Miracle-dz/UniFAR-FTUs huggingface dataset:
Chance Constraint Robust Control with Control Barrier Functions

Path planning and control is a crucial task for the robot, especially when considering noise measurements. In this paper, we propose a novel approach to design linear feedback controllers for a robot navigating in polygonal environments with noisy measurements. The stability and safety guarantees of the controller come from the chance Control Barrier Function constraints and the chance Control Lyapunov Function constraints, respectively. The controller design problem is set up as a chance constraint-based robust optimization. We apply convex over-approximations to obtain upper bounds of constraints, which lead to a quadratic constraint quadratic program (QCQP). We provide simulation results for equilibrium control and path control. Numerical experiments demonstrate that the controller is robust with noise measurements.
Dual-energy CT Reconstruction from Dual Quarter Scans

Compared with conventional single-energy computed tomography (CT), dual-energy CT (DECT) provides better material differentiation but most DECT imaging systems require dual full-angle projection data at different X-ray spectra. Relaxing the requirement of data acquisition is a particularly attractive research to promote the applications of DECT in a wide range of imaging areas. In this work, we design a novel DECT imaging scheme with dual quarter scans and propose an efficient method to reconstruct the desired DECT images from dual limitedangle projection data, which enables DECT on imaging configurations with half-scan and largely reduces scanning angles and radiation doses. We first study the characteristics of image artifacts under dual quarter scans scheme, and find that the directional limited-angle artifacts of DECT images are complementarily distributed in image domain because the corresponding X-rays of highand low-energy scans are orthogonal. Inspired by this finding, a fusion CT image is generated by integrating the limited-angle DECT images of dual quarter scans. This strategy largely reduces the limited-angle artifacts and preserves the image edges and inner structures. Utilizing the capability of neural network in the modeling of nonlinear problem, a novel Anchor network with singleentry double-out architecture is designed in this work to yield the desired DECT images from the generated fusion CT image. Experimental results on the simulated and real data verify the effectiveness of the proposed method.
Revealing beyond-mean-field correlations in a nuclear ensemble via a proxy qubit

Dorian A. Gangloff, Leon Zaporski, Jonathan H. Bodey, Clara Bachorz, Daniel M. Jackson, Gabriel Éthier-Majcher, Constantin Lang, Edmund Clarke, Maxime Hugues, Claire Le Gall1,†, and Mete Atatüre1,† Cavendish Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge, CB3 0HE, UK EPSRC National Epitaxy Facility, University of Sheffield, Broad Lane, Sheffield, S3 7HQ, UK Université Côte d’Azur, CNRS, CRHEA, rue Bernard Gregory, 06560 Valbonne, France
On a length-biased Birnbaum-Saunders regression model applied to meteorological data

Abstract The length-biased Birnbaum-Saunders distribution is both useful and practical for environmental sciences. In this paper, we initially derive some new properties for the length-biased Birnbaum-Saunders distribution, showing that one of its parameters is the mode and that it is bimodal. We then introduce a new regression model based on this distribution. We implement the maximum likelihood method for parameter estimation, approach interval estimation and consider three types of residuals. An elaborate Monte Carlo study is carried out for evaluating the performance of the likelihood-based estimates, the confidence intervals and the empirical distribution of the residuals. Finally, we illustrate the proposed regression model with the use of a real meteorological data set.
One-Bit Target Detection in Collocated MIMO Radar and Performance Degradation Analysis

Target detection is an important problem in multiple-input multiple-output (MIMO) radar. Many existing target detection algorithms were proposed without taking into consideration the quantization error caused by analog-to-digital converters (ADCs). This paper addresses the problem of target detection for MIMO radar with one-bit ADCs and derives a Rao’s test-based detector. The proposed method has several appealing features: 1) it is a closed-form detector; 2) it allows us to handle sign measurements straightforwardly; 3) there are closed-form approximations of the detector’s distributions, which allow us to theoretically evaluate its performance. Moreover, the closed-form distributions allow us to study the performance degradation due to the one-bit ADCs, yielding an approximate 2 dB loss in the low-signal-to-noise-ratio (SNR) regime compared to $\infty$-bit ADCs. Simulation results are included to showcase the advantage of the proposed detector and validate the accuracy of the theoretical results.
Weak Identification with Bounds in a Class of Minimum Distance Models

When parameters are weakly identified, bounds on the parameters may provide a valuable source of information. Existing weak identification estimation and inference results are unable to combine weak identification with bounds. Within a class of minimum distance models, this paper proposes identification-robust inference that incorporates information from bounds when parameters are weakly identified. The inference is based on limit theory that combines weak identification theory with parameter-on-the-boundary theory. This paper demonstrates the role of the bounds and identification-robust inference in two example factor models. This paper also demonstrates the identification-robust inference in an empirical application, a factor model for parental investments in children.
Large- and small-amplitude shock-wave oscillations over axisymmetric bodies in high-speed flow

Abstract The phenomena of self-sustained shock-wave oscillations over conical bodies with a blunt axisymmetric base subject to uniform high-speed flow are investigated in a hypersonic wind tunnel at Mach number $M = 6$. The flow and shock-wave dynamics is dictated by two non-dimensional geometric parameters presented by the three length scales of the body, two of which are associated with the conical forebody and one with the base. Time-resolved schlieren imagery from these experiments reveals the presence of two disparate states of shock-wave oscillations in the flow, and allows for the mapping of unsteadiness boundaries in the two-parameter space. Physical mechanisms are proposed to explain the oscillations and the transitions of the shock-wave system from steady to oscillatory states. In comparison with the canonical single-parameter problem of shock-wave oscillations over spiked-blunt bodies reported in literature, the two-parameter nature of the present problem introduces distinct elements to the flow dynamics.
Exploring Effectiveness of Inter-Microtask Qualification Tests in Crowdsourcing

Qualification tests in crowdsourcing are often used to pre-filter workers by measuring their ability in executing microtasks.While creating qualification tests for each task type is considered as a common and reasonable way, this study investigates into its worker-filtering performance when the same qualification test is used across multiple types of tasks.On Amazon Mechanical Turk, we tested the annotation accuracy in six different cases where tasks consisted of two different difficulty levels, arising from the identical real-world domain: four combinatory cases in which the qualification test and the actual task were the same or different from each other, as well as two other cases where workers with Masters Qualification were asked to perform the actual task only.The experimental results demonstrated the two following findings: i) Workers that were assigned to a difficult qualification test scored better annotation accuracy regardless of the difficulty of the actual task; ii) Workers with Masters Qualification scored better annotation accuracy on the low-difficulty task, but were not as accurate as those who passed a qualification test on the high-difficulty task.
VHE gamma-ray detection of FSRQ QSO B1420+326 and modeling of its enhanced broadband state in 2020

Context. QSO B1420+326 is a blazar classified as a flat-spectrum radio quasar (FSRQ). At the beginning of the year 2020, it was found to be in an enhanced flux state and an extensive multiwavelength campaign allowed us to trace the evolution of the flare. Aims. We search for very high-energy (VHE) gamma-ray emission from QSO B1420+326 during this flaring state. We aim to characterize and model the broadband emission of the source over different phases of the flare. Methods. The source was observed with a number of instruments in radio, near-infrared, optical (including polarimetry and spectroscopy), ultraviolet, X-ray, and gamma-ray bands. We use dedicated optical spectroscopy results to estimate the accretion disk and the dust torus luminosity. We performed spectral energy distribution modeling in the framework of combined synchrotron-self-Compton and external Compton scenario in which the electron energy distribution is partially determined from acceleration and cooling processes. Results. During the enhanced state, the flux of both SED components of QSO B1420+326 drastically increased and the peaks were shifted to higher energies. Follow-up observations with the MAGIC telescopes led to the detection of VHE gamma-ray emission from this source, making it one of only a handful of FSRQs known in this energy range. Modeling allows us to constrain the evolution of the magnetic field and electron energy distribution in the emission region. The gamma-ray flare was accompanied by a rotation of the optical polarization vector during a low -polarization state. Also, a new superluminal radio knot contemporaneously appeared in the radio image of the jet. The optical spectroscopy shows a prominent FeII bump with flux evolving together with the continuum emission and a MgII line with varying equivalent width.
Adversarial Patch Generation for Automated Program Repair

Automated Program Repair has attracted significant research in recent years, leading to diverse techniques that focus on two main directions: search-based and semantic-based program repair. The former techniques often face challenges due to the vast search space, resulting in difficulties in identifying correct solutions, while the latter approaches are constrained by the capabilities of the underlying semantic analyser, limiting their scalability. In this paper, we propose NEVERMORE, a novel learning-based mechanism inspired by the adversarial nature of bugs and fixes. NEVERMORE is built upon the Generative Adversarial Networks architecture and trained on historical bug fixes to generate repairs that closely mimic human-produced fixes. Our empirical evaluation on 500 real-world bugs demonstrates the effectiveness of NEVERMORE in bug-fixing, generating repairs that match human fixes for 21.2% of the examined bugs. Moreover, we evaluate NEVERMORE on the Defects4J dataset, where our approach generates repairs for 4 bugs that remained unresolved by state-of-the-art baselines. NEVERMORE also fixes another 8 bugs which were only resolved by a subset of these baselines. Finally, we conduct an in-depth analysis of the impact of input and training styles on NEVERMORE's performance, revealing where the chosen style influences the model's bug-fixing capabilities.
Chiral Thermodynamics in Tailored Chiral Optical Environments

We present an optomechanical model that describes the stochastic motion of an overdamped chiral nanoparticle diffusing in the optical bistable potential formed in the standing-wave of two counter-propagating Gaussian beams. We show how chiral optical environments can be induced in the standing-wave with no modification of the initial bistability by controlling the polarizations of each beam. Under this control, optical chiral densities and/or an optical chiral fluxes are generated, associated respectively with reactive vs. dissipative chiral optical forces exerted on the diffusing chiral nanoparticle. This optomechanical chiral coupling bias the thermodynamics of the thermal activation of the barrier crossing, in ways that depend on the nanoparticle enantiomer and on the optical field enantiomorph. We show that reactive chiral forces, being conservative, contribute to a global, enantiospecific, change of the Helmholtz free energy bistable landscape. In contrast, when the chiral nanoparticle is immersed in a dissipative chiral environment, the symmetry of the bistable potential is broken by non-conservative chiral optical forces. In this case, the chiral electromagnetic fields continuously transfer, through dissipation, mechanical energy to the chiral nanoparticle. For this chiral nonequilibrium steady-state, the thermodynamic changes of the barrier crossing take the form of heat transferred to the thermal bath and yield chiral deracemization schemes that can be explicitly calculated within the framework of our model. Threedimensional stochastic simulations confirm and further illustrate the thermodynamic impact of chirality. Our results reveal how chiral degrees of freedom both of the nanoparticle and of the optical fields can be transformed into true thermodynamics control parameters, thereby demonstrating the significance of optomechanical chiral coupling in stochastic thermodynamics.
Strong coupling of magnetization waves with sub-THz cavity fields

The hybrid nature of strongly coupled light-matter states, were the dissipation rate is lower than the exchange rate (Rabi frequency) [1], has attracted a lot of attention since the late 1980s [2]. In the 2000s decade, strong light-matter coupling was investigated in the solid state [3], where the coupling of light interacting with N resonators increases by a factor of √ N [4]. This regime might lead to device elements that may play a role in quantum devices [1, 5–9]. In the 2010s, interest turned toward magnon-photon coupling in ferromagnetic materials [10–18], as spins benefit from a relatively low coupling to their environment [6, 19]. Achieving strong coupling with antiferromagnets has the advantage of operating at frequencies in the terahertz (THz) range. There is a large variety of materials available, and many of them show magnetic ordering above room temperature. The absence of stray fields could in principle allow for extremely dense packing of elements [20]. However, there are only a few examples of weak magnon-photon coupling in antiferromagnets [21, 22]. Strong coupling was only achieved at low frequencies [23], in bulk samples [24] or via an indirect coupling [25, 26], while direct strong cavity-magnon coupling was not shown until now [27].
NN-EMD: Efficiently Training Neural Networks Using Encrypted Multi-Sourced Datasets

Training complex neural network models using third-party cloud-based infrastructure among multiple data sources is a promising approach among existing machine learning solutions. However, privacy concerns of large-scale data collections and recent regulations have restricted the availability and use of privacy sensitive data in the third-party infrastructure. To address such privacy issues, a promising emerging approach is to train a neural network model over an encrypted dataset. Specifically, the model training process can be outsourced to a third party such as a cloud service that is backed by significant computing power, while the encrypted training data keeps the data confidential from the third party. Compared to training a traditional machine learning model over encrypted data, however, it is extremely challenging to train a deep neural network (DNN) model over encrypted data for two reasons: first, it requires large-scale computation over huge datasets; second, the existing solutions for computation over encrypted data, such as using homomorphic encryption, is inefficient. Further, for enhanced performance of a DNN model, we also need to use huge training datasets composed of data from multiple data sources that may not have pre-established trust relationships among each other. We propose a novel framework, NN-EMD, to train DNN over encrypted multiple datasets collected from multiple sources. Toward this, we propose a set of secure computation protocols using hybrid functional encryption schemes. We evaluate our framework for performance with regards to the training time and model accuracy on the MNIST datasets. We show that compared to other existing frameworks, our proposed NN-EMD framework can significantly reduce the training time, while providing comparable model accuracy and privacy guarantees as well as supporting multiple data sources. Furthermore, the depth and complexity of neural networks do not affect the training time despite introducing a privacy-preserving NN-EMD setting.
Enabling Tangible Interaction through Detection and Augmentation of Everyday Objects

Digital interaction with everyday objects has become popular since the proliferation of camera-based systems that detect and augment objects"just-in-time". Common systems use a vision-based approach to detect objects and display their functionalities to the user. Sensors, such as color and depth cameras, have become inexpensive and allow seamless environmental tracking in mobile as well as stationary settings. However, object detection in different contexts faces challenges as it highly depends on environmental parameters and the conditions of the object itself. In this work, we present three tracking algorithms which we have employed in past research projects to track and recognize objects. We show, how mobile and stationary augmented reality can be used to extend the functionalities of objects. We conclude, how common items can provide user-defined tangible interaction beyond their regular functionality.
Variation and oscillation for harmonic operators in the inverse Gaussian setting

<p style='text-indent:20px;'>We prove variation and oscillation <inline-formula><tex-math id="M1">\begin{document}$ L^p $\end{document}</tex-math></inline-formula>-inequalities associated with fractional derivatives of certain semigroups of operators and with the family of truncations of Riesz transforms in the inverse Gaussian setting. We also study these variational <inline-formula><tex-math id="M2">\begin{document}$ L^p $\end{document}</tex-math></inline-formula>-inequalities in a Banach-valued context by considering Banach spaces with the UMD-property and whose martingale cotype is fewer than the variational exponent. We establish <inline-formula><tex-math id="M3">\begin{document}$ L^p $\end{document}</tex-math></inline-formula>-boundedness properties for weighted difference involving the semigroups under consideration. </p>
Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers

Video analytics applications use edge compute servers for the analytics of the videos (for bandwidth and privacy). Compressed models that are deployed on the edge servers for inference suffer from data drift, where the live video data diverges from the training data. Continuous learning handles data drift by periodically retraining the models on new data. Our work addresses the challenge of jointly supporting inference and retraining tasks on edge servers, which requires navigating the fundamental tradeoff between the retrained model's accuracy and the inference accuracy. Our solution Ekya balances this tradeoff across multiple models and uses a micro-profiler to identify the models that will benefit the most by retraining. Ekya's accuracy gain compared to a baseline scheduler is 29% higher, and the baseline requires 4x more GPU resources to achieve the same accuracy as Ekya.
The ABC of Deutsch–Hayden Descriptors

It has been more than 20 years since Deutsch and Hayden proved the locality of quantum theory, using the Heisenberg picture of quantum computational networks. Of course, locality holds even in the face of entanglement and Bell’s theorem. Today, most researchers in quantum foundations are still convinced not only that a local description of quantum systems has not yet been provided, but that it cannot exist. The main goal of this paper is to address this misconception by re-explaining the descriptor formalism in a hopefully accessible and self-contained way. It is a step-by-step guide to how and why descriptors work. Finally, superdense coding is revisited in the light of descriptors.
CAMTA: Causal Attention Model for Multi-touch Attribution

Advertising channels have evolved from conventional print media, billboards and radio-advertising to online digital advertising (ad), where the users are exposed to a sequence of ad campaigns via social networks, display ads, search etc. While advertisers revisit the design of ad campaigns to concurrently serve the requirements emerging out of new ad channels, it is also critical for advertisers to estimate the contribution from touch-points (view, clicks, converts) on different channels, based on the sequence of customer actions. This process of contribution measurement is often referred to as multi-touch attribution (MTA). In this work, we propose CAMTA, a novel deep recurrent neural network architecture which is a causal attribution mechanism for user-personalised MTA in the context of observational data. CAMTA minimizes the selection bias in channel assignment across time-steps and touchpoints. Furthermore, it utilizes the users' pre-conversion actions in a principled way in order to predict per-channel attribution. To quantitatively benchmark the proposed MTA model, we employ the real-world Criteo dataset and demonstrate the superior performance of CAMTA with respect to prediction accuracy as compared to several baselines. In addition, we provide results for budget allocation and user-behaviour modeling on the predicted channel attribution.
Icarus: A Flat and Fast Prograde Stellar Stream in the Milky Way Disk

We explore the local volume of the Milky Way via chemical and kinematical measurements from high-quality astrometric and spectroscopic data recently released by the Gaia, APOGEE, and GALAH programs. We chemically select 1137 stars up to 2.5 kpc of the Sun and [Fe/H] ≤ −1.0 dex, and find evidence of statistically significant substructures. Clustering analysis in velocity space classifies 163 objects into eight kinematical groups, whose origin is further investigated with high-resolution N-body numerical simulations of single merging events. The two retrograde groups appear associated with Gaia-Sausage-Enceladus (GSE), while the slightly prograde group could be connected to GSE or possibly Wukong. We find evidence of a new 44-member-strong prograde stream that we name Icarus; to our knowledge, Icarus is the fast-rotating stream closest to the Galactic disk to date ( , 〈V + VLSR〉 ≃ 231 km s−1). Its peculiar chemical (〈[Fe/H]〉 ≃ −1.45, 〈[Mg/Fe]〉 ≃ −0.02) and dynamical (mean eccentricity ≃ 0.11) properties are consistent with the accretion of debris from a dwarf galaxy progenitor with a stellar mass of ∼109M☉ on an initial prograde low-inclination orbit, ∼10°. The remaining prograde groups are either streams previously released by the same progenitor of Icarus (or Nyx), or remnants from different satellites accreted on initial orbits at higher inclination.