Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonclassical correlations of radiation in relation to Bell nonlocality

We analyze nonclassical correlations between outcomes of measurements conducted on two spatial radiation modes. These correlations cannot be simulated with statistical mixtures of coherent states or, more generally, with non-negative phase-space functions of quantum states and measurements. We argue that nonclassical correlations are naturally related to Bell nonlocality, the former being a more general class of quantum correlations. Indeed, it is known that local realistic as well as noncontextual models correspond to non-negative solutions to a system of linear equations for the joint probability distributions of all observables. We demonstrate that nonclassical correlations correspond to a particular solution to this system, which may have negative values even if local realism is not violated. A modification of Bell inequalities enables us to test such correlations. At the same time, our approach leads to a formulation of Bell inequalities applicable also to continuous variables. The results are illustrated with two-mode squeezed vacuum states and with hybrid entangled states (Schr\"odinger-Cat states), one mode being analyzed by balanced and the other one by unbalanced homodyne detection.
Cooperation for Scalable Supervision of Autonomy in Mixed Traffic

Advances in autonomy offer the potential for dramatic positive outcomes in a number of domains, yet enabling their safe deployment remains an open problem. This work's motivating question is: In safety-critical settings, can we avoid the need to have one human supervise one machine at all times? The work formalizes this scalable supervision problem by considering remotely located human supervisors and investigating how autonomous agents can cooperate to achieve safety. This article focuses on the safety-critical context of autonomous vehicles (AVs) merging into traffic consisting of a mixture of AVs and human drivers. The analysis establishes high reliability upper bounds on human supervision requirements. It further shows that AV cooperation can improve supervision reliability by orders of magnitude and counterintuitively requires fewer supervisors (per AV) as more AVs are adopted. These analytical results leverage queuing-theoretic analysis, order statistics, and a conservative, reachability-based approach. A key takeaway is the potential value of cooperation in enabling the deployment of autonomy at scale. While this work focuses on AVs, the scalable supervision framework may be of independent interest to a broader array of autonomous control challenges.
The effects of routing flexibility in a multi-stage pull-type system

The pull-type manufacturing system is known for its need for a frozen schedule, balanced work load and inability to handle fluctuations in demand. But today research and development are addressing ways to handle a pull-type system to accommodate greater fluctuations. One approach is to design the system with machines that have a greater routing flexibility in what work they can perform. However, most of the existing literature dealing with routing flexibility has primarily been devoted to the push-type production systems. This research examined the effects of routing flexibility for a multi-stage kanban-controlled pull-type system.
Photon Walk in Transparent Wood: Scattering and Absorption in Hierarchically Structured Materials

The optical response of hierarchical materials is convoluted, which hinders their direct study and property control. Transparent wood (TW) is an emerging biocomposite in this category, which adds optical function to the structural properties of wood. Nano‐ and microscale inhomogeneities in composition, structure, and at interfaces strongly affect light transmission and haze. While interface manipulation can tailor TW properties, the realization of optically clear wood requires detailed understanding of light–TW interaction mechanisms. Herein is shown how material scattering and absorption coefficients can be extracted from a combination of experimental spectroscopic measurements and a photon diffusion model. Contributions from different length scales can thus be deciphered and quantified. It is shown that forward scattering dominates haze in TW, primarily caused by refractive index (RI) mismatch between the wood substrate and the polymer phase. Rayleigh scattering from the wood cell wall and absorption from residual lignin have minor effects on transmittance, but the former affects haze. Results provide guidance for material design of transparent hierarchical composites toward desired optical functionality; it is demonstrated experimentally how transmittance and haze of TW can be controlled over a broad range.
A Systematic Analysis of Stellar Populations in the Host Galaxies of Changing-look AGNs

“Changing-look” active galactic nuclei (CL-AGNs) are a newly discovered class of AGNs that show the appearance (or disappearance) of broad emission lines within short timescales (months to years), and are often associated with dramatic changes in their continuum emissions. They provide us with an unprecedented chance to directly investigate the host galaxy properties with minimal contamination from the luminous central engine during the turn-off state, which is difficult for normal luminous AGNs. In this work, for the first time, we systematically characterize the stellar populations and star formation histories of host galaxies for 26 turn-off CL-AGNs using the stellar population synthesis code STARLIGHT. We find that the stellar populations of CL-AGNs are similar to those of normal AGNs, except that the intermediate stellar populations contribute more fractions. We estimate their stellar velocity dispersions (σ ⋆) and black hole masses (M BH,vir), and find that CL-AGNs also follow the overall M BH–σ ⋆ relationship. We also confirm the previous claims that CL-AGNs tend to be biased toward lower Eddington ratios, and that their extreme variabilities are more likely due to the intrinsic changes of the accretion rates. In addition, CL-AGNs with recent star formations tend to have higher Eddington ratios. Compared with previous studies, our analysis suggests that there may be a correlation between CL-AGN host galaxy properties and their CL phenomena.
Valence can control the non-exponential viscoelastic relaxation of reversible multivalent gels

Hugo Le Roy, ∗ Jake Song, Gareth H. McKinley, Niels Holten-Andersen, and Martin Lenz 4, † Université Paris-Saclay, CNRS, LPTMS, 91405, Orsay, France Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA PMMH, CNRS, ESPCI Paris, PSL University, Sorbonne Université, Université de Paris, F-75005, Paris, France
Sentiment Dynamics of Success: Fractal Scaling of Story Arcs Predicts Reader Preferences

e explore the correlation between the sentiment arcs of H. C. Andersen’s fairy tales and their popularity, measured as their average score on the platform GoodReads. Specifically, we do not conceive a story’s overall sentimental trend as predictive per se, but we focus on its coherence and predictability over time as represented by the arc’s Hurst exponent. We find that degrading Hurst values tend to imply degrading quality scores, while a Hurst exponent between . 55 and . 65 might indicate a “sweet spot” for literary appreciation.
The $f$-Divergence Reinforcement Learning Framework

The framework of deep reinforcement learning (DRL) provides a powerful and widely applicable mathematical formalization for sequential decision-making. This paper present a novel DRL framework, termed \emph{$f$-Divergence Reinforcement Learning (FRL)}. In FRL, the policy evaluation and policy improvement phases are simultaneously performed by minimizing the $f$-divergence between the learning policy and sampling policy, which is distinct from conventional DRL algorithms that aim to maximize the expected cumulative rewards. We theoretically prove that minimizing such $f$-divergence can make the learning policy converge to the optimal policy. Besides, we convert the process of training agents in FRL framework to a saddle-point optimization problem with a specific $f$ function through Fenchel conjugate, which forms new methods for policy evaluation and policy improvement. Through mathematical proofs and empirical evaluation, we demonstrate that the FRL framework has two advantages: (1) policy evaluation and policy improvement processes are performed simultaneously and (2) the issues of overestimating value function are naturally alleviated. To evaluate the effectiveness of the FRL framework, we conduct experiments on Atari 2600 video games and show that agents trained in the FRL framework match or surpass the baseline DRL algorithms.
Better Pay Attention Whilst Fuzzing

Fuzzing is one of the prevailing methods for vulnerability detection. However, even state-of-the-art fuzzing methods become ineffective after some period of time, i.e., the coverage hardly improves as existing methods are ineffective to focus the attention of fuzzing on covering the hard-to-trigger program paths. In other words, they cannot generate inputs that can break the bottleneck due to the fundamental difficulty in capturing the complex relations between the test inputs and program coverage. In particular, existing fuzzers suffer from the following main limitations: 1) lacking an overall analysis of the program to identify the most “rewarding” seeds, and 2) lacking an effective mutation strategy which could continuously select and mutates the more relevant “bytes” of the seeds. In this work, we propose an approach called ATTuzz to address these two issues systematically. First, we propose a lightweight dynamic analysis technique that estimates the “reward” of covering each basic block and selects the most rewarding seeds accordingly. Second, we mutate the selected seeds according to a neural network model which predicts whether a certain “rewarding” block will be covered given certain mutations on certain bytes of a seed. The model is a deep learning model equipped with an attention mechanism which is learned and updated periodically whilst fuzzing. Our evaluation shows that ATTuzz significantly outperforms 5 state-of-the-art grey-box fuzzers on 6 popular real-world programs and MAGMA data sets at achieving higher edge coverage and finding new bugs. In particular, ATTuzz achieved 1.2X edge coverage and 1.8X bugs detected than AFL++ over 24-hour runs. In addition, ATTuzz also finds 4 new bugs in the latest version of some popular software including p7zip and openUSD.
Sequential Change Detection through Empirical Distribution and Universal Codes

Universal compression algorithms have been studied in the past for sequential change detection, where they have been used to estimate the post-change distribution in the modified version of the Cumulative Sum (CUSUM) Test. In this paper, we introduce a modified CUSUM test where the pre-change distribution is also unknown and an empirical version of the pre-change distribution is used to implement the algorithm. We present a study of various characteristics of this modified CUSUM Test and then prove its asymptotic optimality.
Analyzing population dynamics models via Sumudu transform

This study demonstrates how to construct the solutions of a more general form of population dynamics models via a blend of variational iterative method with Sumudu transform. In this paper, population growth models are formulated in the form of delay differential equations of pantograph type which is a general form for the existing models. Innovative ways are presented for obtaining the solutions of population growth models where other analytic methods fail. Stimulating procedures for finding patterns and regularities in seemingly chaotic processes have been elucidated in this paper. How, when and why the changes in population sizes occur can be deduced through this study.
Public Release and Validation of SPEC CPU2017 PinPoints

Phase-based statistical sampling methods such as SimPoints have proven to be effective at dramatically reducing the long time for architectural simulators to run large workloads such as SPEC CPU2017. However, generating and validating them is a long and tenuous process. While checkpoints of program phases, or"pinballs", of SPEC CPU2017 have been collected by other researchers and shared with the research community, they are outdated and produce errors when used with the latest versions of the Sniper architectural simulator. To facilitate our own research as well as contribute to the community, we collect and validate our own pinballs for the SPEC CPU2017 SPECspeed suite and release them to the public domain. In this work we document our methodology, the hardware and software details of the collection process, and our validation results. In terms of CPI, our pinballs have an average error rate of 12% when compared with the native whole-program benchmark execution.
Design, implementation issues and performance of an inductive power transfer system for electric vehicle chargers with series–series compensation

The design, construction and evaluation of a contactless battery charger for electric vehicles (EVs) based on inductive power transfer (IPT) is presented in this study. The design of such systems entails a high degree of complexity because of the large number of design parameters involved and, consequently, trade-offs in selecting the key design parameters have to be established. The design process and selection of the IPT system parameters is detailed in this study, considering the most common specifications of EV chargers and the practical issues of the implementation. Regarding the compensation scheme, which is one of the main issues in the design, series compensation in both the primary and secondary has been adopted because of the advantages identified after a comprehensive analysis. A laboratory prototype has been built and tested, providing extensive results of the system performance in terms of efficiency and power transfer capability, considering load power variations, as well as changes in the air gap between coils. A detailed analysis of the efficiency of each stage in the IPT system and their contribution to the overall efficiency is also provided.
Circularly-polarised microstrip leaky-wave antenna

A novel circularly polarised microstrip leaky-wave antenna is proposed and tested. Two linear polarised orthogonal dual-terminals feeding microstrip leaky-wave antennas are fed with equal amplitude and 90° phase difference to generate the two orthogonal modes. The measured results show that the antenna achieves circular polarisation efficiently.
Mixed–frequency quantile regression with realized volatility to forecast Value-at-Risk*

The use of quantile regression to calculate risk measures has been widely recognized in the financial econometrics literature. When data are observed at mixed–frequency, the standard quantile regression models are no longer adequate. In this paper, we develop a model built on a mixed–frequency quantile regression to directly estimate the Value-at-Risk. In particular, the low–frequency component incorporates information coming from variables observed at, typically, monthly or lower frequencies, while the high–frequency component can include a variety of daily variables, like realized volatility measures or market indices. We derive the conditions for the weak stationarity of the daily return process suggested while the finite sample properties are investigated in an extensive Monte Carlo exercise. The validity of the proposed model is then explored through a real data application using the most important financial indexes. We show that our model outperforms other competing specifications, using backtesting and Model Confidence Set procedures.
Lifespan development of resting-state brain functional networks and its associations with ADHD symptoms

Attention-deficit/hyperactivity disorder (ADHD) is increasingly being diagnosed in both children and adults, but the neural mechanisms underlying its distinct symptoms and whether children and adults share the same mechanism remain poorly understood. Here, we used a nested-spectral partition (NSP) approach to study resting-state brain functional networks for ADHD patients (n=97) and healthy controls (HCs, n=97) across the lifespan (7-50 years). Compared to the linear developmental trajectory in HCs, ADHD patients have a quadratic trajectory in the whole brain and in most functional systems, whereas the limbic system dominantly affected by ADHD has a faster development. Furthermore, the limbic system better predicts hyperactivity, and the salient attention system better predicts inattention. These predictions are shared in children and adults with ADHD but not in HCs. Our findings reveal a lifespan developmental trajectory of brain networks in ADHD patients and provide shared neural bases of distinct ADHD symptoms in children and adults.
Lamb Waves for Detecting Delamination between Steel Bars and Concrete

The feasibility of detecting interface degradation and separation of steel bars in concrete beams using Lamb waves is investigated in this paper. The Lamb wave can propagate a long distance along the reinforcing steel bars embedded in concrete as the guided wave and is sensitive to the interface bonding condition between the steel bar and the concrete. The traditional ultrasonic methods for inspecting defects in concrete use reflection, transmission, and scattering of longitudinal waves by internal defects. These methods are good for detecting large voids in concrete, but they are not very efficient for detecting delamination at the interface between concrete and steel bars. In this study, a special coupler between the steel bar and ultrasonic transducers has been used to launch nonaxisymmetric guided waves in the steel bar. This investigation shows that the Lamb wave inspection technique is an efficient and effective tool for health monitoring of reinforced concrete structures.
Dielectric High-Power Bandpass Filter Using Quarter-Cut TE/sub 01delta/ Image Resonator for Cellular Base Stations

A dielectric high-power bandpass filter using "Quarter-cut TE/sub 01delta/ Image Resonators" has been developed. This resonator construction has a high unloaded Q over 7000 and also provides a sufficient thermal diffusion path to the metal housing. The insertion loss and the attenuation level of the 8-Pole elliptic function type filter are 0.37 dB and 95 dB respectively. The physical size of the dielectric filter is 280x135x65 (mm), one third to one fifth the volume of conventional cavity resonator filters.
Basophils as a potential therapeutic target in cancer

Basophils, which are considered as redundant relatives of mast cells and the rarest granulocytes in peripheral circulation, have been neglected by researchers in the past decades. Previous studies have revealed their vital roles in allergic diseases and parasitic infections. Intriguingly, recent studies even reported that basophils might be associated with cancer development, as activated basophils synthesize and release a variety of cytokines and chemokines in response to cancers. However, it is still subject to debate whether basophils function as tumor-protecting or tumor-promoting components; the answer may depend on the tumor biology and the microenvironment. Herein, we reviewed the role of basophils in cancers, and highlighted some potential and promising therapeutic strategies.
Ginsenosides Rd monomer inhibits proinflammatory cytokines production and alleviates DSS-colitis by NF-κB and P38MAPK pathways in mice

Abstract Background Ulcerative colitis (UC) is dramatically increasing worldwide, cannot be thoroughly cured, and reduces patients’ quality of life. Excessive activation of macrophages and over-production of cytokines play an important role in the pathogenesis of UC. Therefore, for its treatment, inhibiting macrophages' hyperactivation would be effective to develop new treatment approaches. Ginsenosides, extracted from ginseng, show an anti-inflammatory effect on the immunologic process. Our study used ginsenosides Rd monomer (GRd) to intervene in DSS-induced colitis mouse models and tested the immunological effect of macrophages. Method We observed body weights, weights of colons, colonic lengths, and inflammatory scores, as well as histological changes of DSS/DSS-GRd mice. We also isolated intestinal and peritoneal macrophages, performed qRT-PCR and ELISA to detect cytokines production by macrophages, and screened possible involved pathways by Western blotting. Results Administering 20 mg/Kg GRd to DSS mice for 7–14 days reduced colonic inflammation. Moreover, both in vivo and in vitro, levels of TNF-α, IFN-γ, IL-6, IL-12/23p40, and IL-17A were all inhibited by GRd at 14 days in intestinal macrophages, and 20 μmol/L GRd at 12 h in peritoneal macrophages, respectively, but longer time made no more benefit. Western blotting showed GRd could decrease expression of pJNK, p-p38, pIκBα, and P65 in nuclear. Conclusions Our data indicate that GRd could down-regulate cytokines production in macrophages and alleviate DSS-colitis in mice, which may be related to NF-κB and P38MAPK pathways. These results suggest that GRd has an anti-inflammatory effect on experimental colitis and may have potential efficacy in the treatment of UC alone or in combination.