Miracle-dz/UniFAR-FTUs huggingface dataset:
Future Development of Quantum Computing and Its Relevance to NATO

: The first quantum computers are becoming a reality, and scientists working in various areas look forward to taking advantage of their enormous computational potential. At the same time, the high performance of quantum computers imposes serious risks for cybersecurity. We can expect an arms race between rival parties: a defensive side trying to ensure the privacy and dependability of stored and transmitted information and their adversaries. With this article, the authors aim to provide an overview of the status of quantum computer development, project the next steps, and investigate the impact future quantum systems may have on cybersecurity and military operations. We first discuss the basic aspects that differentiate quantum computing from classical computing and find that analogies between both domains are quite limited. The world of quantum computers is remarkably diverse already, and we elaborate that quantum simulators and universal quantum computers have “ qubits ” in common but still work in fundamentally different ways. Since security experts focus on upcoming trends in quantum computing, we take a look at the latest technologies and at the race for first reaching “ quantum suprem-acy. ” Finally, we provide a detailed analysis of the specific risks future quantum computers represent for established cryptosystems and conclude that asymmetric algorithms like the RSA protocol are particularly vulnerable. The dangers of quantum computing for cryptography are obvious, as is the high relevance of the safety of
Source apportionment of VOCs, IVOCs and SVOCs by positive matrix factorization in suburban Livermore, California

Abstract. Gas- and particle-phase molecular markers provide highly specific information about the sources and atmospheric processes that contribute to air pollution. In urban areas, major sources of pollution are changing as regulation selectively mitigates some pollution sources and climate change impacts the surrounding environment. In this study, a comprehensive thermal desorption aerosol gas chromatograph (cTAG) was used to measure volatile, intermediate-volatility and semivolatile molecular markers every other hour over a 10 d period from 11 to 21 April 2018 in suburban Livermore, California. Source apportionment via positive matrix factorization (PMF) was performed to identify major sources of pollution. The PMF analysis identified 13 components, including emissions from gasoline, consumer products, biomass burning, secondary oxidation, aged regional transport and several factors associated with single compounds or specific events with unique compositions. The gasoline factor had a distinct morning peak in concentration but lacked a corresponding evening peak, suggesting commute-related traffic emissions are dominated by cold starts in residential areas. More monoterpene and monoterpenoid mass was assigned to consumer product emissions than biogenic sources, underscoring the increasing importance of volatile chemical products to urban emissions. Daytime isoprene concentrations were controlled by biogenic sunlight- and temperature-dependent processes, mediated by strong midday mixing, but gasoline was found to be the dominant and likely only source of isoprene at night. Biomass burning markers indicated residential wood burning activity remained an important pollution source even in the springtime. This study demonstrates that specific high-time-resolution molecular marker measurements across a wide range of volatility enable more comprehensive pollution source profiles than a narrower volatility range would allow.
Audio- and text-based Podcast Retrieval and Summarization Master’s Thesis

This thesis proposes approaches for two fields in the domain of podcasts, reranking topical retrieval results of fixed two-minute podcast segments and automatic textual summarization of podcast episodes. Regarding segment retrieval, we show the effect of the additional usage of audio data in contrast to only a transcription of the podcast. Therefore, we propose and compare multiple approaches for the re-ranking of podcast segments based on three criteria. The criteria are whether a segment is entertaining, whether it contains opinions and whether it contains discussion. One approach utilizes only text data, another approach uses only audio data and a third approach incorporates a combination of both. Two approaches are proposed for summarization. One approach produces abstractive summaries and utilizes a DistilBART summarization model. The other approach produces extractive summaries and is based on the TextRank algorithm. Summaries by both systems are generated by prioritizing entertaining segments of the podcast. Both approaches generate short text summaries for podcast episodes intended to arouse interest of potential listeners.
Extraction of hemicellulose from oil palm biomass using subcritical water extraction to produce value-added product or fine chemicals

Renewable energy sources such as wind, hydro, solar, hydrogen, nuclear and biomass are various type of alternatives energy available to replace non-renewable fossil fuels. The total oil palm biomass produced by Malaysia alone is about 53.04 million tonnes (mt), which include 17.08 mt of empty fruit bunches, 9.66 mt of fibre, 5.2 mt of shell, 12.9 and 8.2 mt of fronds and trunks. This research will focused on the production of value-added product or fine chemicals based on extraction of xylose and xylooligosaccharides from hemicellulose of lignocellulosic biomass. As preliminary results, the pH value, TOC value and total sugar concentration were determined.
PRIVEE: A Visual Analytic Workflow for Proactive Privacy Risk Inspection of Open Data

Open data sets that contain personal information are susceptible to adversarial attacks even when anonymized. By performing low-cost joins on multiple datasets with shared attributes, malicious users of open data portals might get access to information that violates individuals’ privacy. However, open data sets are primarily published using a release-and-forget model, whereby data owners and custodians have little to no cognizance of these privacy risks. We address this critical gap by developing a visual analytic solution that enables data defenders to gain awareness about the disclosure risks in local, joinable data neighborhoods. The solution is derived through a design study with data privacy researchers, where we initially play the role of a red team and engage in an ethical data hacking exercise based on privacy attack scenarios. We use this problem and domain characterization to develop a set of visual analytic interventions as a defense mechanism and realize them in PRIVEE, a visual risk inspection workflow that acts as a proactive monitor for data defenders. PRIVEE uses a combination of risk scores and associated interactive visualizations to let data defenders explore vulnerable joins and interpret risks at multiple levels of data granularity. We demonstrate how PRIVEE can help emulate the attack strategies and diagnose disclosure risks through two case studies with data privacy experts.
Court Judgement Labeling Using Topic Modeling and Syntactic Parsing

In regions that practice common law, relevant historical cases are essential references for sentencing. To help legal practitioners ﬁnd previous judgement easier, this paper aims to label each court judgement by some tags. These tags are legally important to summarize the judgement and can guide the user to similar judgements. We introduce a heuristic system to solve the problem, which starts from Aspect-driven Topic Modeling and uses Dependency Parsing and Constituency Parsing for phrase generation. We also construct a legal term tree for Hong Kong and implemented a sentence simpliﬁcation module to support the system. Finally, we propose a similar document recommendation algorithm based on the generated tags. It enables users to ﬁnd similar documents based on a few selected aspects rather than the whole passage. Experiment results show that this system is the best approach for this speciﬁc task. It is better than simple term extraction method in terms of summarizing the document, and the recommendation algorithm is more eﬀective than full-text comparison approaches. We believe that the system has huge potential in law as well as in other areas.
Transformer-based Value Function Decomposition for Cooperative Multi-agent Reinforcement Learning in StarCraft

The StarCraft II Multi-Agent Challenge (SMAC) was created to be a challenging benchmark problem for cooperative multi-agent reinforcement learning (MARL). SMAC focuses exclusively on the problem of StarCraft micromanagement and assumes that each unit is controlled individually by a learning agent that acts independently and only possesses local information; centralized training is assumed to occur with decentralized execution (CTDE). To perform well in SMAC, MARL algorithms must handle the dual problems of multi-agent credit assignment and joint action evaluation. This paper introduces a new architecture TransMix, a transformer-based joint action-value mixing network which we show to be efficient and scalable as compared to the other state-of-the-art cooperative MARL solutions. TransMix leverages the ability of transformers to learn a richer mixing function for combining the agents' individual value functions. It achieves comparable performance to previous work on easy SMAC scenarios and outperforms other techniques on hard scenarios, as well as scenarios that are corrupted with Gaussian noise to simulate fog of war.
DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities

Reinforcement learning (RL) techniques have shown great success in many challenging quantitative trading tasks, such as portfolio management and algorithmic trading. Especially, intraday trading is one of the most profitable and risky tasks because of the intraday behaviors of the financial market that reflect billions of rapidly fluctuating capitals. However, a vast majority of existing RL methods focus on the relatively low frequency trading scenarios (e.g., day-level) and fail to capture the fleeting intraday investment opportunities due to two major challenges: 1) how to effectively train profitable RL agents for intraday investment decision-making, which involves high-dimensional fine-grained action space; 2) how to learn meaningful multi-modality market representation to understand the intraday behaviors of the financial market at tick-level. Motivated by the efficient workflow of professional human intraday traders, we propose DeepScalper, a deep reinforcement learning framework for intraday trading to tackle the above challenges. Specifically, DeepScalper includes four components: 1) a dueling Q-network with action branching to deal with the large action space of intraday trading for efficient RL optimization; 2) a novel reward function with a hindsight bonus to encourage RL agents making trading decisions with a long-term horizon of the entire trading day; 3) an encoder-decoder architecture to learn multi-modality temporal market embedding, which incorporates both macro-level and micro-level market information; 4) a risk-aware auxiliary task to maintain a striking balance between maximizing profit and minimizing risk. Through extensive experiments on real-world market data spanning over three years on six financial futures (2 stock index and 4 treasury bond), we demonstrate that DeepScalper significantly outperforms many state-of-the-art baselines in terms of four financial criteria. Furthermore, we conduct a series of exploratory and ablative studies to analyze the contributions of each component in DeepScalper.
Proximal Survival Analysis to Handle Dependent Right Censoring

Many epidemiological and clinical studies aim at analyzing a time-to-event endpoint. A common complication is right censoring. In some cases, it arises because subjects are still surviving after the study terminates or move out of the study area, in which case right censoring is typically treated as independent or non-informative. Such an assumption can be further relaxed to conditional independent censoring by leveraging possibly time-varying covariate information, if available, assuming censoring and failure time are independent among covariate strata. In yet other instances, events may be censored by other competing events like death and are associated with censoring possibly through prognoses. Realistically, measured covariates can rarely capture all such associations with certainty. For such dependent censoring, often covariate measurements are at best proxies of underlying prognoses. In this paper, we establish a nonparametric identification framework by formally admitting that conditional independent censoring may fail in practice and accounting for covariate measurements as imperfect proxies of underlying association. The framework suggests adaptive estimators which we give generic assumptions under which they are consistent, asymptotically normal, and doubly robust. We illustrate our framework with concrete settings, where we examine the finite-sample performance of our proposed estimators via a Monte-Carlo simulation and apply them to the SEER-Medicare dataset.
Sensitivity to Unobserved Confounding in Studies with Factor-structured Outcomes

In this work, we propose an approach for assessing sensitivity to unobserved confounding in studies with multiple outcomes. We demonstrate how prior knowledge unique to the multi-outcome setting can be leveraged to strengthen causal conclusions beyond what can be achieved from analyzing individual outcomes in isolation. We argue that it is often reasonable to make a shared confounding assumption, under which residual dependence amongst outcomes can be used to simplify and sharpen sensitivity analyses. We focus on a class of factor models for which we can bound the causal effects for all outcomes conditional on a single sensitivity parameter that represents the fraction of treatment variance explained by unobserved confounders. We characterize how causal ignorance regions shrink under additional prior assumptions about the presence of null control outcomes, and provide new approaches for quantifying the robustness of causal effect estimates. Finally, we illustrate our sensitivity analysis workflow in practice, in an analysis of both simulated data and a case study with data from the National Health and Nutrition Examination Survey (NHANES).
Secure Transmission Design for Virtual Antenna Array-Aided Device-to-Device Multicast Communications

This paper investigates the physical-layer security in a Virtual Antenna Array (VAA)-aided Device-to-Device Multicast (D2DM) communication system, where the User Equipments (UEs) who cache the common content will form a VAA system and cooperatively multicast the content to UEs who desire it. Specifically, with the target of securing the VAA-aided D2DM communication under the threat of multiple eavesdroppers, we propose a secure beamforming scheme by jointly considering the formed VAA and the Base Station (BS). For obtaining the optimal beamforing vectors, a nonsmooth and nonconvex Weight Sum Rate Maximization Problem (WRMP) is formulated and solved using Successive Convex Approximation (SCA) approach. Furthermore, we consider the worst case that eavesdroppers cooperatively form a eavesdrop VAA to enhance the overhearing capacity. In the worst case, we modify the securing beamforming scheme, formulate the corresponding WRMP and solve it using a two-level optimization. Simulation results validate the improvements of the VAA-aided D2DM scheme in terms of communication security compared with conventional D2DM schemes.
Vehicle-road Cooperative Simulation and 3D Visualization System

The safety of single-vehicle autonomous driving technology is limited due to the limits of perception capability of on-board sensors. In contrast, vehicle-road collaboration technology can overcome those limits and improve the traffic safety and efficiency, by expanding the sensing range, improving the perception accuracy, and reducing the response time. However, such a technology is still under development; it requires rigorous testing and verification methods to ensure the reliability and trustworthiness of the technology. In this thesis, we focus on three major tasks: (1) analyze the functional characteristics related to the scenarios of vehicle-road cooperations, highlightening the differences between vehicle-road cooperative systems and traditional single-vehicle autonomous driving systems; (2) refine and classifiy the functional characteristics of vehicle-road cooperative systems; (3) design and develop a simulation system, and provide a visual interface to facilitate development and analysis. The efficiency and effectiveness the proposed method are verfied by experiments.
Terrain-based vehicle localization using an active suspension system

This paper, for the first time, presents a terrain-based localization approach using sensor data from an active suspension system. The contribution is four-fold. First, it is shown that a location dependent road height profile can be created from sensor data of the active suspension system. Second, an algorithm is developed to extract a pitch profile from the road height profile data. The ideal pitch profile is vehicle-independent and only depends on the road. This pitch profile generated from an on-board computer is matched with a known terrain map to achieve real-time positioning. Third, a crowd-sourced map creation algorithm is developed to create and improve the terrain map that contains pitch profile. Fourth, experiments have been conducted to validate the accuracy and robustness of the proposed localization approach.
Simultaneous Brillouin and piezoelectric coupling to high-frequency bulk acoustic resonator

Bulk acoustic resonators support robust, long-lived mechanical modes, capable of coupling to various quantum systems. In separate works, such devices have achieved strong coupling to both superconducting qubits, via piezoelectricity, and optical cavities, via Brillouin interactions. In this work, we present a novel hybrid microwave/optical platform capable of coupling to bulk acoustic waves through cavity-enhanced piezoelectric and photoelastic interactions. The modular, tunable system achieves fully resonant and well-mode-matched interactions between a 3D microwave cavity, a high-frequency bulk acoustic resonator, and a Fabry Perot cavity. We realize this piezo-Brillouin interaction in x-cut quartz, demonstrating the potential for strong optomechanical interactions and high cooperativity using optical cavity enhancement. We further show how this device functions as a bidirectional electro-opto-mechanical transducer, with quantum efficiency exceeding $10^{-8}$, and a feasible path towards unity conversion efficiency. The high optical sensitivity and ability to apply large resonant microwave field in this system also offers a new tool for probing anomalous electromechanical couplings, which we demonstrate by investigating (nominally-centrosymmetric) CaF$_2$ and revealing a parasitic piezoelectricity of 83 am/V. Such studies are an important topic for emerging quantum technologies, and highlight the versatility of this new hybrid platform.
Fair Coflow Scheduling via Controlled Slowdown

The average coflow completion time (CCT) is the standard performance metric in coflow scheduling. However, standard CCT minimization may introduce unfairness between the data transfer phase of different computing jobs. Thus, while progress guarantees have been introduced in the literature to mitigate this fairness issue, the trade-off between fairness and efficiency of data transfer is hard to control. This paper introduces a fairness framework for coflow scheduling based on the concept of slowdown, i.e., the performance loss of a coflow compared to isolation. By controlling the slowdown it is possible to enforce a target coflow progress while minimizing the average CCT. In the proposed framework, the minimum slowdown for a batch of coflows can be determined in polynomial time. By showing the equivalence with Gaussian elimination, slowdown constraints are introduced into primal-dual iterations of the CoFair algorithm. The algorithm extends the class of the sigma-order schedulers to solve the fair coflow scheduling problem in polynomial time. It provides a 4-approximation of the average CCT w.r. t. an optimal scheduler. Extensive numerical results demonstrate that this approach can trade off average CCT for slowdown more efficiently than existing state of the art schedulers.
An Iterative Warping and Clustering Algorithm to Estimate Multiple Wave-Shape Functions From a Nonstationary Oscillatory Signal

Nonsinusoidal oscillatory signals are everywhere. In practice, the nonsinusoidal oscillatory pattern, modeled as a 1-periodic wave-shape function (WSF), might vary from cycle to cycle. When there are finite different WSFs, <inline-formula><tex-math notation="LaTeX">$s_{1},\ldots,s_{K}$</tex-math></inline-formula>, so that the WSF jumps from one to another suddenly, the different WSFs and jumps encode useful information. We present an iterative warping and clustering algorithm to estimate <inline-formula><tex-math notation="LaTeX">$s_{1},\ldots,s_{K}$</tex-math></inline-formula> from a nonstationary oscillatory signal with time-varying amplitude and frequency, and hence the change points of the WSFs. The algorithm is a novel combination of time-frequency analysis, singular value decomposition entropy and vector spectral clustering. We demonstrate the efficiency of the proposed algorithm with simulated and real signals, including the voice signal, arterial blood pressure, electrocardiogram and accelerometer signal. Moreover, we provide a mathematical justification of the algorithm under the assumption that the amplitude and frequency of the signal are slowly time-varying and there are finite change points that model sudden changes from one wave-shape function to another one.
CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition

Current facial expression recognition systems de-mand an expensive re-training routine when deployed to different scenarios than they were trained for. Biasing them towards learning specific facial characteristics, instead of performing typical transfer learning methods, might help these systems to maintain high performance in different tasks, but with a reduced training effort. In this paper, we propose Contrastive Inhibitory AdaptatiOn (CIAO), a mechanism that adapts the last layer of facial encoders to depict specific affective characteristics on different datasets. CIAO presents an improvement in facial expression recognition performance over six different datasets with very unique affective representations, in particular when compared with state-of-the-art models. In our discussions, we make an in-depth analysis on how the learned high-level facial features are represented, and how they contribute to each indi-vidual dataset characteristics. We finalize our study by discussing how CIAO positions itself within the range of recent findings on non-universal facial expressions perception, and its impact on facial expression recognition research.