Abstract

Biological processes like growth, aging, and disease progression are generally studied with follow-up scans taken at different time points, i.e., with image time series (TS) based analysis. Comparison between TS representing a biological process of two individuals/populations is of interest. A metric to quantify the difference between TS is desirable for such a comparison. The two TS represent the evolution of two different subject/population average anatomies through two paths. A method to untangle and quantify the path and inter-subject anatomy(shape) difference between the TS is presented in this paper. The proposed metric is a generalized version of Fréchet distance designed to compare curves. The proposed method is evaluated with simulated and adult and fetal neuro templates. Results show that the metric is able to separate and quantify the path and shape differences between TS. Keywords: Image TS · Time-dependant variation · Time dependant variation.

Abstract

Unconstrained Asian roads often involve poor infrastructure, affecting overall road safety. Missing traffic signs are a regular part of such roads. Missing or non-existing object detection has been studied for locating missing curbs and estimating reasonable regions for pedestrians on road scene images. Such methods involve analyzing task-specific single object cues. In this paper, we present the first and most challenging video dataset for missing objects, with multiple types of traffic signs for which the cues are visible without the signs in the scenes. We refer to it as the Missing Traffic Signs Video Dataset (MTSVD). MTSVD is challenging compared to the previous works in two aspects i) The traffic signs are generally not present in the vicinity of their cues, ii) The traffic signs cues are diverse and unique. Also, MTSVD is the first publicly available missing object dataset. To train the models for identifying missing signs, we complement our dataset with 10K traffic sign tracks, with 40 percent of the traffic signs having cues visible in the scenes. For identifying missing signs, we propose the Cue-driven Contextual Attention units (CueCAn), which we incorporate in our model encoder. We first train the encoder to classify the presence of traffic sign cues and then train the entire segmentation model end-to-end to localize missing traffic signs. Quantitative and qualitative analysis shows that CueCAn significantly improves the performance of base models.

Abstract

Hardware accelerators are being designed to offload compute-intensive tasks such as deep neural networks from the CPU to improve the overall performance of an application, specifically on the performance-per-watt metric. Encoder-decoderbased sequence-to-sequence models such as the Transformer model have demonstrated state-of-the-art results in end-to-end automatic speech recognition systems (ASRs). The Transformer model being intensive on memory and computation poses a challenge for an FPGA implementation. This paper proposes an end-to-end architecture to accelerate a Transformer for an ASR system. The host CPU orchestrates the computations from different encoder and decoder stages of the Transformer architecture on the designed hardware accelerator with no necessity for intervening FPGA reconfiguration. The communication latency is hidden by prefetching the weights of the next encoder/decoder block while the current block is being processed. The computation is split across both the Super Logic Regions (SLRs) of the FPGA, mitigating the inter-SLR communication. The proposed design presents an optimal latency, exploiting the available resources. The accelerator design is realized using highlevel synthesis tools and evaluated on an Alveo U-50 FPGA card. The design demonstrates an average speed-up of 32× compared to an Intel Xeon E5-2640 CPU and 8.8× compared to NVIDIA GeForce RTX 3080 Ti Graphics card for a 32-bit floating point single precision model. Index Terms—Automatic speech recognition, Hardware accelerator, Transformer, FPGAs.

Abstract

Efficient certification and quantification of high dimensional entanglement of composite systems are challenging both theoretically as well as experimentally. Here, we demonstrate how to measure the linear entropy, negativity and the Schmidt number of bipartite systems from the visibility of Mach–Zehnder interferometer using single copies of the quantum state. Our result shows that for any two qubit pure bipartite state, the interference visibility is a direct measure of entanglement. We also propose how to measure the mutual predictability experimentally from the intensity patterns of the interferometric set-up without having to resort to local measurements of mutually unbiased bases. Furthermore, we show that the entanglement witness operator can be measured in a interference setup and the phase shift is sensitive to the separable or entangled nature of the state. Our proposal bring out the power of Interferometric set-up in entanglement detection of pure and several mixed states which paves the way towards design of entanglement meter.

Abstract

In order to advance academic research, it is important to assess and evaluate the academic influence of researchers and the findings they produce. Citation metrics are universally used methods to evaluate researchers. Amongst the several variations of citation metrics, the h-index proposed by Hirsch has become the leading measure. Recent work shows that h-index is not an effective measure to determine scientific impact - due to changing authorship patterns. This can be mitigated by using h-index of a paper to compute hindex of an author. We show that using fractional allocation of h-index gives better results. In this work, we reapply two indices based on the h-index of a single paper. The indices are referred to as: hp-index and hp-frac-index. We run large-scale experiments in three different fields with about a million publications and 3,000 authors. Our experiments show that hp-frac-index provides a unique ranking when compared to h-index. It also performs better than h-index in providing higher ranks to the awarded researcher.

Abstract

Electric vehicles (EVs) represent the long-term green substitute for traditional fuel-based vehicles. To encourage EV adoption, the trust of the end-users must be assured. In this work, we focus on a recently emerging privacy threat of profiling and identifying EVs via the analog electrical data exchanged during the EV charging process. The core focus of our work is to investigate the feasibility of such a threat at scale. To this end, we first propose an improved EV profiling approach that outperforms the state-of-the-art EV profiling techniques. Next, we exhaustively evaluate the performance of our improved approach to profile EVs in real-world settings. In our evaluations, we conduct a series of experiments including 25032 charging sessions from 530 real EVs, sub-sampled datasets with different data distributions, etc. Our results show that even with our improved approach, profiling and individually identifying the growing number of EVs appear extremely difficult in practice; at least with the analog charging data utilized throughout the literature. We believe that our findings from this work will further foster the trust of potential users in the EV ecosystem, and consequently, encourage EV adoption.

Abstract

Password managers (PMs) are becoming increasingly popular on mobile devices, especially on small-screen devices, mainly due to the convenience of automatically filling credentials into login forms. Modern mobile OSes advocate for system-wide autofill frameworks to support autofilling on browsers as well as other apps. Mobile OSes also empower apps to directly render web content within WebView controls without redirecting users to the main browser. par We present a novel technique, called AutoSpill, to leak users' saved credentials during an autofill operation on a webpage loaded into an app's WebView. AutoSpill conveniently dodges the secure autofill process. The majority of popular Android PMs considered in our experiments were found vulnerable to AutoSpill; even when the app hosting the WebView is not actively participating in the leak. Android intermediates in the autofill process because of its app sandboxing. Hence, the responsibility for any credential leakage is often stranded between PMs and the Android system. We investigate the root causes of AutoSpill and propose countermeasures to fundamentally fix AutoSpill for both the parties. We responsibly disclosed our findings to the affected PMs and Android security team.

Abstract

Linear regression is a widely used technique to fit linear models and finds widespread applications across different areas such as machine learning and statistics. In most real-world scenarios, however, linear regression problems are often ill-posed or the underlying model suffers from overfitting, leading to erroneous or trivial solutions. This is often dealt with by adding extra constraints, known as regularization. In this paper, we use the frameworks of block-encoding and quantum singular value transformation (QSVT) to design the first quantum algorithms for quantum least squares with general ℓ2-regularization. These include regularized versions of quantum ordinary least squares, quantum weighted least squares, and quantum generalized least squares. Our quantum algorithms substantially improve upon prior results on quantum ridge regression (polynomial improvement in the condition number and an exponential improvement in accuracy), which is a particular case of our result. To this end, we assume approximate block-encodings of the underlying matrices as input and use robust QSVT algorithms for various linear algebra operations. In particular, we develop a variable-time quantum algorithm for matrix inversion using QSVT, where we use quantum eigenvalue discrimination as a subroutine instead of gapped phase estimation. This ensures that substantially fewer ancilla qubits are required for this procedure than prior results. Owing to the generality of the block-encoding framework, our algorithms are applicable to a variety of input models and can also be seen as improved and generalized versions of prior results on standard (non-regularized) quantum least squares algorithms.

Abstract

The management and regulation of the waste generated from coal-based power plants have been a key issue throughout the world. There is a need for the voluminous utilization of coal ash in India. The bulk utilization of the industrial waste materials like coal ash can control the exploitation of the natural resources like soil in diferent aspects. Here, it has been attempted to evaluate the potential use of coal ash as a foundation/fll material. In addition, physical, geotechnical, morphological, and chemical characterization of the pond ash has been done. Then, fnite element modeling of the vibratory roller compaction was simulated using PLAXIS3D software for the recommendation of the pond ash as a construction material in place of soil. Also, the reduction in CO2 emission with pond ash was compared with that of the soil. The reduction in CO2 emission was found to be about 15% considering several factors like distance from the source station to the test site, types of coal ash used, types of construction equipment employed, etc. The utilization of pond ash as fll material was found to be economical, and its utilization saves around INR 32 to INR 399 per cubic meter. Keywords Coal ash · Foundation/fll material · Finite element modeling · Vibratory roller compaction · CO2 emission

Abstract

In this paper, we present online algorithm called {it Delaytron} for learning multi class classifiers using delayed bandit feedbacks. The sequence of feedback delays ${d_t}_{t=1}^T$ is unknown to the algorithm. At the $t$-th round, the algorithm observes an example $mathbf{x}_t$ and predicts a label $tilde{y}_t$ and receives the bandit feedback $mathbb{I}[tilde{y}_t=y_t]$ only $d_t$ rounds later. When $t+d_t>T$, we consider that the feedback for the $t$-th round is missing. We show that the proposed algorithm achieves regret of $mathcal{O}left(sqrt{frac{2 K}{gamma}left[frac{T}{2}+left(2+frac{L^2}{R^2Vert WVert_F^2}right)sum_{t=1}^Td_tright]}right)$ when the loss for each missing sample is upper bounded by $L$. In the case when the loss for missing samples is not upper bounded, the regret achieved by Delaytron is $mathcal{O}left(sqrt{frac{2 K}{gamma}left[frac{T}{2}+2sum_{t=1}^Td_t+vert mathcal{M}vert Tright]}right)$ where $mathcal{M}$ is the set of missing samples in $T$ rounds. These bounds were achieved with a constant step size which requires the knowledge of $T$ and $sum_{t=1}^Td_t$. For the case when $T$ and $sum_{t=1}^Td_t$ are unknown, we use a doubling trick for online learning and proposed Adaptive Delaytron. We show that Adaptive Delaytron achieves a regret bound of $mathcal{O}left(sqrt{T+sum_{t=1}^Td_t}right)$. We experimentally show that the proposed approach can learn efficient classifiers even with delayed bandit feedbacks and the accuracy does not degrade much due to delays in feedbacks.