Abstract

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Abstract

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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

Face possesses a rich spatial structure that can provide valuable cues to guide various face-related tasks. The eyes are considered an important sociovisual cue for effective communication. They are an integral feature of facial expressions as they are an important aspect of interpersonal communication. However, virtual reality headsets occlude a significant portion of the face and restrict the visibility of certain facial features, particularly the eye region. Reproducing this region with realistic content and handling complex eye movements such as blinks is challenging. Previous facial inpainting methods are not capable enough to capture subtle eye movements. In view of this, we propose a working solution to refine the reconstructions, particularly around the eye region, by leveraging inherent eye structure. We introduce spatial supervision and a novel landmark predictor module to regularize per-frame reconstructions obtained from an existing image-based facial de-occlusion network. experiments verify the usefulness of our approach in enhancing the quality of reconstructions to capture subtle eye movements

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

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease. Aberrant production and aggregation of amyloid beta (Ab) peptide into plaques is a frequent feature of AD, but therapeutic approaches targeting Ab accumulation fail to inhibit disease progression. The approved cholinesterase inhibitor drugs are symptomatic treatments. During human brain development, the progenitor cells differentiate into neurons and switch to a postmitotic state. However, cell cycle re-entry often precedes loss of neurons. We developed mathematical models of multiple routes leading to cell cycle re-entry in neurons that incorporate the crosstalk between cell cycle, neuronal, and apoptotic signaling mechanisms. We show that the integration of multiple feedback loops influences disease severity making the switch to pathological state irreversible. We observe that the transcriptional changes associated with this transition are also character- istics of the AD brain. We propose that targeting multiple arms of the feedback loop may bring about disease-modifying effects in AD

Abstract

Threshold signature schemes allow any qualified subset of participants (t-out-of-n) to combine its shares and generate a signature that can be verified using a single threshold public key. While there are several existing threshold signature schemes, most are either n-out-of-n and/or require consistent availability of the exact same set of participants through several rounds. This can result in signer availability becoming a bottleneck in the signing process. Our threshold signature scheme removes this dependence by introducing truly threshold asynchronous signatures, i.e., once the message to be signed has been revealed, the signers simply sign and broadcast their signature. Our scheme also uses misbehaviour detection to impose accountability for invalid signing. We prove that our scheme is safe against known distributed attacks and is EUF−CMA secure in the Random Oracle Model for up to t−1 malicious participants.

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.