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Recently, there have been significant advancements in Image Restoration based on CNN and transformer. However, the inherent characteristics of the Image Restoration task are often overlooked in many works. These works often focus on the basic block design and stack numerous basic blocks to the model, leading to redundant parameters and unnecessary computations and hindering the efficiency of the image restoration. In this paper, we propose a Lightweight Image Restoration network called LIR to efficiently remove degradation (blur, rain, noise, haze, etc.). A key component in LIR is the Efficient Adaptive Attention (EAA) Block, which is mainly composed of Adaptive Filters and Attention Blocks. It is capable of adaptively sharpening contours, removing degradation, and capturing global information in various image restoration scenes in an efficient and computation-friendly manner. In addition, through a simple structural design, LIR addresses the degradations existing in the local and global residual connections that are ignored by modern networks. Extensive experiments demonstrate that our LIR achieves comparable performance to state-of-the-art networks on most benchmarks with fewer parameters and computations. It is worth noting that our LIR produces better visual results than state-of-the-art networks that are more in line with the human aesthetic.

Parameter Efficient Fine-Tuning (PEFT) is an alternate choice to full fine-tuning a language model. Though PEFT methods are used in natural language domain widely, there are limited studies on using PEFT for language models that are pre-trained on code and comment datasets (i.e., code-LMs). Previous research has also shown that code summarization, a task that intends to generate natural description of the given code snippet automatically and is known to benefit the program comprehension, benefits from multilingual fine-tuning approach. In multilingual fine-tuning, the code-LM is fine-tuned on a dataset consisting of different programming languages. AdapterFusion is a specific PEFT approach that aims to extract and compose the latent knowledge from multiple (language) adapters for a downstream task. However, our experiments reveal that the AdapterFusion still learns from the same language, not taking advantage of other programming languages. Therefore, we change the architecture and propose AdvFusion, a PEFT approach that enforces the model to first learn from other programming languages, and then pay attention to the language of the target task. Therefore, the AdvFusion emphasizes the knowledge transfer among different programming languages, as stated in the multilingual fine-tuning. Our results on the CodeSearchNet dataset using two code-LMs, show that Adapters, AdapterFusion, and our proposed AdvFusion can achieve results on-par with or higher than the full fine-tuning models for code summarization and method name prediction. Notably, the number of trainable parameters are 123x less and the training time is reduced by ~30%. AdvFusion exhibits a notable enhancement compared to AdapterFusion, showcasing a 0.9 to 1.7-point increase in BLEU-4 scores specifically for Ruby, JavaScript, and Go.

Table understanding capability of Large Language Models (LLMs) has been extensively studied through the task of question-answering (QA) over tables. Typically, only a small part of the whole table is relevant to derive the answer for a given question. The irrelevant parts act as noise and are distracting information, resulting in sub-optimal performance due to the vulnerability of LLMs to noise. To mitigate this, we propose CABINET (Content RelevAnce-Based NoIse ReductioN for TablE QuesTion-Answering) - a framework to enable LLMs to focus on relevant tabular data by suppressing extraneous information. CABINET comprises an Unsupervised Relevance Scorer (URS), trained differentially with the QA LLM, that weighs the table content based on its relevance to the input question before feeding it to the question-answering LLM (QA LLM). To further aid the relevance scorer, CABINET employs a weakly supervised module that generates a parsing statement describing the criteria of rows and columns relevant to the question and highlights the content of corresponding table cells. CABINET significantly outperforms various tabular LLM baselines, as well as GPT3-based in-context learning methods, is more robust to noise, maintains outperformance on tables of varying sizes, and establishes new SoTA performance on WikiTQ, FeTaQA, and WikiSQL datasets. We release our code and datasets at //github.com/Sohanpatnaik106/CABINET_QA.

The effectiveness of Intrusion Detection Systems (IDS) is critical in an era where cyber threats are becoming increasingly complex. Machine learning (ML) and deep learning (DL) models provide an efficient and accurate solution for identifying attacks and anomalies in computer networks. However, using ML and DL models in IDS has led to a trust deficit due to their non-transparent decision-making. This transparency gap in IDS research is significant, affecting confidence and accountability. To address, this paper introduces a novel Explainable IDS approach, called X-CBA, that leverages the structural advantages of Graph Neural Networks (GNNs) to effectively process network traffic data, while also adapting a new Explainable AI (XAI) methodology. Unlike most GNN-based IDS that depend on labeled network traffic and node features, thereby overlooking critical packet-level information, our approach leverages a broader range of traffic data through network flows, including edge attributes, to improve detection capabilities and adapt to novel threats. Through empirical testing, we establish that our approach not only achieves high accuracy with 99.47% in threat detection but also advances the field by providing clear, actionable explanations of its analytical outcomes. This research also aims to bridge the current gap and facilitate the broader integration of ML/DL technologies in cybersecurity defenses by offering a local and global explainability solution that is both precise and interpretable.

Combining the strengths of many existing predictors to obtain a Mixture of Experts which is superior to its individual components is an effective way to improve the performance without having to develop new architectures or train a model from scratch. However, surprisingly, we find that na\"ively combining expert object detectors in a similar way to Deep Ensembles, can often lead to degraded performance. We identify that the primary cause of this issue is that the predictions of the experts do not match their performance, a term referred to as miscalibration. Consequently, the most confident detector dominates the final predictions, preventing the mixture from leveraging all the predictions from the experts appropriately. To address this, when constructing the Mixture of Experts, we propose to combine their predictions in a manner which reflects the individual performance of the experts; an objective we achieve by first calibrating the predictions before filtering and refining them. We term this approach the Mixture of Calibrated Experts and demonstrate its effectiveness through extensive experiments on 5 different detection tasks using a variety of detectors, showing that it: (i) improves object detectors on COCO and instance segmentation methods on LVIS by up to $\sim 2.5$ AP; (ii) reaches state-of-the-art on COCO test-dev with $65.1$ AP and on DOTA with $82.62$ $\mathrm{AP_{50}}$; (iii) outperforms single models consistently on recent detection tasks such as Open Vocabulary Object Detection.

To obtain high-quality Positron emission tomography (PET) images while minimizing radiation exposure, numerous methods have been proposed to reconstruct standard-dose PET (SPET) images from the corresponding low-dose PET (LPET) images. However, these methods heavily rely on voxel-based representations, which fall short of adequately accounting for the precise structure and fine-grained context, leading to compromised reconstruction. In this paper, we propose a 3D point-based context clusters GAN, namely PCC-GAN, to reconstruct high-quality SPET images from LPET. Specifically, inspired by the geometric representation power of points, we resort to a point-based representation to enhance the explicit expression of the image structure, thus facilitating the reconstruction with finer details. Moreover, a context clustering strategy is applied to explore the contextual relationships among points, which mitigates the ambiguities of small structures in the reconstructed images. Experiments on both clinical and phantom datasets demonstrate that our PCC-GAN outperforms the state-of-the-art reconstruction methods qualitatively and quantitatively. Code is available at //github.com/gluucose/PCCGAN.

Current IR evaluation is based on relevance judgments, created either manually or automatically, with decisions outsourced to Large Language Models (LLMs). We offer an alternative paradigm, that never relies on relevance judgments in any form. Instead, a text is defined as relevant if it contains information that enables the answering of key questions. We use this idea to design the EXAM Answerability Metric to evaluate information retrieval/generation systems for their ability to provide topically relevant information. We envision the role of a human judge to edit and define an exam question bank that will test for the presence of relevant information in text. We support this step by generating an initial set of exam questions. In the next phase, an LLM-based question answering system will automatically grade system responses by tracking which exam questions are answerable with which system responses. We propose two evaluation measures, the recall-oriented EXAM Cover metric, and the precision-oriented EXAM Qrels metric, the latter which can be implemented with trec_eval. This paradigm not only allows for the expansion of the exam question set post-hoc but also facilitates the ongoing evaluation of future information systems, whether they focus on retrieval, generation, or both.

System Verilog Assertion (SVA) formulation, a critical yet complex task, is a pre-requisite in the Formal Property Verification (FPV) process. Traditionally, SVA formulation involves expert-driven interpretation of specifications. This is time consuming and prone to human error. However, recent advances in Large Language Models (LLM), LLM-informed automatic assertion generation is gaining interest. We designed a novel LLM-based pipeline to generate assertions in English Language, Linear Temporal Logic, and SVA from natural language specifications. We developed a custom LLM-based on OpenAI GPT4 for our experiments. Furthermore, we developed testbenches to verify/validate the LLM-generated assertions. Only 43% of LLM-generated raw assertions had errors, including syntax and logical errors. By iteratively prompting the LLMs using carefully crafted prompts derived from test case failures, the pipeline could generate correct SVAs after a maximum of nine iterations of prompting. Our results show that LLMs can streamline the assertion generation workflow, reshaping verification workflows.

Due to its conceptual simplicity and generality, compressive neural representation has emerged as a promising alternative to traditional compression methods for managing massive volumetric datasets. The current practice of neural compression utilizes a single large multilayer perceptron (MLP) to encode the global volume, incurring slow training and inference. This paper presents an efficient compressive neural representation (ECNR) solution for time-varying data compression, utilizing the Laplacian pyramid for adaptive signal fitting. Following a multiscale structure, we leverage multiple small MLPs at each scale for fitting local content or residual blocks. By assigning similar blocks to the same MLP via size uniformization, we enable balanced parallelization among MLPs to significantly speed up training and inference. Working in concert with the multiscale structure, we tailor a deep compression strategy to compact the resulting model. We show the effectiveness of ECNR with multiple datasets and compare it with state-of-the-art compression methods (mainly SZ3, TTHRESH, and neurcomp). The results position ECNR as a promising solution for volumetric data compression.

Besides entity-centric knowledge, usually organized as Knowledge Graph (KG), events are also an essential kind of knowledge in the world, which trigger the spring up of event-centric knowledge representation form like Event KG (EKG). It plays an increasingly important role in many machine learning and artificial intelligence applications, such as intelligent search, question-answering, recommendation, and text generation. This paper provides a comprehensive survey of EKG from history, ontology, instance, and application views. Specifically, to characterize EKG thoroughly, we focus on its history, definitions, schema induction, acquisition, related representative graphs/systems, and applications. The development processes and trends are studied therein. We further summarize perspective directions to facilitate future research on EKG.