Abstract

Greenhouse gases (GHGs) play an important role in controlling local air pollution as well as climate change. In this study, we retrieved column-averaged dry-air (X) mole fractions of carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) using a ground-based EM27/SUN Fourier transform infrared spectrometer (FTIR). The EM27/SUN spectrometers are widely in use in the COllaborative Carbon Column Observing Network (COCCON). The PROFFAST software provided by COCCON has been used to analyze the measured atmospheric solar absorption spectra. In this letter, the diurnal variation and the time series of daily averaged XCO2, XCH4, and XCO covering the period from December 2020 to May 2021 are analyzed. The maximum values of XCO2, XCH4, and XCO are observed to be 420.57 ppm, 1.93 ppm, and 170.40 ppb, respectively. Less diurnal but clear seasonal changes are observed during the study period. XCH4 and XCO from the Sentinel-5Precursor (S5P)/TROPOspheric Monitoring Instrument (TROPOMI) are compared against the EM27/SUN retrievals. The correlation coefficient for the EM27/SUN retrieved XCH4 and XCO, with the S5P/TROPOMI products, are 0.75 and 0.94, respectively

Abstract

Active Learning aims to solve the problem of alleviating labelling costs for large-scale datasets by selecting a subset of data to effectively train on. Deep Active Learning (DAL) techniques typically involve repeated training of a model for sample acquisition over the entire subset of labelled data available in each round. This can be prohibitively expensive to run in real-world scenarios with large and constantly growing data. Some work has been done to address this – notably, SelectionVia-Proxy (SVP) proposed the use of a separate, smaller “proxy” model for acquisition. We explore further optimizations to the standard DAL setup and propose CLActive: an optimization procedure that brings significant speedups which maintains a constant training time for the selection model across rounds and retains information from past rounds using Experience Replay. We demonstrate large improvements in total train-time compared to the fully-trained baselines and SVP. We achieve up to 89×, 7×, 61× speedups over the fully-trained baseline at 50% of dataset collection in CIFAR, Imagenet and Amazon Review datasets, respectively, with little accuracy loss. We also show that CLActive is robust against catastrophic forgetting in a challenging class-incremental active-learning setting. Overall, we believe that CLActive can effectively enable rapid prototyping and deployment of deep AL algorithms in real-world use cases across a variety of settings.

Abstract

A system and method generating an optimized medical image using a machine learning model are provided . The method includes ( i ) receiving one or more medical images , ( ii ) segmenting to generate a transformed medical image for detecting a plurality of target elements , ( iii ) displaying the transformed medical image , ( iv ) receiving markings and scribblings associated with scribble locations from a user , ( v ) identifying errors associated with an outline of a target element , ( vi ) computing a loss function for a location of pixels where the target element is located on the transformed medical image , ( vii ) modifying the pre - defined weights ( w ) to match the segmentation output and the determined target element ,( viii ) determining whether the segmentation output is matched with the target element and ( ix ) generating the optimized medical image if the segmentation output is matched with the determined target element

Abstract

—Smart vehicles-enabled intelligent transportation system (ITS) supports a wide range of applications, such as, but not limited to, traffic planning and management, collision avoidance alert system, automated road speed enforcement, electronic toll collection, and real-time parking management, to name a few. However, it suffers from various types of security and privacy issues due to insecure communication among the entities over public channels. Therefore, an efficient and lightweight security mechanism is essential to protect the data that is both at rest as well as in transit. To this direction, we propose a public blockchain-envisioned secure communication framework for ITS (in short, called PBSCF-ITS). The proposed PBSCFITS guarantees access control and key management among the vehicle to vehicle, vehicle to road side unit, and road side unit to cloud server. We analyze the security of PBSCF-ITS to prove its resilience against various types of possible attacks. Furthermore, the performance of PBSCF-ITS with other related competing schemes has been compared. The obtained results illustrate that PBSCF-ITS outperforms the existing ones. Additionally, the pragmatic study of PBSCF-ITS is conducted to check its influence on various network related performance parameters, like number of mined blocks and transactions per block.

Abstract

The time delay between signals received at two spatially separated microphones is estimated using the location of the peak in the cross-correlation sequence between the two signals. The estimated delay is usually expressed in integer multiple of the sampling interval. The robustness of the delay estimation is affected by the degradation of the signals due to waveform distortion, noise, reflections, and reverberation. The broadband nature of speech signals is exploited to derive multiple evidence for the time delay from each frequency component of the signal. This helps to reduce the effects of waveform distortion, as the cross-correlation is computed on individual components. The multiple evidence provides robustness in the estimation of the time delay. The accuracy of the estimated time delay depends on the error between the integer delay and the true fractional delay. Methods are proposed to estimate the fractional part of the sampling interval of the delay. Robustness of the proposed method of estimation of the fractional part of the delay is examined for different types and levels of additive noise, as well for reverberation. Experimental results are given for data collected in an actual live room

Abstract

The field of quantum technologies has made rapid strides in recent years, and is poised for significant breakthroughs in the coming years. Practical applications are expected to appear first in sensing and metrology, then in communications and simulations, then as feedback to foundations of quantum theory, and ultimately in computation. The essential features that contribute to these technologies are superposition, entanglement, squeezing and tunneling of quantum states. The theoretical foundation of the field is clear; laws of quantum mechanics are precisely known, and elementary hardware components work as predicted [1, 2]. The challenge is a large scale integration, say of 10 or more components. Quantum systems are highly sensitive to disturbances from the environment; even necessary controls and observations perturb them. The available, and upcoming, quantum devices are noisy, and techniques to bring down the environmental error rate are being intensively pursued. At the same time, it is necessary to come up with error-resilient system designs, as well as techniques that validate and verify the results. This era of noisy intermediate scale quantum systems has been labeled NISQ [

Abstract

Most of the popular explanations of the observed anomalies in the semileptonic B-meson decays involve TeV scale Leptoquarks (LQs). Among the various possible LQ models, two particular LQs – S1(3, 1, 1/3) and U1(3, 1, 2/3) seem to be most promising. Here, we use current LHC data to constrain the S 1(3 , 1 , 1 /3) and U 1(3 , 1 , 2 /3) parameter spaces relevant for the RD(∗) observables. We recast the latest ATLAS τ τ resonance search data to obtain new exclusion limits. For this purpose, we consider both resonant (pair and single productions) and nonresonant (t-channel LQ exchange) productions of these LQs at the LHC. For the limits, the most dominant contribution comes from the (destructive) interference of the non-resonant production with Standard Model backgrounds. The combined contributio

Abstract

The significance of negative quantum conditional entropy is underscored by its ubiquity in information processing tasks like dense coding, state merging and one-way entanglement distillation. Mere presence of entanglement is not sufficient for these tasks, a fact underpinned by the existence of entangled quantum states with non-negative conditional entropy. This stimulates one to go beyond the paradigm of entanglement theory and probe the hitherto unexplored area of operations influencing quantum conditional entropy. To this end, we introduce the class of A-unital channels. We characterize them and demonstrate that A-unital channels are to states having non-negative conditional entropy what separable channels are to separable states. We also show that A-unital channels form the largest class of channels that do not decrease the conditional entropy of any state, a relation analogous to unital channels and entropy. A comparative study between A-unital and other channels crucial to the resource theory of entanglement is done to further exemplify the distinction with entanglement theory. Non-local unitary operations can decrease conditional entropy to sub-zero levels. However, there exist states whose conditional entropy remains non-negative even under this action. Termed as states having absolute conditional entropy, they prove to be robust against non-local unitary action. In this work, the completely free operations for such states are also characterized. Finally, since von Neumann entropy is the mainspring of these discussions, the bounds on the conditional entropy of states having a given entropy are derived. The definition of A-unital channels naturally lends itself to a procedure for determining membership of channels in this class. Thus, we not only characterize completely free channels for quantum conditional entropy, but also show how resourceful channels can be detected.

Abstract

Standard Model Predictive Control (MPC) or trajectory optimization approaches perform only a local search to solve a complex non-convex optimization problem. As a result, they cannot capture the multi-modal characteristic of human driving. A global optimizer can be a potential solution but is computationally intractable in a real-time setting. In this paper, we present a real-time MPC capable of searching over different driving modalities. Our basic idea is simple: we run several goal-directed parallel trajectory optimizations and score the resulting trajectories based on user-defined meta cost functions. This allows us to perform a global search over several locally optimal motion plans. Although conceptually straightforward, realizing this idea in real-time with existing optimizers is highly challenging from technical and computational standpoints. With this motivation, we present a novel batch nonholonomic trajectory optimization whose underlying matrix algebra is easily parallelizable across problem instances and reduces to computing large batch matrix-vector products. This structure, in turn, is achieved by deriving a linearization-free multi-convex reformulation of the non-holonomic kinematics and collision avoidance constraints. We extensively validate our approach using both synthetic and real data sets (NGSIM) of traffic scenarios. We highlight how our algorithm automatically takes lane-change and overtaking decisions based on the defined meta cost function. Our batch optimizer achieves trajectories with lower meta cost, up to 6x faster than competing baselines.

Abstract

One of the main reasons for query model’s prominence in quantum complexity is the presence of concrete lower bounding techniques: polynomial method and adversary method. There have been considerable efforts to not just give lower bounds using these methods but even to compare and relate them. We explore the value of these bounds on quantum query complexity for the class of symmetric functions, arguably one of the most natural and basic set of Boolean functions. We show (using the recently introduced spectral sensitivity) that both these bounds (positive adversary and approximate degree) give the same value for every total symmetric Boolean function. We also look at the quantum query complexity of Gap Majority, a partial symmetric function. It has gained importance recently in regard to understanding the composition of randomized query complexity. We characterize the quantum query complexity of Gap Majority and show a lower bound on noisy randomized query complexity (Ben-David and Blais, FOCS 2020) in terms of quantum query complexity. In addition, we study how large certificate complexity and block sensitivity can be as compared to sensitivity (even up to constant factors) for symmetric functions. We show tight separations, i.e., give upper bound on possible separations and construct functions achieving the same.