Abstract

Accurate localization of sound sources is essential in many acoustic sensing and monitoring applications. In the absence of temporal continuity models, many methods produce unrealistic direction of arrival (DOA) estimates involving sudden changes. To address this, we propose an approach that trains a neural network to predict DOA derivatives in Cartesian coordi- nates (x′, y′, z′), which capture the rate of change in DOA (x, y, z) over time. By combining the predicted DOAs with the predicted derivatives, our method can suppress sudden DOA changes and generate smooth motion trajectories. We introduce an update rule that combines the predicted DOAs with the predicted derivatives to obtain the final DOAs. We validate our approach using the TAU-NIGENS Spatial Sound Events (TNSSE) 2021 dataset. Our results demonstrate that incorporating DOA derivatives improves the accuracy of DOA estimation, particularly in low signal-to- noise ratio scenarios

Abstract

Modern transceiver integrated circuits used in wireless communication systems seek low power wide frequency range receiver front-ends (RxFE) to support multiple communication bands. For this, RxFE leveraging passive N-path mixer first approach are becoming progressively popular due to their low power nature. However, N-path RxFE requires N precise phases of a local oscillator (LO) to control mixer operation, which becomes challenging as the frequency of operation increases (> 10 GHz). In this paper, we give useful insights and present design considerations for the faithful generation of different non- overlapping phases of LO to achieve wide band RF input match. We also present the design and post-layout simulation results of a 4-path mixer based 8-20 GHz RxFE with programmable gain of 10 to 29 dB in TSMC 65 nm CMOS technology. In post-layout simulations, the proposed design achieves an IIP3 of > -10 dB, S11 of < -12 dB and consumes total power of 66 mW from 1.2 V supply.

Abstract

Efficient and reliable non-invasive methods for micro-organism detection have significant implications in various fields such as medical diagnostics, environmental monitoring, and food safety. Existing detection techniques, such as culture-based methods, polymerase chain reaction (PCR) have limitations including time-consuming processes, expensive equipment requirements and invasive sampling procedures. These drawbacks emphasize the need for alternate detection techniques that are non-invasive, rapid, sensitive, specific, and user-friendly. For this purpose, in this research, we present a portable integrated system for micro-organism detection, which is based on electrochemical impedance spectroscopy (EIS). The proposed system includes Zinc Oxide (ZnO) nanorods based interdigitized electrode (IDE) sensor and an EIS circuit. This work also presents the considerations and methodology for the fabrication of ZnO nanorods with high-yield. To validate the efficacy of the proposed methods, experimental results through scanning electron microscope are also presented. Finally, measurement results for detection of a micro-organism (yeast) is also presented to demonstrate the utility and effectiveness of the proposed methods and system.

Abstract

Generative AI has seen remarkable growth over the past few years, with diffusion models being state-of-the-art for image generation. This study investigates the use of diffusion models in generating artificial data generation for electronic circuits for enhancing the accuracy of subsequent machine learning models in tasks such as performance assessment, design, and testing when training data is usually known to be very limited. We utilize simulations in the HSPICE design environment with 22nm CMOS technology nodes to obtain representative real training data for our proposed diffusion model. Our results demonstrate the close resemblance of synthetic data using diffusion model to real data. We validate the quality of generated data, and demonstrate that data augmentation certainly effective in predictive analysis of VLSI design for digital circuits.

Abstract

Criticality, observed during second-order phase transitions, is an emergent phenomenon. The brain operates near criticality where complex systems exhibit high correlations. As a system approaches criticality, it develops “domain”-like regions with competing phases and increased spatio-temporal correlations that diverge. The dynamics of these domains depend on the system’s proximity to criticality. This study explores the differences in the proximity to criticality of Alzheimer’s-afflicted and cognitively normal brains through the use of a spin-lattice model derived from resting-state fMRI data and investigates the type of criticality found in the human brain - whether it is of the Ising class or something more complex. The autocorrelation relaxation time in both groups displays a stretched exponential nature, indicating closer alignment with the criticality of the spin-glass class rather than the Ising class. Longer relaxation times observed in cognitively normal subjects suggest increased proximity to the phase boundary. The weak distinction observed in the spatial characteristics related to proximity to criticality might once more point to a spin-glass scenario, necessitating nuanced order parameters to distinguish between phase-ordering in Alzheimer’s and cognitively normal brains.

Abstract

Absolute separable (AS) quantum states are those states from which it is impossible to create entanglement, even under global unitary operations. It is known from the resource theory of non- absolute separability that the set of absolute separable states forms a convex and compact set, and global unitaries are free operations. We show that the action of a quantum switch controlled by an ancilla qubit over the global unitaries can break this robustness of AS states and produce ordinary separable states. First, we consider bipartite qubit systems and find the effect of quantum switch starting from the states sitting on the boundary of the set of absolute separable states. As particular examples, we illustrate what happens to modified Werner states and Bell diagonal (BD) states. For the Bell diagonal states, we provide the structure for the set of AS BD states and show how the structure changes under the influence of a switch. Further, we consider numerical generalization of the global unitary operations and show that it is always possible to take AS states out of the convex set under switching operations. We also generalized our results in higher dimensions.

Abstract

The burdensome impact of a skewed judges-to-cases ratio on the judicial system manifests in an overwhelming backlog of pending cases alongside an ongoing influx of new ones. To tackle this issue and expedite the judicial process, the proposition of an automated system capable of suggesting case outcomes based on factual evidence and precedent from past cases gains significance. This research paper centres on developing a graph neural network-based model to address the Legal Judgment Prediction (LJP) problem, recognizing the intrinsic graph structure of judicial cases and making it a binary node classification problem. We explored various embeddings as model features, while nodes such as time nodes and judicial acts were added and pruned to evaluate the model's performance. The study is done while considering the ethical dimension of fairness in these predictions, considering gender and name biases. A link prediction task is also conducted to assess the model's proficiency in anticipating connections between two specified nodes. By harnessing the capabilities of graph neural networks and incorporating fairness analyses, this research aims to contribute insights towards streamlining the adjudication process, enhancing judicial efficiency, and fostering a more equitable legal landscape, ultimately alleviating the strain imposed by mounting case backlogs. Our best-performing model with XLNet pre-trained embeddings as its features gives the macro F1 score of 75% for the LJP task. For link prediction, the same set of features is the best performing giving ROC of more than 80%

Abstract

The default approach to deal with the enormous size and lim- ited accessibility of many Web and social media networks is to sample one or more subnetworks from a conceptually unbounded unknown network. Clearly, the extracted subnet- works will crucially depend on the sampling scheme. Mo- tivated by studies of homophily and opinion formation, we propose a variant of snowball sampling designed to priori- tize inclusion of entire cohesive communities rather than any kind of representativeness, breadth, or depth of coverage. The method is illustrated on a concrete example, and experiments on synthetic networks suggest that it behaves as desired.

Abstract

Abstract Working from home (WFH) is a new reality and norm in today’s work culture. COVID-induced lockdown introduced the concept of WFH for many people. Blurring home and workplace boundaries was a prominent cause of disorientation in people’s lives. Hence, WFH becomes a signifcant phenomenon to explore as it raises the fundamental question of body and space in shaping people’s experiences. To study this, the researchers designed a phenomenological inquiry and examined the lived phenomenon of WFH during the COVID lockdown. Borrowing theoretical concepts from philosophers Martin Heidegger and Merleau-Ponty, they aimed to understand human experiences from an embodied perspective. They interviewed a few adults (age group 27–50) in three urban Indian cities during the frst phase of the COVID-19 lockdown. The participants’ experiences were transcribed, and Interpretative Phenomenological Analysis (IPA) was conducted. The IPA themes highlighted their varied lived experiences and lifeworld, such as distractions in working from home, their changed routines, habits, and social interaction. The fndings are discussed through phenomenological psychology concepts such as embodied cognition, body memory, extended self, shared empathy, intersubjectivity and prerefective bodily intelligence in coping. The study’s contribution is that it advances a methodological understanding by interpreting people’s experiences from a phenomenological view of being-in-the-world in which the individual is not an isolated

Abstract

Understanding the factors that determine video memorability has important applications in areas such as educational technology and advertising. Towards this goal, we investigate the semantic and temporal attention mechanisms underlying video memorability. We propose a Transformer based model with spatio-temporal attention that matches SoTA performance on video memorability prediction on a large naturalistic video dataset. More importantly, the self attention patterns show us where the model looks to predict memorability. We compare model attention against humangaze fixation density maps collected through a small-scale eye-tracking experiment where humans perform a video memory task. Quantitative saliency metrics show that the model attention and human gaze follow similar patterns. Furthermore, while panoptic segmentation confirms that the model and humans attend more to thing classes, stuff classes that receive increased/decreased attention tend to have higher memorability scores. We also observe that the model assigns greater importance to the initial frames, mimicking temporal attention patterns found in humans