes by interacting the valid template shapes with the instance-wise latent codes. Experimental results on three datasets demonstrate the superiority of our approach in anatomical structure reconstruction. The Chamfer Distance/Earth Mover's Distance achieved by ReShapeIT are 0.225/0.318 on Liver, 0.125/0.067 on Pancreas, and 0.414/0.098 on Lung Lobe.
MART: Learning Hierarchical Music Audio Representations with Part-Whole Transformer
Venue: arXiv cs.SD 2024
Year: 2024

Recent research in self-supervised contrastive learning of music representations has demonstrated remarkable results across diverse downstream tasks. However, a prevailing trend in existing methods involves representing equally-sized music clips in either waveform or spectrogram formats, often overlooking the intrinsic part-whole hierarchies within music. In our quest to comprehend the bottom-up structure of music, we introduce MART, a hierarchical music representation learning approach that facilitates feature interactions among cropped music clips while considering their part-whole hierarchies. Specifically, we propose a hierarchical part-whole transformer to capture the structural relationships between music clips in a part-whole hierarchy. Furthermore, a hierarchical contrastive learning objective is crafted to align part-whole music representations at adjacent levels, progressively establishing a multi-hierarchy representation space. The effectiveness of our music representation learning from part-whole hierarchies has been empirically validated across multiple downstream tasks, including music classification and cover song identification.
EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

We introduce EQ-Bench, a novel benchmark designed to evaluate aspects of emotional intelligence in Large Language Models (LLMs). We assess the ability of LLMs to understand complex emotions and social interactions by asking them to predict the intensity of emotional states of characters in a dialogue. The benchmark is able to discriminate effectively between a wide range of models. We find that EQ-Bench correlates strongly with comprehensive multi-domain benchmarks like MMLU (Hendrycks et al., 2020) (r=0.97), indicating that we may be capturing similar aspects of broad intelligence. Our benchmark produces highly repeatable results using a set of 60 English-language questions. We also provide open-source code for an automated benchmarking pipeline at https://github.com/EQ-bench/EQ-Bench and a leaderboard at https://eqbench.com
SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization
Venue: arXiv cs.CV 2024
Year: 2024

Semi-supervised learning has attracted much attention due to its less dependence on acquiring abundant annotations from experts compared to fully supervised methods, which is especially important for medical image segmentation which typically requires intensive pixel/voxel-wise labeling by domain experts. Although semi-supervised methods can improve the performance by utilizing unlabeled data, there are still gaps between fully supervised methods under extremely limited annotation scenarios. In this paper, we propose a simple yet efficient strategy to explore the usage of the Segment Anything Model (SAM) for enhancing semi-supervised medical image segmentation. Concretely, the segmentation model trained with domain knowledge provides information for localization and generating input prompts to the SAM. Then the generated pseudo-labels of SAM are utilized as additional supervision to assist in the learning procedure of the semi-supervised framework. Extensive experiments demonstrate that SemiSAM significantly improves the performance of existing semi-supervised frameworks when only one or a few labeled images are available and shows strong efficiency as a plug-and-play strategy for semi-supervised medical image segmentation.
FOSS: A Self-Learned Doctor for Query Optimizer
Venue: arXiv cs.DB 2024
Year: 2024

Various works have utilized deep learning to address the query optimization problem in database system. They either learn to construct plans from scratch in a bottom-up manner or steer the plan generation behavior of traditional optimizer using hints. While these methods have achieved some success, they face challenges in either low training efficiency or limited plan search space. To address these challenges, we introduce FOSS, a novel framework for query optimization based on deep reinforcement learning. FOSS initiates optimization from the original plan generated by a traditional optimizer and incrementally refines suboptimal nodes of the plan through a sequence of actions. Additionally, we devise an asymmetric advantage model to evaluate the advantage between two plans. We integrate it with a traditional optimizer to form a simulated environment. Leveraging this simulated environment, FOSS can bootstrap itself to rapidly generate a large amount of high-quality simulated experiences. FOSS then learns from these experiences to improve its optimization capability. We evaluate the performance of FOSS on Join Order Benchmark, TPC-DS, and Stack Overflow. The experimental results demonstrate that FOSS outperforms the state-of-the-art methods in terms of latency performance. Compared to PostgreSQL, FOSS achieves speedup ranging from 1.15x to 8.33x in total latency across different benchmarks.
Towards A Flexible Accuracy-Oriented Deep Learning Module Inference Latency Prediction Framework for Adaptive Optimization Algorithms
Venue: arXiv cs.LG 2024
Year: 2024

With the rapid development of Deep Learning, more and more applications on the cloud and edge tend to utilize large DNN (Deep Neural Network) models for improved task execution efficiency as well as decision-making quality. Due to memory constraints, models are commonly optimized using compression, pruning, and partitioning algorithms to become deployable onto resource-constrained devices. As the conditions in the computational platform change dynamically, the deployed optimization algorithms should accordingly adapt their solutions. To perform frequent evaluations of these solutions in a timely fashion, RMs (Regression Models) are commonly trained to predict the relevant solution quality metrics, such as the resulted DNN module inference latency, which is the focus of this paper. Existing prediction frameworks specify different RM training workflows, but none of them allow flexible configurations of the input parameters (e.g., batch size, device utilization rate) and of the selected RMs for different modules. In this paper, a deep learning module inference latency prediction framework is proposed, which i) hosts a set of customizable input parameters to train multiple different RMs per DNN module (e.g., convolutional layer) with self-generated datasets, and ii) automatically selects a set of trained RMs leading to the highest possible overall prediction accuracy, while keeping the prediction time / space consumption as low as possible. Furthermore, a new RM, namely MEDN (Multi-task Encoder-Decoder Network), is proposed as an alternative solution. Comprehensive experiment results show that MEDN is fast and lightweight, and capable of achieving the highest overall prediction accuracy and R-squared value. The Time/Space-efficient Auto-selection algorithm also manages to improve the overall accuracy by 2.5% and R-squared by 0.39%, compared to the MEDN single-selection scheme.
An infinite class of quantum codes derived from duadic constacyclic codes
Venue: arXiv cs.IT 2024
Year: 2024

We present a family of quantum stabilizer codes using the structure of duadic constacyclic codes over $\mathbb{F}_4$. Within this family, quantum codes can possess varying dimensions, and their minimum distances are lower bounded by a square root bound. For each fixed dimension, this allows us to construct an infinite sequence of binary quantum codes with a growing minimum distance. Additionally, we prove that this family of quantum codes includes an infinite subclass of degenerate codes. We also introduce a technique for extending splittings of duadic constacyclic codes, providing new insights into the minimum distance and minimum odd-like weight of specific duadic constacyclic codes. Finally, we provide numerical examples of some quantum codes with short lengths within this family.
Dense X Retrieval: What Retrieval Granularity Should We Use?
Venue: arXiv cs.CL 2024
Year: 2024

Dense retrieval has become a prominent method to obtain relevant context or world knowledge in open-domain NLP tasks. When we use a learned dense retriever on a retrieval corpus at inference time, an often-overlooked design choice is the retrieval unit in which the corpus is indexed, e.g. document, passage, or sentence. We discover that the retrieval unit choice significantly impacts the performance of both retrieval and downstream tasks. Distinct from the typical approach of using passages or sentences, we introduce a novel retrieval unit, proposition, for dense retrieval. Propositions are defined as atomic expressions within text, each encapsulating a distinct factoid and presented in a concise, self-contained natural language format. We conduct an empirical comparison of different retrieval granularity. Our experiments reveal that indexing a corpus by fine-grained units such as propositions significantly outperforms passage-level units in retrieval tasks. Moreover, constructing prompts with fine-grained retrieved units for retrieval-augmented language models improves the performance of downstream QA tasks given a specific computation budget.
System-level Safety Guard: Safe Tracking Control through Uncertain Neural Network Dynamics Models
Venue: arXiv cs.RO 2024
Year: 2024

The Neural Network (NN), as a black-box function approximator, has been considered in many control and robotics applications. However, difficulties in verifying the overall system safety in the presence of uncertainties hinder the deployment of NN modules in safety-critical systems. In this paper, we leverage the NNs as predictive models for trajectory tracking of unknown dynamical systems. We consider controller design in the presence of both intrinsic uncertainty and uncertainties from other system modules. In this setting, we formulate the constrained trajectory tracking problem and show that it can be solved using Mixed-integer Linear Programming (MILP). The proposed MILP-based approach is empirically demonstrated in robot navigation and obstacle avoidance through simulations. The demonstration videos are available at https://xiaolisean.github.io/publication/2023-11-01-L4DC2024.
Reducing Energy Bloat in Large Model Training
Venue: arXiv cs.LG 2024
Year: 2024

Training large AI models on numerous GPUs consumes a massive amount of energy, making power delivery one of the largest limiting factors in building and operating datacenters for AI workloads. However, we observe that not all energy consumed during training directly contributes to end-to-end throughput; a significant portion can be removed without slowing down training. We call this portion energy bloat. In this work, we identify two independent sources of energy bloat in large model training and propose Perseus, a training system that mitigates both. To do this, Perseus obtains the time--energy tradeoff frontier of a large model training job using an efficient graph cut-based algorithm, and schedules computation energy consumption across time to reduce both types of energy bloat. Evaluation on large models, including GPT-3 and Bloom, shows that Perseus reduces the energy consumption of large model training by up to 30% without any throughput loss or hardware modification.
TransMed: Large Language Models Enhance Vision Transformer for Biomedical Image Classification
Venue: arXiv cs.CV 2024
Year: 2024

Few-shot learning has been studied to adapt models to tasks with very few samples. It holds profound significance, particularly in clinical tasks, due to the high annotation cost of medical images. Several works have explored few-shot learning on medical images, yet they still require a large number of medical images for pre-training models to gain domain-specific priors. Vision foundation models recently have achieved remarkable success in natural images. Hence, adapting rapidly advancing vision foundation models from natural images to few-shot clinical tasks holds great promise. MedFMC has recently organized a challenge to shed more light on this topic at NeurIPS 2023. In this work, we present our challenge solution. We observe that a simple variant of fine-tuning with partial freezing shows remarkable performance. Empirical evidence demonstrates that this approach could outperform various common fine-tuning methods under limited sample sizes. Additionally, we explore enhanced utilization of semantic supervision to boost performance. We propose a novel approach that contextualizes labels via large language models (LLMs). Our findings reveal that the context generated by LLMs significantly enhances the discrimination of semantic embeddings for similar categories, resulting in a notable performance improvement of 3%-5% in 1-shot settings compared to commonly employed one-hot labels and other semantic supervision methods. Our solution secures the 1st place in the MedFMC challenge.
LatentMan: Generating Consistent Animated Characters using Image Diffusion Models
Venue: arXiv cs.CV 2024
Year: 2024

We propose a zero-shot approach for generating consistent videos of animated characters based on Text-to-Image (T2I) diffusion models. Existing Text-to-Video (T2V) methods are expensive to train and require large-scale video datasets to produce diverse characters and motions. At the same time, their zero-shot alternatives fail to produce temporally consistent videos with continuous motion. We strive to bridge this gap, and we introduce LatentMan, which leverages existing text-based motion diffusion models to generate diverse continuous motions to guide the T2I model. To boost the temporal consistency, we introduce the Spatial Latent Alignment module that exploits cross-frame dense correspondences that we compute to align the latents of the video frames. Furthermore, we propose Pixel-Wise Guidance to steer the diffusion process in a direction that minimizes visual discrepancies between frames. Our proposed approach outperforms existing zero-shot T2V approaches in generating videos of animated characters in terms of pixel-wise consistency and user preference. Project page https://abdo-eldesokey.github.io/latentman/.
Rate-Splitting Multiple Access for Semantic-Aware Networks: an Age of Incorrect Information Perspective
Venue: arXiv cs.IT 2024
Year: 2024

In this letter, we design a downlink multi-user communication framework based on Rate-Splitting Multiple Access (RSMA) for semantic-aware networks. First, we formulate an optimization problem to obtain the optimal user scheduling, precoding, and power allocation schemes jointly. We consider the metric Age of Incorrect Information (AoII) in the objective function of the formulated problem to maximize the freshness of the overall information to be transmitted. Using big-M and Successive Convex Approximation (SCA) methods, we convert the resulting non-convex problem with conditional objective and constraints into a convex one and propose an iterative algorithm to solve it. By numerical results, we show that RSMA achieves a lower AoII than SDMA owing to its superior performance under multi-user interference.
Performance Characterization of Containerized DNN Training and Inference on Edge Accelerators
Venue: arXiv cs.DC 2024
Year: 2024

Edge devices have typically been used for DNN inferencing. The increase in the compute power of accelerated edges is leading to their use in DNN training also. As privacy becomes a concern on multi-tenant edge devices, Docker containers provide a lightweight virtualization mechanism to sandbox models. But their overheads for edge devices are not yet explored. In this work, we study the impact of containerized DNN inference and training workloads on an NVIDIA AGX Orin edge device and contrast it against bare metal execution on running time, CPU, GPU and memory utilization, and energy consumption. Our analysis shows that there are negligible containerization overheads for individually running DNN training and inference workloads.