Abstract

In this work, we develop upper bounds on key rates for device-independent quantum key distribution (DI-QKD) protocols and devices. We study the reduced cc-squashed entanglement and show that it is a convex functional. As a result, we show that the convex hull of the currently known bounds is a tighter upper bound on the device-independent key rates of the standard Clauser-Horne-Shimony-Holt (CHSH)- based protocol. We further provide tighter bounds for DI-QKD key rates achievable by any protocol applied to the CHSH-based device. This bound is based on reduced relative entropy of entanglement optimized over decompositions into local and nonlocal parts. In the dynamical scenario of quantum channels, we obtain upper bounds for device-independent private capacity for the CHSH-based protocols. We show that the device-independent private capacity for the CHSH-based protocols on depolarizing and erasure channels is limited by the secret key capacity of dephasing channels.

Abstract

Continuous-time quantum walks provide a natural framework to tackle the fundamental problem of finding a node among a set of marked nodes in a graph, known as spatial search. Whether spatial search by continuous-time quantum walk provides a quadratic advantage over classical random walks has been an outstanding problem. Thus far, this advantage is obtained only for specific graphs or when a single node of the underlying graph is marked. In this article, we provide a new continuous-time quantum walk search algorithm that completely resolves this: our algorithm can find a marked node in any graph with any number of marked nodes, in a time that is quadratically faster than classical random walks. The overall algorithm is quite simple, requiring time evolution of the quantum walk Hamiltonian followed by a projective measurement. A key component of our algorithm is a purely analog procedure to perform operations on a state of the form $e^{-tH^2}|\psi\rangle$, for a given Hamiltonian H: it only requires evolving H for time scaling as $\sqrt{t}$. This allows us to quadratically fast-forward the dynamics of a continuous-time classical random walk. The applications of our work thus go beyond the realm of quantum walks and can lead to new analog quantum algorithms for preparing ground states of Hamiltonians or solving optimization problems.

Abstract

This article proposes Convolutional Neural Network based Auto Encoder (CNN-AE) to predict location dependent rate and coverage probability of a network from its topology. We train the CNN utilising BS location data of India, Brazil, Germany and the USA and compare its performance with stochastic geometry (SG) based analytical models. In comparison to the best-fitted SG-based model, CNN-AE improves the coverage and rate prediction errors by a margin of as large as 40 and 25 percents respectively. As an application, we propose a low complexity, provably convergent algorithm that, using trained CNN-AE, can compute locations of new BSs that need to be deployed in a network in order to satisfy pre-defined spatially heterogeneous performance goals.

Abstract

The article presents a combined theoretical and experimental study attempting to show how Pd nanoparticles (NPs) loading onto SnO2 substrate improves the acetone gas sensing performance. Pristine nanostructured SnO2 and Pd nanoparticles (Pd NPs) loaded SnO2 substrates have been prepared, characterized, and their acetone sensing performances have been measured. Experimental measurements have shown that Pd NP loading onto SnO2 suppresses the interfering effects of ethanol, water vapors, etc., and enhances the acetone sensor response, reversibility, response/recovery speeds, and signal-to-noise ratio. Various parameters like the adsorption energy, HOMO–LUMO energy gap, charge distribution, polarizability change, electrophilicity index, global hardness, etc., of several model systems, have been computed by using DFT. The computed parameters have been correlated with the conductivity, local reactivity, sensor response and selectivity, response/recovery times, etc., of the systems to understand the molecular-level effects of the Pd NP loading onto the SnO2 on the gas sensing process.

Abstract

Neural models command state-of-the-art per- formance across NLP tasks, including ones involving ‘‘reasoning’’. Models claiming to reason about the evidence presented to them should attend to the correct parts of the input while avoiding spurious patterns therein, be self-consistent in their predictions across in- puts, and be immune to biases derived from their pre-training in a nuanced, context- sensitive fashion. Do the prevalent *BERT- family of models do so? In this paper, we study this question using the problem of rea- soning on tabular data. Tabular inputs are especially well-suited for the study—they ad- mit systematic probes targeting the properties listed above. Our experiments demonstrate that a RoBERTa-based model, representative of the current state-of-the-art, fails at reason- ing on the following counts: it (a) ignores relevant parts of the evidence, (b) is over- sensitive to annotation artifacts, and (c) relies on the knowledge encoded in the pre-trained language model rather than the evidence pre- sented in its tabular inputs. Finally, through inoculation experiments, we show that fine- tuning the model on perturbed data does not help it overcome the above challenges.

Abstract

The paper presents a seismic safety assessment of unreinforced masonry (URM) building using two approaches. The first approach uses the ‘Pier Analysis’ method, based on the concept of equivalent lateral stiffness, where in-plane and out-of-plane actions are considered independently. The second approach is developed with the program SAP2000, where the linear response is evaluated using continuum ‘finite element modelling’ (FEM). Both methods are compared to evaluate the safety of wall piers and the differences in the outcomes under combined gravitational and lateral seismic forces. The analysis results showed that few wall elements are unsafe in in-plane and out-of-plane tension. It is also observed that the pier analysis method is conservative compared to FEM, but can be used as a simplified and quick tool in design offices for safety assessment, with reasonable accuracy. To safeguard the URM wall piers under lateral loads, a retrofitting technique is adopted by providing vertical and horizontal belts called splints and bandages, respectively, using welded wire mesh (WWM) reinforcement. The study using the ‘Pier Analysis’ shows that the lateral load capacity of unsafe URM piers can be enhanced up to 3.67 times and made safe using the applied retrofitting technique. Further, the retrofitting design methodology and recommendations for application procedures to on-site URM buildings are discussed in detail.

Abstract

Visual data obtained during driving scenarios usually con-tain large amounts of text that conveys semantic information necessary to analyse the urban environment and is integral to the traffic control plan. Yet, research on autonomous driving or driver assistance systems typically ignores this information. To advance research in this direction, we present RoadText-3K, a large driving video dataset with fully anno-tated text. RoadText-3K is three times bigger than its predecessor and contains data from varied geographical locations, unconstrained driving conditions and multiple languages and scripts. We offer a comprehensive analysis of tracking by detection and detection by tracking methods ex-ploring the limits of state-of-the-art text detection. Finally, we propose a new end-to-end trainable tracking model that yields state-of-the-art results on this challenging dataset. Our experiments demonstrate the complexity and variability of RoadText-3K and establish a new, realistic benchmark for scene text tracking in the wild.

Abstract

Bluetooth technology has enabled short-range wireless communication for billions of devices. Bluetooth Low-Energy (BLE) variant aims at improving power consumption on battery-constrained devices. BLE-enabled devices broadcast information (e.g., as beacons) to nearby devices via advertisements. Unfortunately, such functionality can become a double-edged sword at the hands of attackers. In this paper, we primarily show how an attacker can exploit BLE advertisements to exfiltrate information from BLE-enable devices. In particular, our attack establishes a communication medium between two devices without requiring any prior authentication or pairing. We develop a proof-of-concept attack framework on the Android ecosystem and assess its performance via a thorough set of experiments. Our results indicate that such an exfiltration attack is indeed possible though with a limited data rate. Nevertheless, we also demonstrate potential use cases and enhancements to our attack that can further its severeness. Finally, we discuss possible countermeasures to prevent such an attack.

Abstract

AI is adept at using large quantities of data, sometimes sensitive personal data, and can adversely affect individuals’ privacy. Data privacy concerns significantly impact the course of next-generation AI. Users do not trust anyone withholding their data and need privacy-preserving intelligent systems. In addition, several regulations mandate that organizations handle users’ data in ways that do not affect their privacy and provide them control on their data. Federated Learning emerged as a privacy-preserving technology for data-intensive machine learning by training the models on-site or on-device. However, several concerns related to federated learning emerged due to: (i) dynamic, distributed, heterogeneous, and collaborative nature of client devices, (ii) membership inference and model inversion attacks affecting the overall privacy and security of FL systems, (iii) the need for strict compliance to data privacy and protection laws, (iv) the vulnerabilities at local client devices leading to data leakage, and (iv) diversity and ubiquity of smart devices collecting real-time multimodal data leading to lack of standardization efforts for security and privacy management framework. In this paper, we discuss (a) how federated learning can help us withholding privacy, (b) the need for improving security and privacy in federated learning systems, (c) the privacy regulations and their application to federated learning in various business domains, (d) proposed a federated recommender system and demonstrated the performance that matches the central setting.

Abstract

The exact determination of settlement and the time required to complete that settlement is very important from the safety point of view. There are several methods available to find the rate of consolidation that is highly dependent on the settlement versus time curve. Here, it has been attempted to identify the suitable method for the determination of the rate of consolidation of coal ash. After analyzing the value of the coefficient of consolidation (Cv) estimated from various methods, the rectangular hyperbola method (RH method) is found to be in best linear correlation between the measured Cv and calculated Cv. The coal ash shows an increment in Cv with the increase in effective pressure. The Cv obtained from the RH method is higher than the Cv obtained from Taylor’s and Casagrande method that helps in close approximation with field Cv. The precise determination of Cv helps in better prediction of the time required for the consolidation settlement that will increase the performance of the structure.