Abstract

Extensive searches to probe the particle nature of dark matter (DM) have been going on for some decades now but, so far, no conclusive evidence has been found. Among various options, the Weakly Interacting Massive Particles (WIMP) remains one of the prime possibilities as candidates for DM near the TeV scale. Taking a phenomenological view, such null results may be explained for a generic WIMP in a Higgs-portal scenario if we allow the light-quark Yukawa couplings to assume non-Standard Model (non-SM)-like values. This follows from a cancellation among different terms in the DM-nucleon scattering which can, in turn, lead to a vanishingly small direct-detection cross section. It might also lead to isospin violation in the DM-nucleon scattering. Such non-SM values of light-quark Yukawa couplings may be probed in the high luminosity run of the LHC.

Abstract

The problem of data exchange between multiple nodes with storage and communication capabilities models several current multi-user communication problems like Coded Caching, Data Shuffling, Coded Computing, etc. The goal in such problems is to design communication schemes which accomplish the desired data exchange between the nodes with the optimal (minimum) amount of communication load. In this work, we present a converse to such a general data exchange problem. The expression of the converse depends only on the number of bits to be moved between different subsets of nodes, and does not assume anything further specific about the parameters in the problem. Specific problem formulations, such as those in Coded Caching, Coded Data Shuffling, and Coded Distributed Computing, can be seen as instances of this generic data exchange problem. Applying our generic converse, we can efficiently recover known important converses in these formulations. Further, for a generic coded caching problem with heterogeneous cache sizes at the clients with or without a central server, we obtain a new general converse, which subsumes some existing results. Finally we relate a “centralized” version of our bound to the known generalized independence number bound in index coding and discuss our bound’s tightness in this context.

Abstract

This paper analyses patch antennas operating at 16 GHz on flexible Kapton polyimide substrate in various shapes, namely rectangular, circular, pentagon, hexagon, heptagon, and octagon. Their applications in medical areas, more particularly in wearable devices for E-health, are targeted. The bending effects of these antennas are studied, more specifically on gain, return loss, radiation characteristics, bandwidth, and beamwidth. A detailed comparison results showed that the rectangular patch had better performance even under the bending when the diameter of the surface is varied from 40mm to 120mm and maintains a gain of 4.8 dB at the given frequency. Under such extreme bending, the antennas operate satisfactorily with an efficiency of 43.424% to 47.41%

Abstract

Localization of multiple speakers using microphone arrays remains a challenging problem, especially in the presence of noise and reverberation. State-of-the-art localization algorithms generally exploit the sparsity of speech in some representation for this purpose. Whereas the broadband approaches exploit time-domain sparsity for multi-speaker localization, narrowband approaches can additionally exploit sparsity and disjointness in the time-frequency representation. Broadband approaches are robust to spatial aliasing but do not optimally exploit the frequency domain sparsity, leading to poor localization performance for arrays with short inter-microphone distances. Narrowband approaches, on the other hand, are vulnerable to spatial aliasing, making them unsuitable for arrays with large inter-microphone spacing. Proposed here is an approach that decomposes a signal spectrum into a weighted sum of broadband spectral components (atoms) and then exploits signal sparsity in the time-atom representation for simultaneous multiple source localization. The decomposition into atoms is performed in situ using non-negative

Abstract

Residential electricity consumption (REC) in India has tripled in the past two decades accounting for 24% of the overall electricity consumption during 2018-19. Residential air conditioning (AC) usage is responsible for about 20%-40% of REC in India. This paper investigates the relationship of residential AC use with indoor temperature and relative humidity (RH) using concurrent time-series monitoring data gathered in eight dwellings during summer and monsoon seasons. Contextual data about the dwelling (physical) and household characteristics were gathered using face-to-face interview based surveys. The dwellings were located in Hyderabad representing the composite climate of India. The mean daily electricity consumption was found to be higher in summer (11.5kWh) possibly due to the higher usage of AC (because of higher ambient conditions) as compared to 6.5kWh/day during monsoon season. Binary logistic regression identified the trigger indoor temperature and RH at which AC was likely to be switched on in the summer as 29 °C - 31.9 °C for indoor temperature and 36%-38.9% RH. In the monsoon season AC was predicted to come on sooner at 26°C-28.9°C but at higher RH range of 59%-61.9%. These empirical findings can be used to reduce residential cooling energy demand through smart management of ACs in Indian dwellings

Abstract

Conventional approaches for addressing road safety rely on manual interventions or immobile CCTV infras-tructure. Such methods are expensive in enforcing compliance to traffic rules and do not scale to large road networks. This paper proposes a simple mobile imaging setup to address several common problems in road safety at scale. We use recent computer vision techniques to identify possible irregularities on roads, the absence of street lights, and defective traffic signs using videos from a moving camera-mounted vehicle. Beyond the inspection of static road infrastructure, we also demonstrate the mobile imaging solution’s applicability to spot traffic violations. Before deploying our system in the real-world, we investigate the strengths and shortcomings of computer vi- sion techniques on thirteen condition-based hierarchical labels. These conditions include different timings, road type, traffic density, and state of road damage. Our demonstrations are then carried out on 2000 km of unconstrained road scenes, captured across an entire city. Through this, we quantitatively measure the overall safety of roads in the city through carefully constructed metrics. We also show an interactive dashboard for visually inspecting and initiating action in a time, labor and cost-efficient manner. Code, models, and datasets used in this work will be publicly released

Abstract

Quantum walks [1–6] are quantum analogs of random walks on graphs and have widespread applications in several areas of quantum information processing [7]. In particular, they are a universal model for quantum computation and are crucial to the design of quantum algorithms for a plethora of problems such as element distinctness [8,9], searching for a marked state in graphs [10–16], matrix product verification [17], triangle finding [18], group commutativity [19], and many others [20–24]. In fact, for some oracular problems, quantum walks exhibit an exponential speedup over their classical counterparts [21,22]. For several of these algorithms, the runtime strongly depends on the time required for the quantum walk on a graph to localize at a vertex inside a marked set of vertices of interest. In the context of classical random walks, the hitting time is defined as the expected time required to hit a marked vertex. As such, the quantum hitting time characterizes the running time of the underlying quantum algorithm [14,15,20,21,25,26]. For continuous-time quantum walk (CTQW), the Hamiltonian governing the dynamics of the walk encodes the (vertex or edge) connectivity of the underlying graph. The dynamics involves evolving some initial state according to this Hamiltonian for time T , followed by a measurement in the basis spanned by the vertices of the graph, thus finding a target state with a high probability. However, Hamiltonian evolutions are unitary, and therefore, unlike classical random walks, quantum walks never converge to a fixed state with time. One method to address this is to evolve the system by H

Abstract

We propose the ViNet architecture for audiovisual saliency prediction. ViNet is a fully convolutional encoder-decoder architecture. The encoder uses visual features from a network trained for action recognition, and the decoder infers a saliency map via trilinear interpolation and 3D convolutions, combining features from multiple hierarchies. The overall architecture of ViNet is conceptually simple; it is causal and runs in real-time (60 fps). ViNet does not use audio as input and still outperforms the state-of-the-art audio-visual saliency prediction models on nine different datasets (three visual-only and six audio-visual datasets). ViNet also surpasses human performance on the CC, SIM and AUC metrics for the AVE dataset, and to our knowledge, it is the first model to do so. We also explore a variation of ViNet architecture by augmenting audio features into the decoder. To our surprise, upon sufficient training, the network becomes agnostic to the input audio and provides the same output irrespective of the input. Interestingly, we also observe similar behaviour in the previous state-of-the-art models [1] for audio-visual saliency prediction. Our findings contrast with previous works on deep learning-based audio-visual saliency prediction, suggesting a clear avenue for future explorations incorporating audio in a more effective manner. The code and pre-trained models are

Abstract

The paper introduces the novel Modular Pipe Climber III with a Three-Output Open Differential (3-OOD) mechanism to eliminate slipping of the tracks due to the changing cross-sections of the pipe. This will be achieved in any orientation of the robot. Previous pipe climbers use three-wheel/track modules, each with an individual driving mechanism to achieve stable traversing. Slipping of tracks is prevalent in such robots when it encounters the pipe turns. Thus, active control of each module’s speed is employed to mitigate the slip, thereby requiring substantial control effort. The proposed pipe climber implements the 3-OOD to address this issue by allowing the robot to mechanically modulate the track speeds as it encounters a turn. The proposed 3-OOD is the first three-output differential to realize the functional abilities of a traditional two-output differential.

Abstract

long-term meditation intervention. MCI or mild AD patients underwent detailed clinical and neuropsychological assessment and were assigned into meditation or non-meditation groups. High resolution T1-weighted magnetic resonance images (MRI) were acquired at baseline and after 6 months. Longitudinal symmetrized percentage changes (SPC) in cortical thickness and gray matter volume were estimated. Left caudal middle frontal, left rostral middle frontal, left superior parietal, right lateral orbitofrontal, and right superior frontal cortices showed changes in both cortical thickness and gray matter volume; the left paracentral cortex showed changes in cortical thickness; the left lateral occipital, left superior frontal, left banks of the superior temporal sulcus (bankssts), and left medial orbitofrontal cortices showed changes in gray matter volume. All these areas exhibited significantly higher SPC values in meditators as compared to non-meditators. Conversely, the left lateral occipital, and right posterior cingulate cortices showed significantly lower SPC values for cortical thickness in the meditators. In hippocampal subfields analysis, we observed significantly higher SPC in gray matter volume of the left CA1, molecular layer HP, and CA3 with a trend for increased gray matter volume in most other areas. No significant changes were found for the hippocampal