OpenRaiser/Intern-Atlas huggingface dataset:
Tail-Erasure-Correcting Codes
Venue: arXiv cs.IT 2024
Year: 2024

The increasing demand for data storage has prompted the exploration of new techniques, with molecular data storage being a promising alternative. In this work, we develop coding schemes for a new storage paradigm that can be represented as a collection of two-dimensional arrays. Motivated by error patterns observed in recent prototype architectures, our study focuses on correcting erasures in the last few symbols of each row, and also correcting arbitrary deletions across rows. We present code constructions and explicit encoders and decoders that are shown to be nearly optimal in many scenarios. We show that the new coding schemes are capable of effectively mitigating these errors, making these emerging storage platforms potentially promising solutions.
Low-rank Attention Side-Tuning for Parameter-Efficient Fine-Tuning
Venue: arXiv cs.CV 2024
Year: 2024

In finetuning a large pretrained model to downstream tasks, parameter-efficient fine-tuning (PEFT) methods can effectively finetune pretrained models with few trainable parameters, but suffer from high GPU memory consumption and slow training speed. Because learnable parameters from these methods are entangled with the pretrained model, gradients related to the frozen pretrained model's parameters have to be computed and stored during finetuning. We propose Low-rank Attention Side-Tuning (LAST), which disentangles the trainable module from the pretrained model by freezing not only parameters but also outputs of the pretrained network. LAST trains a side-network composed of only low-rank self-attention modules. By viewing the pretrained model as a frozen feature extractor, the side-network takes intermediate output from the pretrained model and focus on learning task-specific knowledge. We also show that LAST can be highly parallel across multiple optimization objectives, making it very efficient in downstream task adaptation, for example, in finding optimal hyperparameters. LAST outperforms previous state-of-the-art methods on VTAB-1K and other visual adaptation tasks with roughly only 30\% of GPU memory footprint and 60\% of training time compared to existing PEFT methods, but achieves significantly higher accuracy.
Provably learning a multi-head attention layer
Venue: arXiv cs.LG 2024
Year: 2024

The multi-head attention layer is one of the key components of the transformer architecture that sets it apart from traditional feed-forward models. Given a sequence length $k$, attention matrices $\mathbfΘ_1,\ldots,\mathbfΘ_m\in\mathbb{R}^{d\times d}$, and projection matrices $\mathbf{W}_1,\ldots,\mathbf{W}_m\in\mathbb{R}^{d\times d}$, the corresponding multi-head attention layer $F: \mathbb{R}^{k\times d}\to \mathbb{R}^{k\times d}$ transforms length-$k$ sequences of $d$-dimensional tokens $\mathbf{X}\in\mathbb{R}^{k\times d}$ via $F(\mathbf{X}) \triangleq \sum^m_{i=1} \mathrm{softmax}(\mathbf{X}\mathbfΘ_i\mathbf{X}^\top)\mathbf{X}\mathbf{W}_i$. In this work, we initiate the study of provably learning a multi-head attention layer from random examples and give the first nontrivial upper and lower bounds for this problem: - Provided $\{\mathbf{W}_i, \mathbfΘ_i\}$ satisfy certain non-degeneracy conditions, we give a $(dk)^{O(m^3)}$-time algorithm that learns $F$ to small error given random labeled examples drawn uniformly from $\{\pm 1\}^{k\times d}$. - We prove computational lower bounds showing that in the worst case, exponential dependence on $m$ is unavoidable. We focus on Boolean $\mathbf{X}$ to mimic the discrete nature of tokens in large language models, though our techniques naturally extend to standard continuous settings, e.g. Gaussian. Our algorithm, which is centered around using examples to sculpt a convex body containing the unknown parameters, is a significant departure from existing provable algorithms for learning feedforward networks, which predominantly exploit algebraic and rotation invariance properties of the Gaussian distribution. In contrast, our analysis is more flexible as it primarily relies on various upper and lower tail bounds for the input distribution and "slices" thereof.
The Use of a Large Language Model for Cyberbullying Detection
Venue: arXiv cs.CL 2024
Year: 2024

The dominance of social media has added to the channels of bullying for perpetrators. Unfortunately, cyberbullying (CB) is the most prevalent phenomenon in todays cyber world, and is a severe threat to the mental and physical health of citizens. This opens the need to develop a robust system to prevent bullying content from online forums, blogs, and social media platforms to manage the impact in our society. Several machine learning (ML) algorithms have been proposed for this purpose. However, their performances are not consistent due to high class imbalance and generalisation issues. In recent years, large language models (LLMs) like BERT and RoBERTa have achieved state-of-the-art (SOTA) results in several natural language processing (NLP) tasks. Unfortunately, the LLMs have not been applied extensively for CB detection. In our paper, we explored the use of these models for cyberbullying (CB) detection. We have prepared a new dataset (D2) from existing studies (Formspring and Twitter). Our experimental results for dataset D1 and D2 showed that RoBERTa outperformed other models.
Read to Play (R2-Play): Decision Transformer with Multimodal Game Instruction
Venue: arXiv cs.AI 2024
Year: 2024

Developing a generalist agent is a longstanding objective in artificial intelligence. Previous efforts utilizing extensive offline datasets from various tasks demonstrate remarkable performance in multitasking scenarios within Reinforcement Learning. However, these works encounter challenges in extending their capabilities to new tasks. Recent approaches integrate textual guidance or visual trajectory into decision networks to provide task-specific contextual cues, representing a promising direction. However, it is observed that relying solely on textual guidance or visual trajectory is insufficient for accurately conveying the contextual information of tasks. This paper explores enhanced forms of task guidance for agents, enabling them to comprehend gameplay instructions, thereby facilitating a "read-to-play" capability. Drawing inspiration from the success of multimodal instruction tuning in visual tasks, we treat the visual-based RL task as a long-horizon vision task and construct a set of multimodal game instructions to incorporate instruction tuning into a decision transformer. Experimental results demonstrate that incorporating multimodal game instructions significantly enhances the decision transformer's multitasking and generalization capabilities.
Harnessing the Plug-and-Play Controller by Prompting
Venue: arXiv cs.CL 2024
Year: 2024

Controllable text generation is a growing field within natural language generation (NLG) that focuses on producing text that meets specific constraints in real-world applications. Previous approaches, such as plug-and-play controllers (PPCs), aimed to steer the properties of generated text in a flexible manner. However, these methods often compromised the integrity of the language model's decoding process, resulting in less smooth text generation. Alternatively, other techniques utilized multiple attribute prompts to align the generated text with desired attributes, but this approach required prompt design for each attribute and was dependent on the size of the language model. This paper introduces a novel method for flexible attribute control in text generation using pre-trained language models (PLMs). The proposed approach aims to enhance the fluency of generated text by guiding the generation process with PPCs. The key idea is to dynamically adjust the distribution of generated text by modifying prompts, effectively constraining the output space of the language model and influencing the desired attribute. To enable smooth cooperation between the PLM and the PPC, our work innovatively proposes a new model fine-tuning method: Reinforcement Learning with Dynamic Adjust Feedback (RLDAF).This fine-tuning process adapts a small subset of the language model's parameters based on the generating actions taken during the PPC control process. The resulting harmonious collaboration between the PLM and PPC leads to improved smoothness in text generation during inference. Extensive experiments were conducted on the SST2 dataset, and the proposed method outperformed previous approaches in various evaluation metrics, including text fluency and attribute consistency.
Instance by Instance: An Iterative Framework for Multi-instance 3D Registration
Venue: arXiv cs.CV 2024
Year: 2024

Multi-instance registration is a challenging problem in computer vision and robotics, where multiple instances of an object need to be registered in a standard coordinate system. In this work, we propose the first iterative framework called instance-by-instance (IBI) for multi-instance 3D registration (MI-3DReg). It successively registers all instances in a given scenario, starting from the easiest and progressing to more challenging ones. Throughout the iterative process, outliers are eliminated continuously, leading to an increasing inlier rate for the remaining and more challenging instances. Under the IBI framework, we further propose a sparse-to-dense-correspondence-based multi-instance registration method (IBI-S2DC) to achieve robust MI-3DReg. Experiments on the synthetic and real datasets have demonstrated the effectiveness of IBI and suggested the new state-of-the-art performance of IBI-S2DC, e.g., our MHF1 is 12.02%/12.35% higher than the existing state-of-the-art method ECC on the synthetic/real datasets.
LIPSTICK: Corruptibility-Aware and Explainable Graph Neural Network-based Oracle-Less Attack on Logic Locking
Venue: arXiv cs.CR 2024
Year: 2024

In a zero-trust fabless paradigm, designers are increasingly concerned about hardware-based attacks on the semiconductor supply chain. Logic locking is a design-for-trust method that adds extra key-controlled gates in the circuits to prevent hardware intellectual property theft and overproduction. While attackers have traditionally relied on an oracle to attack logic-locked circuits, machine learning attacks have shown the ability to retrieve the secret key even without access to an oracle. In this paper, we first examine the limitations of state-of-the-art machine learning attacks and argue that the use of key hamming distance as the sole model-guiding structural metric is not always useful. Then, we develop, train, and test a corruptibility-aware graph neural network-based oracle-less attack on logic locking that takes into consideration both the structure and the behavior of the circuits. Our model is explainable in the sense that we analyze what the machine learning model has interpreted in the training process and how it can perform a successful attack. Chip designers may find this information beneficial in securing their designs while avoiding incremental fixes.
Multi-View Symbolic Regression
Venue: arXiv cs.LG 2024
Year: 2024

Symbolic regression (SR) searches for analytical expressions representing the relationship between a set of explanatory and response variables. Current SR methods assume a single dataset extracted from a single experiment. Nevertheless, frequently, the researcher is confronted with multiple sets of results obtained from experiments conducted with different setups. Traditional SR methods may fail to find the underlying expression since the parameters of each experiment can be different. In this work we present Multi-View Symbolic Regression (MvSR), which takes into account multiple datasets simultaneously, mimicking experimental environments, and outputs a general parametric solution. This approach fits the evaluated expression to each independent dataset and returns a parametric family of functions f(x; theta) simultaneously capable of accurately fitting all datasets. We demonstrate the effectiveness of MvSR using data generated from known expressions, as well as real-world data from astronomy, chemistry and economy, for which an a priori analytical expression is not available. Results show that MvSR obtains the correct expression more frequently and is robust to hyperparameters change. In real-world data, it is able to grasp the group behavior, recovering known expressions from the literature as well as promising alternatives, thus enabling the use of SR to a large range of experimental scenarios.
Enhance DNN Adversarial Robustness and Efficiency via Injecting Noise to Non-Essential Neurons
Venue: arXiv cs.LG 2024
Year: 2024

Deep Neural Networks (DNNs) have revolutionized a wide range of industries, from healthcare and finance to automotive, by offering unparalleled capabilities in data analysis and decision-making. Despite their transforming impact, DNNs face two critical challenges: the vulnerability to adversarial attacks and the increasing computational costs associated with more complex and larger models. In this paper, we introduce an effective method designed to simultaneously enhance adversarial robustness and execution efficiency. Unlike prior studies that enhance robustness via uniformly injecting noise, we introduce a non-uniform noise injection algorithm, strategically applied at each DNN layer to disrupt adversarial perturbations introduced in attacks. By employing approximation techniques, our approach identifies and protects essential neurons while strategically introducing noise into non-essential neurons. Our experimental results demonstrate that our method successfully enhances both robustness and efficiency across several attack scenarios, model architectures, and datasets.
3D printer-controlled syringe pumps for dual, active, regulable and simultaneous dispensing of reagents. Manufacturing of immunochromatographic test strips
Venue: arXiv cs.RO 2024
Year: 2024

Lateral flow immunoassays (LFIA) are widely used worldwide for the detection of different analytes because they combine multiple advantages such as low production cost, simplicity, and portability, which allows biomarkers detection without requiring infrastructure or highly trained personnel. Here we propose to provide solutions to the manufacturing process of LFIA at laboratory-scale, particularly to the controlled and active dispensing of the reagents in the form the Test Lines (TL) and the Control Lines (CL). To accomplish this task, we adapted a 3D printer to also control Syringe Pumps (SP), since the proposed adaptation of a 3D printer is easy, free and many laboratories already have it in their infrastructure. In turn, the standard function of the 3D printer can be easily restored by disconnecting the SPs and reconnecting the extruder. Additionally, the unified control of the 3D printer enables dual, active, regulable and simultaneous dispensing, four features that are typically found only in certain high-cost commercial equipment. With the proposed setup, the challenge of dispensing simultaneously at least 2 lines (CL and TL) with SPs controlled by a 3D printer was addressed, including regulation in the width of dispensed lines within experimental limits. Also, the construction of a LFIA for the detection of leptospirosis is shown as a practical example of automatized reagent dispensing.
Bidirectional Autoregressive Diffusion Model for Dance Generation
Venue: arXiv cs.SD 2024
Year: 2024

Dance serves as a powerful medium for expressing human emotions, but the lifelike generation of dance is still a considerable challenge. Recently, diffusion models have showcased remarkable generative abilities across various domains. They hold promise for human motion generation due to their adaptable many-to-many nature. Nonetheless, current diffusion-based motion generation models often create entire motion sequences directly and unidirectionally, lacking focus on the motion with local and bidirectional enhancement. When choreographing high-quality dance movements, people need to take into account not only the musical context but also the nearby music-aligned dance motions. To authentically capture human behavior, we propose a Bidirectional Autoregressive Diffusion Model (BADM) for music-to-dance generation, where a bidirectional encoder is built to enforce that the generated dance is harmonious in both the forward and backward directions. To make the generated dance motion smoother, a local information decoder is built for local motion enhancement. The proposed framework is able to generate new motions based on the input conditions and nearby motions, which foresees individual motion slices iteratively and consolidates all predictions. To further refine the synchronicity between the generated dance and the beat, the beat information is incorporated as an input to generate better music-aligned dance movements. Experimental results demonstrate that the proposed model achieves state-of-the-art performance compared to existing unidirectional approaches on the prominent benchmark for music-to-dance generation.
Building Retrieval Systems for the ClueWeb22-B Corpus
Venue: arXiv cs.IR 2024
Year: 2024

The ClueWeb22 dataset containing nearly 10 billion documents was released in 2022 to support academic and industry research. The goal of this project was to build retrieval baselines for the English section of the "super head" part (category B) of this dataset. These baselines can then be used by the research community to compare their systems and also to generate data to train/evaluate new retrieval and ranking algorithms. The report covers sparse and dense first stage retrievals as well as neural rerankers that were implemented for this dataset. These systems are available as a service on a Carnegie Mellon University cluster.
Merkle Trees in Blockchain: A Study of Collision Probability and Security Implications
Venue: arXiv cs.CR 2024
Year: 2024

In the rapidly evolving landscape of blockchain technology, ensuring the integrity and security of data is paramount. This study delves into the security aspects of Merkle Trees, a fundamental component in blockchain architectures, such as Ethereum. We critically examine the susceptibility of Merkle Trees to hash collisions, a potential vulnerability that poses significant risks to data security within blockchain systems. Despite their widespread application, the collision resistance of Merkle Trees and their robustness against preimage attacks have not been thoroughly investigated, leading to a notable gap in the comprehensive understanding of blockchain security mechanisms. Our research endeavors to bridge this gap through a meticulous blend of theoretical analysis and empirical validation. We scrutinize the probability of root collisions in Merkle Trees, considering various factors such as hash length and path length within the tree. Our findings reveal a direct correlation between the increase in path length and the heightened probability of root collisions, thereby underscoring potential security vulnerabilities. Conversely, we observe that an increase in hash length significantly reduces the likelihood of collisions, highlighting its critical role in fortifying security. The insights garnered from our research offer valuable guidance for blockchain developers and researchers, aiming to bolster the security and operational efficacy of blockchain-based systems.
SKOOTR: A SKating, Omni-Oriented, Tripedal Robot
Venue: arXiv cs.RO 2024
Year: 2024

In both animals and robots, locomotion capabilities are determined by the physical structure of the system. The majority of legged animals and robots are bilaterally symmetric, which facilitates locomotion with consistent headings and obstacle traversal, but leads to constraints in their turning ability. On the other hand, radially symmetric animals have demonstrated rapid turning abilities enabled by their omni-directional body plans. Radially symmetric tripedal robots are able to turn instantaneously, but are commonly constrained by needing to change direction with every step, resulting in inefficient and less stable locomotion. We address these challenges by introducing a novel design for a tripedal robot that has both frictional and rolling contacts. Additionally, a freely rotating central sphere provides an added contact point so the robot can retain a stable tripod base of support while lifting and pushing with any one of its legs. The SKating, Omni-Oriented, Tripedal Robot (SKOOTR) is more versatile and stable than other existing tripedal robots. It is capable of multiple forward gaits, multiple turning maneuvers, obstacle traversal, and stair climbing. SKOOTR has been designed to facilitate customization for diverse applications: it is fully open-source, is constructed with 3D printed or off-the-shelf parts, and costs approximately $500 USD to build.
A Repeated Auction Model for Load-Aware Dynamic Resource Allocation in Multi-Access Edge Computing
Venue: arXiv cs.GT 2024
Year: 2024

Multi-access edge computing (MEC) is one of the enabling technologies for high-performance computing at the edge of the 6 G networks, supporting high data rates and ultra-low service latency. Although MEC is a remedy to meet the growing demand for computation-intensive applications, the scarcity of resources at the MEC servers degrades its performance. Hence, effective resource management is essential; nevertheless, state-of-the-art research lacks efficient economic models to support the exponential growth of the MEC-enabled applications market. We focus on designing a MEC offloading service market based on a repeated auction model with multiple resource sellers (e.g., network operators and service providers) that compete to sell their computing resources to the offloading users. We design a computationally-efficient modified Generalized Second Price (GSP)-based algorithm that decides on pricing and resource allocation by considering the dynamic offloading requests arrival and the servers' computational workloads. Besides, we propose adaptive best-response bidding strategies for the resource sellers, satisfying the symmetric Nash equilibrium (SNE) and individual rationality properties. Finally, via intensive numerical results, we show the effectiveness of our proposed resource allocation mechanism.
Studying Vulnerable Code Entities in R
Venue: arXiv cs.SE 2024
Year: 2024

Pre-trained Code Language Models (Code-PLMs) have shown many advancements and achieved state-of-the-art results for many software engineering tasks in the past few years. These models are mainly targeted for popular programming languages such as Java and Python, leaving out many other ones like R. Though R has a wide community of developers and users, there is little known about the applicability of Code-PLMs for R. In this preliminary study, we aim to investigate the vulnerability of Code-PLMs for code entities in R. For this purpose, we use an R dataset of code and comment pairs and then apply CodeAttack, a black-box attack model that uses the structure of code to generate adversarial code samples. We investigate how the model can attack different entities in R. This is the first step towards understanding the importance of R token types, compared to popular programming languages (e.g., Java). We limit our study to code summarization. Our results show that the most vulnerable code entity is the identifier, followed by some syntax tokens specific to R. The results can shed light on the importance of token types and help in developing models for code summarization and method name prediction for the R language.