Abstract

We present CheXtriev, a graph-based, anatomy-aware frame- work for chest radiograph retrieval. Unlike prior methods focussed on global features, our method leverages graph transformers to extract in- formative features from specific anatomical regions. Furthermore, it cap- tures spatial context and the interplay between anatomical location and findings. This contextualization, grounded in evidence-based anatomy, results in a richer anatomy-aware representation and leads to more accu- rate, effective and efficient retrieval, particularly for less prevalent find- ings. CheXtriv outperforms state-of-the-art global and local approaches by 18% to 26% in retrieval accuracy and 11% to 23% in ranking quality. The code is available at https://github.com/cvit-mip/chextriev.

Abstract

Autonomous Underwater Vehicles (AUVs) play a vital role in explor- ing and mapping underwater environments. However, the presence of nadir gaps, or blind zones, in commercial AUVs can lead to unex- plored areas during mission execution, limiting their effectiveness. Our work addresses the challenges of path planning in the presence of nadir gaps and presents scalable coverage strategies for AUVs minimizing either the mission completion time or the total number of turns performed while ensuring complete exploration, eliminating the risk of leaving critical areas unexplored. We provide provably complete strategies and perform extensive simulations on diverse input configurations based on real-world instances to demonstrate the efficacy of our strategies.

Abstract

Around the world, there are a lot of unreinforced masonry (URM) buildings situated in seismically active areas. Additionally, the majority of these buildings were not built in accordance with seismic code requirements. Buildings made of URM are seismically unsafe and require strengthening, as demonstrated by previous earthquakes. Masonry material exhibits a great deal of variability in its behaviour, and the tensile or flexural capacity of the structural components is one of the key factors in the seismic susceptibility of masonry buildings. The flexural strength and deformability of URM walls can be increased using a variety of retrofitting methods. These can be loosely divided into two categories: “conventional” and “advanced”. The surface treatments (e.g., shotcrete, reinforced plaster, ferrocement overlays, polyurea spray technology), stitching and grout or epoxy injections, re-pointing, reinforced concrete confinement, and post-tensioning fall under the first category. The second category includes the use of seismic wallpaper (i.e., fiber-reinforced polymers—FRP such as epoxy-bonded strips or in situ impregnated fabrics), textile-reinforced mortar composites (mesh/fabric reinforcement in the form of fibre, wire, polypropylene bands, plastic bags, bamboo, etc.), and centre core. The present study reviews the state-of-the-art literature on several methods for reinforcing URM walls subject to lateral earthquake loading. The paper provides a complete overview of retrofitting techniques, including benefits, suitability, applicability, drawbacks, restrictions, sustainability, and consequences on rural communities. In addition, the effectiveness of retrofitting methods is compared using factors including weight, technology (i.e., level of skill required), cost, manpower, and disruption to occupants. The study’s findings will help the policymakers, archaeological survey of India (ASI), various government departments owning low-rise masonry buildings, planners, building owners, architects, structural engineers, consultants, and designers, select an appropriate retrofitting system. Further, the design methodology for retrofitting masonry walls using advanced composites like TRM and FRP is also described in detail, using an explanatory example for seismic in-plane and out-of-plane actions.

Abstract

Edge computing allows for reduced latency and operational costs compared to centralized cloud systems. In this context, serverless functions are emerging as a lightweight and effective paradigm for managing computational tasks on edge infrastructures. However, the placement of such functions in constrained edge nodes remains an open challenge. On one hand, it is key to minimize network delays and optimize resource consumption; on the other hand, decisions must be made in a timely manner due to the highly dynamic nature of edge environments. In this paper, we propose POSEIDON, a solution based on Deep Reinforcement Learning for the efficient placement of functions at the edge. POSEIDON leverages Proximal Policy Optimization (PPO) to place functions across a distributed network of nodes under highly dynamic workloads. A comprehensive empirical evaluation demonstrates that POSEIDON significantly reduces execution time, network delay, and resource consumption compared to state-of-the-art methods.

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by challenges in social communication, repetitive behaviors, and restricted interests. Emerging research has emphasized the significance of metabolic dysfunctions in the pathophysiology of ASD, including abnormalities in energy production, mitochondrial dysfunction, and oxidative stress. This study explores metabolic alterations specific to brain tissues, cell types and blood in individuals with ASD by integrating omics data with the human metabolic network. We identified significant brain-specific metabolic changes in ASD, underscoring hypometabolism and reduced glucose utilization. Additionally, alterations in pathways related to fatty acid metabolism, amino acid metabolism, and vitamin metabolism were observed. The analysis of cell-specific metabolic activities in the ASD brain showed alterations in pathways in astrocytes, microglia, and oligodendrocytes. Metabolic gene expression in peripheral blood mononuclear cells (PBMC) samples also showed potential as diagnostic biomarkers. These findings offer insights into metabolic interventions in ASD, as well as potential biomarkers and therapeutic targets for ASD.

Abstract

In the circuit model of quantum computing, amplitude amplification techniques can be used to find solutions to NP-hard problems defined on n-bits in time poly(n)2^{n/2}. In this work, we investigate whether such general statements can be made for adiabatic quantum optimization, as provable results regarding its performance are mostly unknown. Although a lower bound of Ω(2^{n/2}) has existed in such a setting for over a decade, a purely adiabatic algorithm with this running time has been absent. We show that adiabatic quantum optimization using an unstructured search approach results in a running time that matches this lower bound (up to a polylogarithmic factor) for a broad class of classical local spin Hamiltonians. For this, it is necessary to bound the spectral gap throughout the adiabatic evolution and compute beforehand the position of the avoided crossing with sufficient precision so as to adapt the adiabatic schedule accordingly. However, we show that the position of the avoided crossing is approximately given by a quantity that depends on the degeneracies and inverse gaps of the problem Hamiltonian and is NP-hard to compute even within a low additive precision. Furthermore, computing it exactly (or nearly exactly) is #P-hard. Our work indicates a possible limitation of adiabatic quantum optimization algorithms, leaving open the question of whether provable Grover-like speed-ups can be obtained for any optimization problem using this approach.

Abstract

In the legal domain, research efforts are being made to enhance precedent retrieval by exploiting features based on meta-data, catchphrases, citations, sentences, paragraphs, etc. It is reported in the legal domain that the text surrounding a citation provides information to identify the referenced judgment and supplies additional information about the referenced judgment and its connection to the formulated argument. In this paper, we have exploited the resourcefulness of the text surrounding the citation to improve the document representation of the referenced judgment. Experiments conducted on Indian court judgments show that the proposed Preceding citation Anchor Text (PAT)-based approach captures certain nuances that are not captured by the text present in the referenced judgment, indicating that there is a scope to exploit PAT to improve the performance of precedent retrieval systems.

Abstract

The limited generalization of coreference resolution (CR) models has been a major bottleneck in the task’s broad application. Prior work has identified annotation differences, especially for mention detection, as one of the main reasons for the generalization gap and proposed using additional annotated target domain data. Rather than relying on this additional annotation, we propose an alternative referential task, Major Entity Identification (MEI), where we: (a) assume the target entities to be specified in the input, and (b) limit the task to only the frequent entities. Through extensive experiments, we demonstrate that MEI models generalize well across domains on multiple datasets with supervised models and LLM-based few-shot prompting. Additionally, MEI fits the classification framework, which enables the use of robust and intuitive classification-based metrics. Finally, MEI is also of practical use as it allows a user to search for all mentions of a particular entity or a group of entities of interest.

Abstract

Seismic signal classification plays a crucial role in mitigating the impact of seismic events on human lives and infrastructure. Traditional methods in seismic hazard assessment often overlook the inherent uncertainties associated with the prediction of this complex geological phenomenon. This work introduces a probabilistic framework that leverages Bayesian principles to model and quantify uncertainty in seismic signal classification by applying a Bayesian Convolutional Neural Network (BCNN). The BCNN was trained on a dataset that comprises waveforms detected in the Southern California region and achieved an accuracy of 99.1%. Monte Carlo Sampling subsequently creates a 95% prediction interval for probabilities that considers epistemic and aleatoric uncertainties. The ability to visualize both aleatoric and epistemic uncertainties provides decision-makers with information to determine the reliability of seismic signal classifications. Further, the use of Bayesian CNN for seismic signal classification provides a more robust foundation for decision-making and risk assessment in earthquake-prone regions.

Abstract

This paper introduces an ultra-low-power RC oscillator that utilizes a resistance amplification technique. The resistance amplifier achieves the effect of a large resistance by amplifying the I-V characteristics of a small resistance, resulting in significant area savings. To improve precision in oscillation frequency, a continuous time auto-zeroing technique is incorporated to dynamically calibrate the offset voltage of an integrated amplifier. Fabricated using a 180−nm CMOS process, the designed oscillator produces a 100−Hz clock signal while consuming only 2 nW power under compact silicon area (0.12mm2). Additionally, it achieves a competitive temperature coefficient (TC) of 280ppm/∘C and line sensitivity (LS) of 1.1 % / V without the need for any calibration.