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Abstract

Despite existing works of Audio-Visual Saliency Prediction (AVSP) models claiming to achieve promising results by fusing audio modality over visual-only models, these models fail to leverage audio information. In this paper, we investigate the relevance of audio cues in conjunction with the visual ones and conduct extensive analysis by employing well-established audio modules and fusion techniques from diverse correlated audio-visual tasks. Our analysis on ten diverse saliency datasets suggests that none of the methods worked for incorporating audio. Furthermore, we bring to light, why AVSP models show a gain in performance over visual-only models, though the audio branch is agnostic at inference. Our work questions the role of audio in current deep AVSP models and motivates the community to a clear avenue for reconsideration of the complex architectures by demonstrating that simpler alternatives work equally well.

Abstract

Automatic segmentation of retinal blood vessels from fundus images plays an important role in the computer aided diagnosis of retinal diseases. The task of blood vessel segmentation is challenging due to the extreme variations in morphology of the vessels against noisy background. In this paper, we formulate the segmentation task as a multi-label inference task and utilize the implicit advantages of the combination of convolutional neural networks and structured prediction. Our proposed convolutional neural network based model achieves strong performance and significantly outperforms the state-of-the-art for automatic retinal blood vessel segmentation on DRIVE dataset with 95.33% accuracy and 0.974 AUC score

Abstract

An autonomous broker that liaises between retail customers and power-generating companies (GenCos) is essential for the smart grid ecosystem. The effciency brought in by such brokers to the smart grid setup can be studied through a well-developed simulation environment. In this paper, we describe the design of one such intelligent energy broker called VidyutVanika21 (VV21) and analyze its performance using a simulation platform called PowerTAC (Power Trading Agent Competition). Specifcally, we discuss the retail (VV21–RM) and wholesale market (VV21–WM) modules of VV21 that help the broker achieve high net profts in a competitive setup. Supported by game-theoretic analysis, the VV21–RM designs tariff contracts that a) maintain a balanced portfolio of different types of customers; b) sustain an appropriate level of market share, and c) introduce surcharges on customers to reduce energy usage during peak demand times. The VV21–WM aims to reduce the cost of procurement by following the supply curve of the GenCo to identify its lowest ask for each auction which is then used to generate suitable bids. We further demonstrate the effcacy of the retail and wholesale strategies of VV21 in PowerTAC 2021 fnals and through several controlled experiments.

Abstract

Moving target defense (MTD) has emerged as a key technique that can be used in various security applications to reduce the threat of attackers by taking away their ability to perform reconnaissance and exploit vulnerabilities. However, most of the existing research in the field assumes unrealistic access to information about the attacker’s motivations and/or actions when developing MTD strategies. Many of the existing approaches also assume complete knowledge regarding the vulnerabilities of a particular application and how each of these vulnerabilities can be exploited by an attacker. In this work, we propose an algorithm that generates effective MTD strategies for web applications that does not rely on prior knowledge about the attackers. Our approach assumes that the only information the defender receives about its own reward function, is via interaction with the attacker in a repeated game setting. We evaluate our algorithm using data which is mined from the National Vulnerability Database to show that it matches the performance of the state of the art techniques, despite using much less information.

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Transformers, which were introduced for solving the task of machine translation, have expanded their utility in multiple domains. A recent application of transformers is in solving elementary mathematics problems. In this paper, we use a hybrid tokenization technique for encoding the mathematics and science problems and answers, which is used to train the transformer. We compare the performance of our tokenization with that of the char-to-char tokenzation in solving various types of mathematics and science problems. We discuss the accuracy, memory usage, and time to train the model with proposed tokenization. The proposed tokenization shows higher accuracy for some problems, and requires lesser memory compared to char-to-char tokenization. We propose an extended dataset of science and mathematics problems that consists of billions of samples in questionanswer format in raw text. Code and Dataset: https: //github.com/misterpawan/scimat2

Abstract

The possibility of a vectorlike top partner decaying to a new colorless weak-singlet scalar or pseudoscalar has attracted some attention in the literature. We investigate the production of a weaksinglet charge-2=3 T quark that can decay to a spinless boson (Φ) and a top quark at the LHC. Earlier [A. Bhardwaj et al., Roadmap to explore vectorlike quarks decaying to a new scalar or pseudoscalar, arXiv:2203.13753.], we showed that in a large part of the parameter space, the T → tΦ and the loopinduced Φ → gg decays become the dominant decay modes for these particles. Here, we investigate the discovery prospects of the T quark in this region through the above decays. In particular, we focus on the pp → TT → ðtΦÞðtΦÞ → ðtðggÞÞðtðggÞÞ channel. Separating this signal from the huge Standard Model background is a challenging task, forcing us to employ a multivariate machine-learning technique. We find that the above channel can be a discovery channel of the top partner in the large part of the parameter space where the above decay chain dominates. Our analysis is largely model independent, and hence our results would be useful in a broad class of new physics models.

Abstract

In the left-right symmetric models, a heavy charged gauge boson W0 can decay to a lepton and a righthanded neutrino (RHN). If the neutrino masses are generated through the standard type-I seesaw mechanism, the Yukawa couplings controlling two-body decays of the RHN become very small. As a result, the RHN decays to another lepton and a pair of jets via an off-shell W0 . This is the basis of the Keung-Senjanović (KS) process, which was originally proposed as a probe of lepton number violation at the LHC. However, if a different mechanism like the inverse seesaw generates the neutrino masses, a TeV-scale RHN can have large Yukawa couplings and hence dominantly decay to a lepton and a W boson, leading to a kinematically different process from the KS one. We investigate the prospect of this unexplored process as a probe of the inverse seesaw mechanism in the left-right symmetric models at the High Luminosity LHC (HL-LHC). Our signal arises from the Drell-Yan production of a W0 and leads to two high-pT same-flavour-opposite-sign leptons and a boosted W-like fatjet in the final state. We find that a sequential W0 with mass up to ∼6 TeV along with a TeV-scale RHN can be discovered at the HL-LHC.

Abstract

At the LHC, the gluon-initiated processes are considered to be the primary source of di-Higgs production. However, in the presence of a new resonance, the light-quark initiated processes can also contribute significantly. In this letter, we look at the di-Higgs production mediated by a new singlet scalar. The singlet is produced in both quark-antiquark and gluon fusion processes through loops involving a scalar leptoquark and right-handed neutrinos. With benchmark parameters inspired from the recent resonant di-Higgs searches by the ATLAS collaboration, we examine the prospects of such a resonance in the TeVrange at the High-Luminosity LHC (HL-LHC) in the b¯ bτ +τ − mode with a multivariate analysis. We obtain the 5σ and 2σ contours and find that a significant part of the parameter space is within the reach of the HL-LHC.

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