Abstract

Air pollution is a growing concern across the world. Road traffic is one of the major contributors to air pollution in urban areas. One of the approaches to solve this problem is to design a transportation policy that i) avoids extreme pollution in any area, ii) enables short transit times, and iii) makes effective use of the road capacities. Previous work to address this problem named MaxFlow-MCMC algorithm, proposed a novel sampling-based approach for this problem. In this work, we propose a significantly faster extension to the algorithm without compromising on the performance involving the following contributions: (a) We provide the first construction of a Markov Chain to sample a set of k-optimal max flow solutions directly from a planar graph. (b) We simulate traffic on large-scale real-world roadmaps using the SUMO traffic simulator. We observe a significant speed improvement in the range of 22 to 242 times in our experiments while obtaining lesser average pollution.

Abstract

Unsupervised Representation Learning on graphs is gaining traction due to the increasing abundance of unlabelled network data and the compactness, richness, and usefulness of the representations generated. In this context, the need to consider fairness and bias constraints while generating the representations has been well-motivated and studied to some extent in prior works. One major limitation of most of the prior works in this setting is that they do not aim to address the bias generated due to connectivity patterns in the graphs, such as varied node centrality, which leads to a disproportionate performance across nodes. In our work, we aim to address this issue of mitigating bias due to inherent graph structure in an unsupervised setting. To this end, we propose CAFIN, a centrality-aware fairness-inducing framework that leverages the structural information of graphs to tune the representations generated by existing frameworks. We deploy it on GraphSAGE (a popular framework in this domain) and showcase its efficacy on two downstream tasks – Node Classification and Link Prediction. Empirically, CAFIN consistently reduces the performance disparity across popular datasets (varying from 18 to 80% reduction in performance disparity) from various domains while incurring only a minimal cost of fairness.

Abstract

This paper presents a low power voltage reference and current reference in one circuit. The reference voltage is the sum of the complementary to absolute temperature (CTAT) gatesource voltage of a MOSFET operating in the subthreshold region and a scaled version of the proportional to absolute temperature (PTAT) ΔVGS of two MOSFETs operating in subthreshold region. The generated temperature independent reference current is the ratio of the reference voltage and a resistance. The proposed circuit does not use an operational transconductance amplifier (OTA) to generate the reference current, thereby eliminates the variation in current due to any offset of the OTA. Furthermore, the circuit uses a native MOSFET for ameliorating the DC PSRR. The proposed circuit is designed in a 130nm CMOS process. The circuit works with a minimum supply voltage of 1.2V while generating a reference voltage of around 0.7V and a reference current of around 100nA. A TC of 100 ppm/˚C for the reference voltage and a TC of 108 ppm/˚C for the reference current are noted for mismatch and process variations from Monte-Carlo simulations for 300 samples. The circuit consumes only 240nW of power, which makes it suitable for ultra-low power applications.

Abstract

Aspect-based summarization of a legal case file related to regulating bodies allows different stakeholders to consume information of interest therein efficiently. In this paper, we propose a multi-step process to achieve the same. First, we explore the semantic sentence segmentation of SEBI case files via classification. We also propose a dataset of Indian legal adjudicating orders which contain tags from carefully crafted domain-specific sentence categories with the help of legal experts. We experiment with various machine learning and deep learning methods for this multi-class classification. Then, we examine the performance of numerous summarization methods on the segmented document to generate persona-specific summaries. Finally, we develop a pipeline making use of the best methods in both sub-tasks to achieve high recall.

Abstract

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Abstract

For a point set, Euclidean distance matrix (EDM) is the matrix consisting of squared distances between every pair of points in the set. It frequently appears in wide ranging applications such as sensor networks, acoustic arrays, crystallography, and self localization among others. Often the measured EDM are inaccurate (due to noise) and incomplete (due to limited availability of measurements) requiring denoising and completion algorithms, frequently both. In literature, methods based on rank alternation and semi-definite relaxation (SDR) have been successful but are restricted to processing a single noisy and/or incomplete input EDM. In this paper we propose extensions of these two algorithms which can process multiple noisy and incomplete EDM simultaneously. Simulations show that the proposed joint algorithms (which process multiple EDM) outperform the corresponding individual algorithms (which process individual EDM and combine the results). We focus on the challenging case of multiplicative noise.

Abstract

Single photon avalanche diode (SPAD) plays a vital role in low-light imaging. Process libraries do not have a SPAD device model for simulation. A circuit model that can accurately simulate SPAD’s behavior is required. This paper reviews and compares currently available SPAD models. A passive quenching circuit (PQC) is used to characterize SPAD models. 30 µm X 30 µm square-shaped n+/p-sub, nwell/p-sub, p+/nwell/p-sub SPADs without guard rings are designed, and a breakdown voltage of 11.16, 14.78, and 10.22 volts is observed, respectively. The models are compared with SPAD’s quenching and recharge time. The n+/p-sub SPAD is used for our study. From this work, we conclude that the most accurate modeling of SPAD can be done by an equation-based model. We also compare the photon detection efficiency (PDE) and dark count rates (DCR). This study is conducted in the TSMC 180 nm process.

Abstract

This paper presents the implementation of inexpensive and adaptable micro-heater circuits utilizing the principles of joule heating and induction heating. The heating mechanisms were simulated and analyzed using COMSOL Multiphysics software. A comparative investigation was conducted in simulation to examine the performance of both heating techniques, and findings are reported. Furthermore, a flexible device prototype was fabricated to demonstrate the feasibility of the micro-heater, and a comparative experimental analysis was performed on micro-heaters integrated in two distinct substrates, namely polydimethylsiloxane (PDMS) and Ecoflex.

Abstract

The increasing prominence of fingerprint recognition as a biomet ric identifier has made it more vulnerable to presentation attacks, specifically non-distal attacks that exploit ridge and minutiae pat terns foundinnon-distalphalanges.Inthisstudy,wepresentpresen tation attacks through non-distal/toe prints and a state-of-the-art lightweight inverted residual network that excels at differentiating between distal and non-distal prints, providing unrivaled perfor mance in terms of accuracy, inference time, and false negative rate (FNR). Our proposed model surpasses other statistical machine learning methods, such as variable-margin SVM, and lightweight models like MobileNet v2, MobileNet v3, and ResNet18. We meticu lously evaluate our model using a diverse array of datasets, includ ing the NIST dataset, an in-house collected dataset, a toe dataset, a synthetic dataset generated by VeriFinger software, and a six-class dataset. To assess performance when only minutiae points are avail able, we develop analgorithmthat converts fingerprints to minutiae points and subsequently reconstructs fingerprints. Furthermore, we examine the ridge density of distal and non-distal prints across datasets, emphasizing their similarities and underscoring the need for advanced detection techniques. To the best of our knowledge, this study represents the first endeavor to propose a solution for presentation attack detection in non-distal phalanges. Our research demonstrate various challenges of presentation attacks, the effectiveness of our approach, which holds the potential to significantly influence the domain of finger print recognition and security. By sharing our dataset, model, and experimental details with the research community, we aim to foster further advancements in this crucial area. Upon publication, we will make our dataset and experimental details available alongside the paper