OpenRaiser/Intern-Atlas huggingface dataset:
Benchmarking and Analyzing In-context Learning, Fine-tuning and Supervised Learning for Biomedical Knowledge Curation: a focused study on chemical entities of biological interest
Venue: arXiv cs.LG 2023
Year: 2023

Automated knowledge curation for biomedical ontologies is key to ensure that they remain comprehensive, high-quality and up-to-date. In the era of foundational language models, this study compares and analyzes three NLP paradigms for curation tasks: in-context learning (ICL), fine-tuning (FT), and supervised learning (ML). Using the Chemical Entities of Biological Interest (ChEBI) database as a model ontology, three curation tasks were devised. For ICL, three prompting strategies were employed with GPT-4, GPT-3.5, BioGPT. PubmedBERT was chosen for the FT paradigm. For ML, six embedding models were utilized for training Random Forest and Long-Short Term Memory models. Five setups were designed to assess ML and FT model performance across different data availability scenarios.Datasets for curation tasks included: task 1 (620,386), task 2 (611,430), and task 3 (617,381), maintaining a 50:50 positive versus negative ratio. For ICL models, GPT-4 achieved best accuracy scores of 0.916, 0.766 and 0.874 for tasks 1-3 respectively. In a direct comparison, ML (trained on ~260,000 triples) outperformed ICL in accuracy across all tasks. (accuracy differences: +.11, +.22 and +.17). Fine-tuned PubmedBERT performed similarly to leading ML models in tasks 1 & 2 (F1 differences: -.014 and +.002), but worse in task 3 (-.048). Simulations revealed performance declines in both ML and FT models with smaller and higher imbalanced training data. where ICL (particularly GPT-4) excelled in tasks 1 & 3. GPT-4 excelled in tasks 1 and 3 with less than 6,000 triples, surpassing ML/FT. ICL underperformed ML/FT in task 2.ICL-augmented foundation models can be good assistants for knowledge curation with correct prompting, however, not making ML and FT paradigms obsolete. The latter two require task-specific data to beat ICL. In such cases, ML relies on small pretrained embeddings, minimizing computational demands.
Defining and executing temporal constraints for evaluating engineering artifact compliance
Venue: arXiv cs.SE 2023
Year: 2023

Engineering processes for safety-critical systems describe the steps and sequence that guide engineers from refining user requirements into executable code, as well as producing the artifacts, traces, and evidence that the resulting system is of high quality. Process compliance focuses on ensuring that the actual engineering work is followed as closely as possible to the described engineering processes. To this end, temporal constraints describe the ideal sequence of steps. Checking these process constraints, however, is still a daunting task that requires a lot of manual work and delivers feedback to engineers only late in the process. In this paper, we present an automated constraint checking approach that can incrementally check temporal constraints across inter-related engineering artifacts upon every artifact change thereby enabling timely feedback to engineers on process deviations. Temporal constraints are expressed in the Object Constraint Language (OCL) extended with operators from Linear Temporal Logic (LTL). We demonstrate the ability of our approach to support a wide range of higher level temporal patterns. We further show that for constraints in an industry-derived use case, the average evaluation time for a single constraint takes around 0.2 milliseconds.
Generative agents in the streets: Exploring the use of Large Language Models (LLMs) in collecting urban perceptions
Venue: arXiv cs.CY 2023
Year: 2023

Evaluating the surroundings to gain understanding, frame perspectives, and anticipate behavioral reactions is an inherent human trait. However, these continuous encounters are diverse and complex, posing challenges to their study and experimentation. Researchers have been able to isolate environmental features and study their effect on human perception and behavior. However, the research attempts to replicate and study human behaviors with proxies, such as by integrating virtual mediums and interviews, have been inconsistent. Large language models (LLMs) have recently been unveiled as capable of contextual understanding and semantic reasoning. These models have been trained on large amounts of text and have evolved to mimic believable human behavior. This study explores the current advancements in Generative agents powered by LLMs with the help of perceptual experiments. The experiment employs Generative agents to interact with the urban environments using street view images to plan their journey toward specific goals. The agents are given virtual personalities, which make them distinguishable. They are also provided a memory database to store their thoughts and essential visual information and retrieve it when needed to plan their movement. Since LLMs do not possess embodiment, nor have access to the visual realm, and lack a sense of motion or direction, we designed movement and visual modules that help agents gain an overall understanding of surroundings. The agents are further employed to rate the surroundings they encounter based on their perceived sense of safety and liveliness. As these agents store details in their memory, we query the findings to get details regarding their thought processes. Overall, this study experiments with current AI developments and their potential in simulated human behavior in urban environments.
Gappy local conformal auto-encoders for heterogeneous data fusion: in praise of rigidity
Venue: arXiv cs.LG 2023
Year: 2023

Fusing measurements from multiple, heterogeneous, partial sources, observing a common object or process, poses challenges due to the increasing availability of numbers and types of sensors. In this work we propose, implement and validate an end-to-end computational pipeline in the form of a multiple-auto-encoder neural network architecture for this task. The inputs to the pipeline are several sets of partial observations, and the result is a globally consistent latent space, harmonizing (rigidifying, fusing) all measurements. The key enabler is the availability of multiple slightly perturbed measurements of each instance:, local measurement, "bursts", that allows us to estimate the local distortion induced by each instrument. We demonstrate the approach in a sequence of examples, starting with simple two-dimensional data sets and proceeding to a Wi-Fi localization problem and to the solution of a "dynamical puzzle" arising in spatio-temporal observations of the solutions of Partial Differential Equations.
HCDIR: End-to-end Hate Context Detection, and Intensity Reduction model for online comments
Venue: arXiv cs.CL 2023
Year: 2023

Warning: This paper contains examples of the language that some people may find offensive. Detecting and reducing hateful, abusive, offensive comments is a critical and challenging task on social media. Moreover, few studies aim to mitigate the intensity of hate speech. While studies have shown that context-level semantics are crucial for detecting hateful comments, most of this research focuses on English due to the ample datasets available. In contrast, low-resource languages, like Indian languages, remain under-researched because of limited datasets. Contrary to hate speech detection, hate intensity reduction remains unexplored in high-resource and low-resource languages. In this paper, we propose a novel end-to-end model, HCDIR, for Hate Context Detection, and Hate Intensity Reduction in social media posts. First, we fine-tuned several pre-trained language models to detect hateful comments to ascertain the best-performing hateful comments detection model. Then, we identified the contextual hateful words. Identification of such hateful words is justified through the state-of-the-art explainable learning model, i.e., Integrated Gradient (IG). Lastly, the Masked Language Modeling (MLM) model has been employed to capture domain-specific nuances to reduce hate intensity. We masked the 50\% hateful words of the comments identified as hateful and predicted the alternative words for these masked terms to generate convincing sentences. An optimal replacement for the original hate comments from the feasible sentences is preferred. Extensive experiments have been conducted on several recent datasets using automatic metric-based evaluation (BERTScore) and thorough human evaluation. To enhance the faithfulness in human evaluation, we arranged a group of three human annotators with varied expertise.
EPNet: An Efficient Pyramid Network for Enhanced Single-Image Super-Resolution with Reduced Computational Requirements
Venue: arXiv cs.CV 2023
Year: 2023

Single-image super-resolution (SISR) has seen significant advancements through the integration of deep learning. However, the substantial computational and memory requirements of existing methods often limit their practical application. This paper introduces a new Efficient Pyramid Network (EPNet) that harmoniously merges an Edge Split Pyramid Module (ESPM) with a Panoramic Feature Extraction Module (PFEM) to overcome the limitations of existing methods, particularly in terms of computational efficiency. The ESPM applies a pyramid-based channel separation strategy, boosting feature extraction while maintaining computational efficiency. The PFEM, a novel fusion of CNN and Transformer structures, enables the concurrent extraction of local and global features, thereby providing a panoramic view of the image landscape. Our architecture integrates the PFEM in a manner that facilitates the streamlined exchange of feature information and allows for the further refinement of image texture details. Experimental results indicate that our model outperforms existing state-of-the-art methods in image resolution quality, while considerably decreasing computational and memory costs. This research contributes to the ongoing evolution of efficient and practical SISR methodologies, bearing broader implications for the field of computer vision.
The Devil Is in the Command Line: Associating the Compiler Flags With the Binary and Build Metadata
Venue: arXiv cs.SE 2023
Year: 2023

Engineers build large software systems for multiple architectures, operating systems, and configurations. A set of inconsistent or missing compiler flags generates code that catastrophically impacts the system's behavior. In the authors' industry experience, defects caused by an undesired combination of compiler flags are common in nontrivial software projects. We are unaware of any build and CI/CD systems that track how the compiler produces a specific binary in a structured manner. We postulate that a queryable database of how the compiler compiled and linked the software system will help to detect defects earlier and reduce the debugging time.
Understanding and Estimating Domain Complexity Across Domains
Venue: arXiv cs.AI 2023
Year: 2023

Artificial Intelligence (AI) systems, trained in controlled environments, often struggle in real-world complexities. We propose a general framework for estimating domain complexity across diverse environments, like open-world learning and real-world applications. This framework distinguishes between intrinsic complexity (inherent to the domain) and extrinsic complexity (dependent on the AI agent). By analyzing dimensionality, sparsity, and diversity within these categories, we offer a comprehensive view of domain challenges. This approach enables quantitative predictions of AI difficulty during environment transitions, avoids bias in novel situations, and helps navigate the vast search spaces of open-world domains.
Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling
Venue: arXiv cs.CL 2023
Year: 2023

Few-shot intent classification and slot filling are important but challenging tasks due to the scarcity of finely labeled data. Therefore, current works first train a model on source domains with sufficiently labeled data, and then transfer the model to target domains where only rarely labeled data is available. However, experience transferring as a whole usually suffers from gaps that exist among source domains and target domains. For instance, transferring domain-specific-knowledge-related experience is difficult. To tackle this problem, we propose a new method that explicitly decouples the transferring of general-semantic-representation-related experience and the domain-specific-knowledge-related experience. Specifically, for domain-specific-knowledge-related experience, we design two modules to capture intent-slot relation and slot-slot relation respectively. Extensive experiments on Snips and FewJoint datasets show that our method achieves state-of-the-art performance. The method improves the joint accuracy metric from 27.72% to 42.20% in the 1-shot setting, and from 46.54% to 60.79% in the 5-shot setting.
HyperEditor: Achieving Both Authenticity and Cross-Domain Capability in Image Editing via Hypernetworks
Venue: arXiv cs.CV 2023
Year: 2023

Editing real images authentically while also achieving cross-domain editing remains a challenge. Recent studies have focused on converting real images into latent codes and accomplishing image editing by manipulating these codes. However, merely manipulating the latent codes would constrain the edited images to the generator's image domain, hindering the attainment of diverse editing goals. In response, we propose an innovative image editing method called HyperEditor, which utilizes weight factors generated by hypernetworks to reassign the weights of the pre-trained StyleGAN2's generator. Guided by CLIP's cross-modal image-text semantic alignment, this innovative approach enables us to simultaneously accomplish authentic attribute editing and cross-domain style transfer, a capability not realized in previous methods. Additionally, we ascertain that modifying only the weights of specific layers in the generator can yield an equivalent editing result. Therefore, we introduce an adaptive layer selector, enabling our hypernetworks to autonomously identify the layers requiring output weight factors, which can further improve our hypernetworks' efficiency. Extensive experiments on abundant challenging datasets demonstrate the effectiveness of our method.
Energy Efficiency Maximization for Intelligent Surfaces Aided Massive MIMO with Zero
Venue: arXiv cs.IT 2023
Year: 2023

In this work, we address the energy efficiency (EE) maximization problem in a downlink communication system utilizing reconfigurable intelligent surface (RIS) in a multi-user massive multiple-input multiple-output (mMIMO) setup with zero-forcing (ZF) precoding. The channel between the base station (BS) and RIS operates under a Rician fading with Rician factor K1. Since systematically optimizing the RIS phase shifts in each channel coherence time interval is challenging and burdensome, we employ the statistical channel state information (CSI)-based optimization strategy to alleviate this overhead. By treating the RIS phase shifts matrix as a constant over multiple channel coherence time intervals, we can reduce the computational complexity while maintaining an interesting performance. Based on an ergodic rate (ER) lower bound closed-form, the EE optimization problem is formulated. Such a problem is non-convex and challenging to tackle due to the coupled variables. To circumvent such an obstacle, we explore the sequential optimization approach where the power allocation vector p, the number of antennas M, and the RIS phase shifts v are separated and sequentially solved iteratively until convergence. With the help of the Lagrangian dual method, fractional programming (FP) techniques, and Lemma 1, insightful compact closed-form expressions for each of the three optimization variables are derived. Simulation results validate the effectiveness of the proposed method across different generalized channel scenarios, including non-line-of-sight (NLoS) and partially line-of-sight (LoS) conditions. This underscores its potential to significantly reduce power consumption, decrease the number of active antennas at the base station, and effectively incorporate RIS structure in mMIMO communication setup with just statistical CSI knowledge.
ARBiBench: Benchmarking Adversarial Robustness of Binarized Neural Networks
Venue: arXiv cs.CV 2023
Year: 2023

Network binarization exhibits great potential for deployment on resource-constrained devices due to its low computational cost. Despite the critical importance, the security of binarized neural networks (BNNs) is rarely investigated. In this paper, we present ARBiBench, a comprehensive benchmark to evaluate the robustness of BNNs against adversarial perturbations on CIFAR-10 and ImageNet. We first evaluate the robustness of seven influential BNNs on various white-box and black-box attacks. The results reveal that 1) The adversarial robustness of BNNs exhibits a completely opposite performance on the two datasets under white-box attacks. 2) BNNs consistently exhibit better adversarial robustness under black-box attacks. 3) Different BNNs exhibit certain similarities in their robustness performance. Then, we conduct experiments to analyze the adversarial robustness of BNNs based on these insights. Our research contributes to inspiring future research on enhancing the robustness of BNNs and advancing their application in real-world scenarios.
Towards More Faithful Natural Language Explanation Using Multi-Level Contrastive Learning in VQA
Venue: arXiv cs.CL 2023
Year: 2023

Natural language explanation in visual question answer (VQA-NLE) aims to explain the decision-making process of models by generating natural language sentences to increase users' trust in the black-box systems. Existing post-hoc methods have achieved significant progress in obtaining a plausible explanation. However, such post-hoc explanations are not always aligned with human logical inference, suffering from the issues on: 1) Deductive unsatisfiability, the generated explanations do not logically lead to the answer; 2) Factual inconsistency, the model falsifies its counterfactual explanation for answers without considering the facts in images; and 3) Semantic perturbation insensitivity, the model can not recognize the semantic changes caused by small perturbations. These problems reduce the faithfulness of explanations generated by models. To address the above issues, we propose a novel self-supervised \textbf{M}ulti-level \textbf{C}ontrastive \textbf{L}earning based natural language \textbf{E}xplanation model (MCLE) for VQA with semantic-level, image-level, and instance-level factual and counterfactual samples. MCLE extracts discriminative features and aligns the feature spaces from explanations with visual question and answer to generate more consistent explanations. We conduct extensive experiments, ablation analysis, and case study to demonstrate the effectiveness of our method on two VQA-NLE benchmarks.
Navigating the Structured What-If Spaces: Counterfactual Generation via Structured Diffusion
Venue: arXiv cs.LG 2023
Year: 2023

Generating counterfactual explanations is one of the most effective approaches for uncovering the inner workings of black-box neural network models and building user trust. While remarkable strides have been made in generative modeling using diffusion models in domains like vision, their utility in generating counterfactual explanations in structured modalities remains unexplored. In this paper, we introduce Structured Counterfactual Diffuser or SCD, the first plug-and-play framework leveraging diffusion for generating counterfactual explanations in structured data. SCD learns the underlying data distribution via a diffusion model which is then guided at test time to generate counterfactuals for any arbitrary black-box model, input, and desired prediction. Our experiments show that our counterfactuals not only exhibit high plausibility compared to the existing state-of-the-art but also show significantly better proximity and diversity.
Multi-Modal Domain Adaptation Across Video Scenes for Temporal Video Grounding
Venue: arXiv cs.CV 2023
Year: 2023

Temporal Video Grounding (TVG) aims to localize the temporal boundary of a specific segment in an untrimmed video based on a given language query. Since datasets in this domain are often gathered from limited video scenes, models tend to overfit to scene-specific factors, which leads to suboptimal performance when encountering new scenes in real-world applications. In a new scene, the fine-grained annotations are often insufficient due to the expensive labor cost, while the coarse-grained video-query pairs are easier to obtain. Thus, to address this issue and enhance model performance on new scenes, we explore the TVG task in an unsupervised domain adaptation (UDA) setting across scenes for the first time, where the video-query pairs in the source scene (domain) are labeled with temporal boundaries, while those in the target scene are not. Under the UDA setting, we introduce a novel Adversarial Multi-modal Domain Adaptation (AMDA) method to adaptively adjust the model's scene-related knowledge by incorporating insights from the target data. Specifically, we tackle the domain gap by utilizing domain discriminators, which help identify valuable scene-related features effective across both domains. Concurrently, we mitigate the semantic gap between different modalities by aligning video-query pairs with related semantics. Furthermore, we employ a mask-reconstruction approach to enhance the understanding of temporal semantics within a scene. Extensive experiments on Charades-STA, ActivityNet Captions, and YouCook2 demonstrate the effectiveness of our proposed method.
On the Hardness of Analyzing Quantum Programs Quantitatively
Venue: arXiv cs.LO 2023
Year: 2023

In this paper, we study quantitative properties of quantum programs. Properties of interest include (positive) almost-sure termination, expected runtime or expected cost, that is, for example, the expected number of applications of a given quantum gate, etc. After studying the completeness of these problems in the arithmetical hierarchy over the Clifford+T fragment of quantum mechanics, we express these problems using a variation of a quantum pre-expectation transformer, a weakest precondition based technique that allows to symbolically compute these quantitative properties. Under a smooth restriction-a restriction to polynomials of bounded degree over a real closed field-we show that the quantitative problem, which consists in finding an upper-bound to the pre-expectation, can be decided in time double-exponential in the size of a program, thus providing, despite its great complexity, one of the first decidable results on the analysis and verification of quantum programs. Finally, we sketch how the latter can be transformed into an efficient synthesis method.