OpenRaiser/Intern-Atlas huggingface dataset:
Synthesis from LTL with Reward Optimization in Sampled Oblivious Environments
Venue: arXiv cs.FL 2024
Year: 2024

This paper addresses the synthesis of reactive systems that enforce hard constraints while optimizing for quality-based soft constraints. We build on recent advancements in combining reactive synthesis with example-based guidance to handle both types of constraints in stochastic, oblivious environments accessible only through sampling. Our approach constructs examples that satisfy LTL-based hard constraints while maximizing expected rewards-representing the soft constraints-on samples drawn from the environment. We formally define this synthesis problem, prove it to be NP-complete, and propose an SMT-based solution, demonstrating its effectiveness with a case study.
Energy-Cautious Designation of Kinematic Parameters for a Sustainable Parallel-Serial Heavy-Duty Manipulator Driven by Electromechanical Linear Actuator
Venue: arXiv cs.RO 2024
Year: 2024

Electrification, a key strategy in combating climate change, is transforming industries, and off-highway machines (OHM) will be next to transition from combustion engines and hydraulic actuation to sustainable fully electrified machines. Electromechanical linear actuators (EMLAs) offer superior efficiency, safety, and reduced maintenance, and they unlock vast potential for high-performance autonomous operations. However, a key challenge lies in optimizing the kinematic parameters of OHMs' on-board manipulators for EMLA integration to exploit the full capabilities of actuation systems and maximize their performance. This work addresses this challenge by delving into the structural optimization of a prevalent closed kinematic chain configuration commonly employed in OHM manipulators. Our approach aims to retain the manipulator's existing capabilities while reducing its energy expenditure, paving the way for a greener future in industrial automation, one in which sustainable and high-performing robotized OHMs can evolve. The feasibility of our methodology is validated through simulation results obtained on a commercially available parallel-serial heavy-duty manipulator mounted on a battery electric vehicle. The results demonstrate the efficacy of our approach in modifying kinematic parameters to facilitate the replacement of conventional hydraulic actuators with EMLAs, all while minimizing the overall energy consumption of the system.
From gymnastics to virtual nonholonomic constraints: energy injection, dissipation, and regulation for the acrobot
Venue: arXiv cs.RO 2024
Year: 2024

In this article we study virtual nonholonomic constraints, which are relations between the generalized coordinates and momenta of a mechanical system that can be enforced via feedback control. We design a constraint which emulates gymnastics giant motion in an acrobot, and prove that this constraint can inject or dissipate energy based on the sign of a design parameter. The proposed constraint is tested both in simulation and experimentally on a real-world acrobot, demonstrating highly effective energy regulation properties and robustness to a variety of disturbances.
Mahi-Mahi: Low-Latency Asynchronous BFT DAG-Based Consensus
Venue: arXiv cs.DC 2024
Year: 2024

We present Mahi-Mahi, the first asynchronous BFT consensus protocol that achieves sub-second latency in the WAN while processing over 100,000 transactions per second. We accomplish this remarkable performance by building Mahi-Mahi on an uncertified structured Directed Acyclic Graph (DAG). By forgoing explicit certification, we significantly reduce the number of messages required to commit and minimize CPU overhead associated with certificate verification. Mahi-Mahi introduces a novel commit rule that allows committing multiple blocks in each DAG round, while ensuring liveness in the presence of an asynchronous adversary. Mahi-Mahi can be parametrized to either attempt to commit within 5 message delays, maximizing the probability of commitment under a continuously active asynchronous adversary, or within 4 message delays, which reduces latency under a more moderate and realistic asynchronous adversary. We demonstrate the safety and liveness of Mahi-Mahi in a Byzantine context. Subsequently, we evaluate Mahi-Mahi in a geo-replicated setting and compare its performance against state-of-the-art asynchronous consensus protocols, showcasing Mahi-Mahi's significantly lower latency.
On the token distance modeling ability of higher RoPE attention dimension
Venue: arXiv cs.CL 2024
Year: 2024

Length extrapolation algorithms based on Rotary position embedding (RoPE) have shown promising results in extending the context length of language models. However, understanding how position embedding can capture longer-range contextual information remains elusive. Based on the intuition that different dimensions correspond to different frequency of changes in RoPE encoding, we conducted a dimension-level analysis to investigate the correlation between a hidden dimension of an attention head and its contribution to capturing long-distance dependencies. Using our correlation metric, we identified a particular type of attention heads, which we named Positional Heads, from various length-extrapolated models. These heads exhibit a strong focus on long-range information interaction and play a pivotal role in long input processing, as evidence by our ablation. We further demonstrate the correlation between the efficiency of length extrapolation and the extension of the high-dimensional attention allocation of these heads. The identification of Positional Heads provides insights for future research in long-text comprehension.
A generic Branch-and-Cut algorithm for bi-objective binary linear programs
Venue: arXiv cs.DM 2024
Year: 2024

This paper presents the first generic bi-objective binary linear branch-and-cut algorithm. Studying the impact of valid inequalities in solution and objective spaces, two cutting frameworks are proposed. The multi-point separation problem is introduced together with a cutting algorithm to efficiently generate valid inequalities violating multiple points simultaneously. The other main idea is to invoke state-of-the-art integer linear programming solver's internal advanced techniques such as cut separators. Aggregation techniques are proposed to use these frameworks with a trade-off among efficient cut separations, tight lower and upper bound sets and advanced branching strategies. Experiments on various types of instances in the literature exhibit the promising efficiency of the algorithm that solves instances with up to 2800 binary variables in less than one hour of CPU time. Our algorithms are easy to extend for more than two objectives and integer variables.
Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Large language models (LLMs) have demonstrated remarkable performance in the legal domain, with GPT-4 even passing the Uniform Bar Exam in the U.S. However their efficacy remains limited for non-standardized tasks and tasks in languages other than English. This underscores the need for careful evaluation of LLMs within each legal system before application. Here, we introduce KBL, a benchmark for assessing the Korean legal language understanding of LLMs, consisting of (1) 7 legal knowledge tasks (510 examples), (2) 4 legal reasoning tasks (288 examples), and (3) the Korean bar exam (4 domains, 53 tasks, 2,510 examples). First two datasets were developed in close collaboration with lawyers to evaluate LLMs in practical scenarios in a certified manner. Furthermore, considering legal practitioners' frequent use of extensive legal documents for research, we assess LLMs in both a closed book setting, where they rely solely on internal knowledge, and a retrieval-augmented generation (RAG) setting, using a corpus of Korean statutes and precedents. The results indicate substantial room and opportunities for improvement.
Cross-chain Sharing of Personal Health Records: Heterogeneous and Interoperable Blockchains
Venue: arXiv cs.CR 2024
Year: 2024

With the widespread adoption of medical informatics, a wealth of valuable personal health records (PHR) has been generated. Concurrently, blockchain technology has enhanced the security of medical institutions. However, these institutions often function as isolated data silos, limiting the potential value of PHRs. As the demand for data sharing between hospitals on different blockchains grows, addressing the challenge of cross-chain data sharing becomes crucial. When sharing PHRs across blockchains, the limited storage and computational capabilities of medical Internet of Things (IoT) devices complicate the storage of large volumes of PHRs and the handling of complex calculations. Additionally, varying blockchain cryptosystems and the risk of internal attacks further complicate the cross-chain sharing of PHRs. This paper proposes a scheme for sharing PHRs across heterogeneous and interoperable blockchains. Medical IoT devices can encrypt and store real-time PHRs in an InterPlanetary File System, requiring only simple operations for data sharing. An enhanced proxy re-encryption(PRE) algorithm addresses the differences in blockchain cryptosystems. Multi-dimensional analysis demonstrates that this scheme offers robust security and excellent performance.
Causal machine learning for predicting treatment outcomes
Venue: arXiv cs.LG 2024
Year: 2024

Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating individualized treatment effects, so that clinical decision-making can be personalized to individual patient profiles. Causal ML can be used in combination with both clinical trial data and real-world data, such as clinical registries and electronic health records, but caution is needed to avoid biased or incorrect predictions. In this Perspective, we discuss the benefits of causal ML (relative to traditional statistical or ML approaches) and outline the key components and steps. Finally, we provide recommendations for the reliable use of causal ML and effective translation into the clinic.
DCNet: A Data-Driven Framework for DVL Calibration
Venue: arXiv cs.RO 2024
Year: 2024

Autonomous underwater vehicles (AUVs) are underwater robotic platforms used in a variety of applications. An AUV's navigation solution relies heavily on the fusion of inertial sensors and Doppler velocity logs (DVL), where the latter delivers accurate velocity updates. To ensure accurate navigation, a DVL calibration is undertaken before the mission begins to estimate its error terms. During calibration, the AUV follows a complex trajectory and employs nonlinear estimation filters to estimate error terms. In this paper, we introduce DCNet, a data-driven framework that utilizes a two-dimensional convolution kernel in an innovative way. Using DCNet and our proposed DVL error model, we offer a rapid calibration procedure. This can be applied to a trajectory with a nearly constant velocity. To train and test our proposed approach a dataset of 276 minutes long with real DVL recorded measurements was used. We demonstrated an average improvement of 70% in accuracy and 80% improvement in calibration time, compared to the baseline approach, with a low-performance DVL. As a result of those improvements, an AUV employing a low-cost DVL, can achieve higher accuracy, shorter calibration time, and apply a simple nearly constant velocity calibration trajectory. Our results also open up new applications for marine robotics utilizing low-cost, high-accurate DVLs.
Uncertainty-Aware Optimal Treatment Selection for Clinical Time Series
Venue: arXiv cs.LG 2024
Year: 2024

In personalized medicine, the ability to predict and optimize treatment outcomes across various time frames is essential. Additionally, the ability to select cost-effective treatments within specific budget constraints is critical. Despite recent advancements in estimating counterfactual trajectories, a direct link to optimal treatment selection based on these estimates is missing. This paper introduces a novel method integrating counterfactual estimation techniques and uncertainty quantification to recommend personalized treatment plans adhering to predefined cost constraints. Our approach is distinctive in its handling of continuous treatment variables and its incorporation of uncertainty quantification to improve prediction reliability. We validate our method using two simulated datasets, one focused on the cardiovascular system and the other on COVID-19. Our findings indicate that our method has robust performance across different counterfactual estimation baselines, showing that introducing uncertainty quantification in these settings helps the current baselines in finding more reliable and accurate treatment selection. The robustness of our method across various settings highlights its potential for broad applicability in personalized healthcare solutions.
One-shot Generative Domain Adaptation in 3D GANs
Venue: arXiv cs.CV 2024
Year: 2024

3D-aware image generation necessitates extensive training data to ensure stable training and mitigate the risk of overfitting. This paper first considers a novel task known as One-shot 3D Generative Domain Adaptation (GDA), aimed at transferring a pre-trained 3D generator from one domain to a new one, relying solely on a single reference image. One-shot 3D GDA is characterized by the pursuit of specific attributes, namely, high fidelity, large diversity, cross-domain consistency, and multi-view consistency. Within this paper, we introduce 3D-Adapter, the first one-shot 3D GDA method, for diverse and faithful generation. Our approach begins by judiciously selecting a restricted weight set for fine-tuning, and subsequently leverages four advanced loss functions to facilitate adaptation. An efficient progressive fine-tuning strategy is also implemented to enhance the adaptation process. The synergy of these three technological components empowers 3D-Adapter to achieve remarkable performance, substantiated both quantitatively and qualitatively, across all desired properties of 3D GDA. Furthermore, 3D-Adapter seamlessly extends its capabilities to zero-shot scenarios, and preserves the potential for crucial tasks such as interpolation, reconstruction, and editing within the latent space of the pre-trained generator. Code will be available at https://github.com/iceli1007/3D-Adapter.
A physics-guided neural network for flooding area detection using SAR imagery and local river gauge observations
Venue: arXiv cs.LG 2024
Year: 2024

The flooding extent area in a river valley is related to river gauge observations. The higher the water elevation, the larger the flooding area. Due to synthetic aperture radar\textquoteright s (SAR) capabilities to penetrate through clouds, radar images have been commonly used to estimate flooding extent area with various methods, from simple thresholding to deep learning models. In this study, we propose a physics-guided neural network for flooding area detection. Our approach takes as input data the Sentinel 1 time-series images and the water elevations in the river assigned to each image. We apply the Pearson correlation coefficient between the predicted sum of water extent areas and the local water level observations of river water elevations as the loss function. The effectiveness of our method is evaluated in five different study areas by comparing the predicted water maps with reference water maps obtained from digital terrain models and optical satellite images. The highest Intersection over Union (IoU) score achieved by our models was 0.89 for the water class and 0.96 for the non-water class. Additionally, we compared the results with other unsupervised methods. The proposed neural network provided a higher IoU than the other methods, especially for SAR images registered during low water elevation in the river.
Bank Loan Prediction Using Machine Learning Techniques
Venue: arXiv cs.LG 2024
Year: 2024

Banks are important for the development of economies in any financial ecosystem through consumer and business loans. Lending, however, presents risks; thus, banks have to determine the applicant's financial position to reduce the probabilities of default. A number of banks have currently, therefore, adopted data analytics and state-of-the-art technology to arrive at better decisions in the process. The probability of payback is prescribed by a predictive modeling technique in which machine learning algorithms are applied. In this research project, we will apply several machine learning methods to further improve the accuracy and efficiency of loan approval processes. Our work focuses on the prediction of bank loan approval; we have worked on a dataset of 148,670 instances and 37 attributes using machine learning methods. The target property segregates the loan applications into "Approved" and "Denied" groups. various machine learning techniques have been used, namely, Decision Tree Categorization, AdaBoosting, Random Forest Classifier, SVM, and GaussianNB. Following that, the models were trained and evaluated. Among these, the best-performing algorithm was AdaBoosting, which achieved an incredible accuracy of 99.99%. The results therefore show how ensemble learning works effectively to improve the prediction skills of loan approval decisions. The presented work points to the possibility of achieving extremely accurate and efficient loan prediction models that provide useful insights for applying machine learning to financial domains.
Exploiting Memory-aware Q-distribution Prediction for Nuclear Fusion via Modern Hopfield Network
Venue: arXiv cs.CV 2024
Year: 2024

This study addresses the critical challenge of predicting the Q-distribution in long-term stable nuclear fusion task, a key component for advancing clean energy solutions. We introduce an innovative deep learning framework that employs Modern Hopfield Networks to incorporate associative memory from historical shots. Utilizing a newly compiled dataset, we demonstrate the effectiveness of our approach in enhancing Q-distribution prediction. The proposed method represents a significant advancement by leveraging historical memory information for the first time in this context, showcasing improved prediction accuracy and contributing to the optimization of nuclear fusion research.
NoVo: Norm Voting off Hallucinations with Attention Heads in Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Hallucinations in Large Language Models (LLMs) remain a major obstacle, particularly in high-stakes applications where factual accuracy is critical. While representation editing and reading methods have made strides in reducing hallucinations, their heavy reliance on specialised tools and training on in-domain samples, makes them difficult to scale and prone to overfitting. This limits their accuracy gains and generalizability to diverse datasets. This paper presents a lightweight method, Norm Voting (NoVo), which harnesses the untapped potential of attention head norms to dramatically enhance factual accuracy in zero-shot multiple-choice questions (MCQs). NoVo begins by automatically selecting truth-correlated head norms with an efficient, inference-only algorithm using only 30 random samples, allowing NoVo to effortlessly scale to diverse datasets. Afterwards, selected head norms are employed in a simple voting algorithm, which yields significant gains in prediction accuracy. On TruthfulQA MC1, NoVo surpasses the current state-of-the-art and all previous methods by an astounding margin -- at least 19 accuracy points. NoVo demonstrates exceptional generalization to 20 diverse datasets, with significant gains in over 90\% of them, far exceeding all current representation editing and reading methods. NoVo also reveals promising gains to finetuning strategies and building textual adversarial defence. NoVo's effectiveness with head norms opens new frontiers in LLM interpretability, robustness and reliability.
Learning Representations of Instruments for Partial Identification of Treatment Effects
Venue: arXiv cs.LG 2024
Year: 2024

Reliable estimation of treatment effects from observational data is important in many disciplines such as medicine. However, estimation is challenging when unconfoundedness as a standard assumption in the causal inference literature is violated. In this work, we leverage arbitrary (potentially high-dimensional) instruments to estimate bounds on the conditional average treatment effect (CATE). Our contributions are three-fold: (1) We propose a novel approach for partial identification through a mapping of instruments to a discrete representation space so that we yield valid bounds on the CATE. This is crucial for reliable decision-making in real-world applications. (2) We derive a two-step procedure that learns tight bounds using a tailored neural partitioning of the latent instrument space. As a result, we avoid instability issues due to numerical approximations or adversarial training. Furthermore, our procedure aims to reduce the estimation variance in finite-sample settings to yield more reliable estimates. (3) We show theoretically that our procedure obtains valid bounds while reducing estimation variance. We further perform extensive experiments to demonstrate the effectiveness across various settings. Overall, our procedure offers a novel path for practitioners to make use of potentially high-dimensional instruments (e.g., as in Mendelian randomization).
Block coupling and rapidly mixing k-heights
Venue: arXiv cs.DM 2024
Year: 2024

A $k$-height on a graph $G=(V, E)$ is an assignment $V\to\{0, \ldots, k\}$ such that the value on ajacent vertices differs by at most $1$. We study the Markov chain on $k$-heights that in each step selects a vertex at random, and, if admissible, increases or decreases the value at this vertex by one. In the cases of $2$-heights and $3$-heights we show that this Markov chain is rapidly mixing on certain families of grid-like graphs and on planar cubic $3$-connected graphs. The result is based on a novel technique called block coupling, which is derived from the well-established monotone coupling approach. This technique may also be effective when analyzing other Markov chains that operate on configurations of spin systems that form a distributive lattice. It is therefore of independent interest.
Design and Control of an Omnidirectional Aerial Robot with a Miniaturized Haptic Joystick for Physical Interaction
Venue: arXiv cs.RO 2024
Year: 2024

Fully actuated aerial robot proved their superiority for Aerial Physical Interaction (APhI) over the past years. This work proposes a minimal setup for aerial telemanipulation, enhancing accessibility of these technologies. The design and the control of a 6-DoF joystick with 4-DoF haptic feedback is detailed. It is the first haptic device with standard Remote Controller (RC) form factor for APhI. By miniaturizing haptic device, it enhances RC with the sense of touch, increasing physical awareness. The goal is to give operators an extra sense, other than vision and sound, to help to perform safe APhI. To the best of the authors knowledge, this is the first teleoperation system able to decouple each single axis input command. On the omnidirectional quadrotor, by reducing the number of components with a new design, we aim a simplified maintenance, and improved force and thrust to weight ratio. Open-sourced physic based simulation and successful preliminary flight tests highlighted the tool as promising for future APhI applications.