OpenRaiser/Intern-Atlas huggingface dataset:
Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing
Venue: arXiv cs.CL 2024
Year: 2024

Aligned large language models (LLMs) are vulnerable to jailbreaking attacks, which bypass the safeguards of targeted LLMs and fool them into generating objectionable content. While initial defenses show promise against token-based threat models, there do not exist defenses that provide robustness against semantic attacks and avoid unfavorable trade-offs between robustness and nominal performance. To meet this need, we propose SEMANTICSMOOTH, a smoothing-based defense that aggregates the predictions of multiple semantically transformed copies of a given input prompt. Experimental results demonstrate that SEMANTICSMOOTH achieves state-of-the-art robustness against GCG, PAIR, and AutoDAN attacks while maintaining strong nominal performance on instruction following benchmarks such as InstructionFollowing and AlpacaEval. The codes will be publicly available at https://github.com/UCSB-NLP-Chang/SemanticSmooth.
Combining Machine Learning with Computational Fluid Dynamics using OpenFOAM and SmartSim
Venue: arXiv cs.LG 2024
Year: 2024

Combining machine learning (ML) with computational fluid dynamics (CFD) opens many possibilities for improving simulations of technical and natural systems. However, CFD+ML algorithms require exchange of data, synchronization, and calculation on heterogeneous hardware, making their implementation for large-scale problems exceptionally challenging. We provide an effective and scalable solution to developing CFD+ML algorithms using open source software OpenFOAM and SmartSim. SmartSim provides an Orchestrator that significantly simplifies the programming of CFD+ML algorithms and a Redis database that ensures highly scalable data exchange between ML and CFD clients. We show how to leverage SmartSim to effectively couple different segments of OpenFOAM with ML, including pre/post-processing applications, solvers, function objects, and mesh motion solvers. We additionally provide an OpenFOAM sub-module with examples that can be used as starting points for real-world applications in CFD+ML.
HypoTermQA: Hypothetical Terms Dataset for Benchmarking Hallucination Tendency of LLMs
Venue: arXiv cs.CL 2024
Year: 2024

Hallucinations pose a significant challenge to the reliability and alignment of Large Language Models (LLMs), limiting their widespread acceptance beyond chatbot applications. Despite ongoing efforts, hallucinations remain a prevalent challenge in LLMs. The detection of hallucinations itself is also a formidable task, frequently requiring manual labeling or constrained evaluations. This paper introduces an automated scalable framework that combines benchmarking LLMs' hallucination tendencies with efficient hallucination detection. We leverage LLMs to generate challenging tasks related to hypothetical phenomena, subsequently employing them as agents for efficient hallucination detection. The framework is domain-agnostic, allowing the use of any language model for benchmark creation or evaluation in any domain. We introduce the publicly available HypoTermQA Benchmarking Dataset, on which state-of-the-art models' performance ranged between 3% and 11%, and evaluator agents demonstrated a 6% error rate in hallucination prediction. The proposed framework provides opportunities to test and improve LLMs. Additionally, it has the potential to generate benchmarking datasets tailored to specific domains, such as law, health, and finance.
HSONet:A Siamese foreground association-driven hard case sample optimization network for high-resolution remote sensing image change detection
Venue: arXiv cs.CV 2024
Year: 2024

In the later training stages, further improvement of the models ability to determine changes relies on how well the change detection (CD) model learns hard cases; however, there are two additional challenges to learning hard case samples: (1) change labels are limited and tend to pointer only to foreground targets, yet hard case samples are prevalent in the background, which leads to optimizing the loss function focusing on the foreground targets and ignoring the background hard cases, which we call imbalance. (2) Complex situations, such as light shadows, target occlusion, and seasonal changes, induce hard case samples, and in the absence of both supervisory and scene information, it is difficult for the model to learn hard case samples directly to accurately obtain the feature representations of the change information, which we call missingness. We propose a Siamese foreground association-driven hard case sample optimization network (HSONet). To deal with this imbalance, we propose an equilibrium optimization loss function to regulate the optimization focus of the foreground and background, determine the hard case samples through the distribution of the loss values, and introduce dynamic weights in the loss term to gradually shift the optimization focus of the loss from the foreground to the background hard cases as the training progresses. To address this missingness, we understand hard case samples with the help of the scene context, propose the scene-foreground association module, use potential remote sensing spatial scene information to model the association between the target of interest in the foreground and the related context to obtain scene embedding, and apply this information to the feature reinforcement of hard cases. Experiments on four public datasets show that HSONet outperforms current state-of-the-art CD methods, particularly in detecting hard case samples.
Foundation Model Transparency Reports
Venue: arXiv cs.LG 2024
Year: 2024

Foundation models are critical digital technologies with sweeping societal impact that necessitates transparency. To codify how foundation model developers should provide transparency about the development and deployment of their models, we propose Foundation Model Transparency Reports, drawing upon the transparency reporting practices in social media. While external documentation of societal harms prompted social media transparency reports, our objective is to institutionalize transparency reporting for foundation models while the industry is still nascent. To design our reports, we identify 6 design principles given the successes and shortcomings of social media transparency reporting. To further schematize our reports, we draw upon the 100 transparency indicators from the Foundation Model Transparency Index. Given these indicators, we measure the extent to which they overlap with the transparency requirements included in six prominent government policies (e.g., the EU AI Act, the US Executive Order on Safe, Secure, and Trustworthy AI). Well-designed transparency reports could reduce compliance costs, in part due to overlapping regulatory requirements across different jurisdictions. We encourage foundation model developers to regularly publish transparency reports, building upon recommendations from the G7 and the White House.
Quadratic Message Passing for Generalized Quadratic Equations Model
Venue: arXiv cs.IT 2024
Year: 2024

For approximate inference in the generalized quadratic equations model, many state-of-the-art algorithms lack any prior knowledge of the target signal structure, exhibits slow convergence, and can not handle any analytic prior knowledge of the target signal structure. So, this paper proposes a new algorithm, Quadratic Message passing (QMP). QMP has a complexity as low as $O(N^{3})$. The SE derived for QMP can capture precisely the per-iteration behavior of the simulated algorithm. Simulation results confirm QMP outperforms many state-of-the-art algorithms.
Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Nucleus instance segmentation from histopathology images suffers from the extremely laborious and expert-dependent annotation of nucleus instances. As a promising solution to this task, annotation-efficient deep learning paradigms have recently attracted much research interest, such as weakly-/semi-supervised learning, generative adversarial learning, etc. In this paper, we propose to formulate annotation-efficient nucleus instance segmentation from the perspective of few-shot learning (FSL). Our work was motivated by that, with the prosperity of computational pathology, an increasing number of fully-annotated datasets are publicly accessible, and we hope to leverage these external datasets to assist nucleus instance segmentation on the target dataset which only has very limited annotation. To achieve this goal, we adopt the meta-learning based FSL paradigm, which however has to be tailored in two substantial aspects before adapting to our task. First, since the novel classes may be inconsistent with those of the external dataset, we extend the basic definition of few-shot instance segmentation (FSIS) to generalized few-shot instance segmentation (GFSIS). Second, to cope with the intrinsic challenges of nucleus segmentation, including touching between adjacent cells, cellular heterogeneity, etc., we further introduce a structural guidance mechanism into the GFSIS network, finally leading to a unified Structurally-Guided Generalized Few-Shot Instance Segmentation (SGFSIS) framework. Extensive experiments on a couple of publicly accessible datasets demonstrate that, SGFSIS can outperform other annotation-efficient learning baselines, including semi-supervised learning, simple transfer learning, etc., with comparable performance to fully supervised learning with less than 5% annotations.
Trimma: Trimming Metadata Storage and Latency for Hybrid Memory Systems
Venue: arXiv cs.AR 2024
Year: 2024

Hybrid main memory systems combine both performance and capacity advantages from heterogeneous memory technologies. With larger capacities, higher associativities, and finer granularities, hybrid memory systems currently exhibit significant metadata storage and lookup overheads for flexibly remapping data blocks between the two memory tiers. To alleviate the inefficiencies of existing designs, we propose Trimma, the combination of a multi-level metadata structure and an efficient metadata cache design. Trimma uses a multi-level metadata table to only track truly necessary address remap entries. The saved memory space is effectively utilized as extra DRAM cache capacity to improve performance. Trimma also uses separate formats to store the entries with non-identity and identity address mappings. This improves the overall remap cache hit rate, further boosting the performance. Trimma is transparent to software and compatible with various types of hybrid memory systems. When evaluated on a representative hybrid memory system with HBM3 and DDR5, Trimma achieves up to 1.68$\times$ and on average 1.33$\times$ speedup benefits, compared to state-of-the-art hybrid memory designs. These results show that Trimma effectively addresses metadata management overheads, especially for future scalable large-scale hybrid memory architectures.
SPC-NeRF: Spatial Predictive Compression for Voxel Based Radiance Field
Venue: arXiv cs.CV 2024
Year: 2024

Representing the Neural Radiance Field (NeRF) with the explicit voxel grid (EVG) is a promising direction for improving NeRFs. However, the EVG representation is not efficient for storage and transmission because of the terrific memory cost. Current methods for compressing EVG mainly inherit the methods designed for neural network compression, such as pruning and quantization, which do not take full advantage of the spatial correlation of voxels. Inspired by prosperous digital image compression techniques, this paper proposes SPC-NeRF, a novel framework applying spatial predictive coding in EVG compression. The proposed framework can remove spatial redundancy efficiently for better compression performance.Moreover, we model the bitrate and design a novel form of the loss function, where we can jointly optimize compression ratio and distortion to achieve higher coding efficiency. Extensive experiments demonstrate that our method can achieve 32% bit saving compared to the state-of-the-art method VQRF on multiple representative test datasets, with comparable training time.
Generative AI in Vision: A Survey on Models, Metrics and Applications
Venue: arXiv cs.CV 2024
Year: 2024

Generative AI models have revolutionized various fields by enabling the creation of realistic and diverse data samples. Among these models, diffusion models have emerged as a powerful approach for generating high-quality images, text, and audio. This survey paper provides a comprehensive overview of generative AI diffusion and legacy models, focusing on their underlying techniques, applications across different domains, and their challenges. We delve into the theoretical foundations of diffusion models, including concepts such as denoising diffusion probabilistic models (DDPM) and score-based generative modeling. Furthermore, we explore the diverse applications of these models in text-to-image, image inpainting, and image super-resolution, along with others, showcasing their potential in creative tasks and data augmentation. By synthesizing existing research and highlighting critical advancements in this field, this survey aims to provide researchers and practitioners with a comprehensive understanding of generative AI diffusion and legacy models and inspire future innovations in this exciting area of artificial intelligence.
Performance Tradeoff Between Overhead and Achievable SNR in RIS Beam Training
Venue: arXiv cs.IT 2024
Year: 2024

Efficient beam training is the key challenge in the codebook-based configuration of reconfigurable intelligent surfaces (RISs) because the beam training overhead can have a strong impact on the achievable system performance. In this paper, we study the performance tradeoff between overhead and achievable signal-to-noise ratio (SNR) in RIS beam training while taking into account the size of the targeted coverage area, the RIS response time, and the delay for feedback transmissions. Thereby, we consider three common beam training strategies: full search (FS), hierarchical search (HS), and tracking-based search (TS). Our analysis shows that the codebook-based illumination of a given coverage area can be realized with wide- or narrow-beam designs, which result in two different scaling laws for the achievable SNR. Similarly, there are two regimes for the overhead, where the number of pilot symbols required for reliable beam training is dependent on and independent of the SNR, respectively. Based on these insights, we investigate the impact of the beam training overhead on the effective rate and provide an upper bound on the user velocity for which the overhead is negligible. Moreover, when the overhead is not negligible, we show that TS beam training achieves higher effective rates than HS and FS beam training, while HS beam training may or may not outperform FS beam training, depending on the RIS response time, feedback delay, and codebook size. Finally, we present numerical simulation results that verify our theoretical analysis. In particular, our results confirm the existence of the proposed regimes, reveal that fast RISs can lead to negligible overhead for FS beam training, and show that large feedback delays can significantly reduce the performance for HS beam training.
Immunization against harmful fine-tuning attacks
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) are often trained with safety guards intended to prevent harmful text generation. However, such safety training can be removed by fine-tuning the LLM on harmful datasets. While this emerging threat (harmful fine-tuning attacks) has been characterized by previous work, there is little understanding of how we should proceed in constructing and validating defenses against these attacks especially in the case where defenders would not have control of the fine-tuning process. We introduce a formal framework based on the training budget of an attacker which we call "Immunization" conditions. Using a formal characterisation of the harmful fine-tuning problem, we provide a thorough description of what a successful defense must comprise of and establish a set of guidelines on how rigorous defense research that gives us confidence should proceed.
Graph Learning with Distributional Edge Layouts
Venue: arXiv cs.LG 2024
Year: 2024

Graph Neural Networks (GNNs) learn from graph-structured data by passing local messages between neighboring nodes along edges on certain topological layouts. Typically, these topological layouts in modern GNNs are deterministically computed (e.g., attention-based GNNs) or locally sampled (e.g., GraphSage) under heuristic assumptions. In this paper, we for the first time pose that these layouts can be globally sampled via Langevin dynamics following Boltzmann distribution equipped with explicit physical energy, leading to higher feasibility in the physical world. We argue that such a collection of sampled/optimized layouts can capture the wide energy distribution and bring extra expressivity on top of WL-test, therefore easing downstream tasks. As such, we propose Distributional Edge Layouts (DELs) to serve as a complement to a variety of GNNs. DEL is a pre-processing strategy independent of subsequent GNN variants, thus being highly flexible. Experimental results demonstrate that DELs consistently and substantially improve a series of GNN baselines, achieving state-of-the-art performance on multiple datasets.
Improving behavior based authentication against adversarial attack using XAI
Venue: arXiv cs.CR 2024
Year: 2024

In recent years, machine learning models, especially deep neural networks, have been widely used for classification tasks in the security domain. However, these models have been shown to be vulnerable to adversarial manipulation: small changes learned by an adversarial attack model, when applied to the input, can cause significant changes in the output. Most research on adversarial attacks and corresponding defense methods focuses only on scenarios where adversarial samples are directly generated by the attack model. In this study, we explore a more practical scenario in behavior-based authentication, where adversarial samples are collected from the attacker. The generated adversarial samples from the model are replicated by attackers with a certain level of discrepancy. We propose an eXplainable AI (XAI) based defense strategy against adversarial attacks in such scenarios. A feature selector, trained with our method, can be used as a filter in front of the original authenticator. It filters out features that are more vulnerable to adversarial attacks or irrelevant to authentication, while retaining features that are more robust. Through comprehensive experiments, we demonstrate that our XAI based defense strategy is effective against adversarial attacks and outperforms other defense strategies, such as adversarial training and defensive distillation.
Enabling Communication and Control Co-Design in 6G Networks
Venue: arXiv cs.NI 2024
Year: 2024

Networked control systems (NCSs), which are feedback control loops closed over a communication network, have been a popular research topic over the past decades. Numerous works in the literature propose novel algorithms and protocols with joint consideration of communication and control. However, the vast majority of the recent research results, which have shown remarkable performance improvements if a cross-layer methodology is followed, have not been widely adopted by the industry. In this work, we review the shortcomings of today's mobile networks that render cross-layer solutions, such as semantic and goal-oriented communications, very challenging in practice. To tackle this, we propose a new framework for 6G user plane design that simplifies the adoption of recent research results in networked control, thereby facilitating the joint communication and control design in next-generation mobile networks.
Parameterized and approximation algorithms for coverings points with segments in the plane
Venue: arXiv cs.CG 2024
Year: 2024

We study parameterized and approximation algorithms for a variant of Set Cover, where the universe of elements to be covered consists of points in the plane and the sets with which the points should be covered are segments. We call this problem Segment Set Cover. We also consider a relaxation of the problem called $δ$-extension, where we need to cover the points by segments that are extended by a tiny fraction, but we compare the solution's quality to the optimum without extension. For the unparameterized variant, we prove that Segment Set Cover does not admit a PTAS unless $\mathsf{P}=\mathsf{NP}$, even if we restrict segments to be axis-parallel and allow $\frac{1}{2}$-extension. On the other hand, we show that parameterization helps for the tractability of Segment Set Cover: we give an FPT algorithm for unweighted Segment Set Cover parameterized by the solution size $k$, a parameterized approximation scheme for Weighted Segment Set Cover with $k$ being the parameter, and an FPT algorithm for Weighted Segment Set Cover with $δ$-extension parameterized by $k$ and $δ$. In the last two results, relaxing the problem is probably necessary: we prove that Weighted Segment Set Cover without any relaxation is $\mathsf{W}[1]$-hard and, assuming ETH, there does not exist an algorithm running in time $f(k)\cdot n^{o(k / \log k)}$. This holds even if one restricts attention to axis-parallel segments.
A fast implementation of the good-suffix array for the Boyer-Moore string matching algorithm
Venue: arXiv cs.DS 2024
Year: 2024

String matching is the problem of finding all the occurrences of a pattern in a text. It has been intensively studied and the Boyer-Moore string matching algorithm is probably one of the most famous solution to this problem. This algorithm uses two precomputed shift tables called the good-suffix table and the bad-character table. The good-suffix table is tricky to compute in linear time. Text book solutions perform redundant operations. Here we present a fast implementation for this good-suffix table based on a tight analysis of the pattern. Experimental results show two versions of this new implementation are the fastest in almost all tested situations.
Covert Communication Over Additive-Noise Channels
Venue: arXiv cs.IT 2024
Year: 2024

We study the fundamental limits of covert communications over general memoryless additive-noise channels. We assume that the legitimate receiver and the eavesdropper share the same channel and therefore see the same outputs. Under mild integrability assumptions, we find a general upper bound on the square-root scaling constant, which only involves the variance of the logarithm of the probability density function of the noise. Furthermore, we show that, under some additional assumptions, this upper bound is tight. We also provide upper bounds on the length of the secret key required to achieve the optimal scaling.