Miracle-dz/UniFAR-FTUs huggingface dataset:
F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits

Generative modelling is an important unsupervised task in machine learning. In this work, we study a hybrid quantum-classical approach to this task, based on the use of a quantum circuit born machine. In particular, we consider training a quantum circuit born machine using f-divergences. We first discuss the adversarial framework for generative modelling, which enables the estimation of any f-divergence in the near term. Based on this capability, we introduce two heuristics which demonstrably improve the training of the born machine. The first is based on f-divergence switching during training. The second introduces locality to the divergence, a strategy which has proved important in similar applications in terms of mitigating barren plateaus. Finally, we discuss the long-term implications of quantum devices for computing f-divergences, including algorithms which provide quadratic speedups to their estimation. In particular, we generalise existing algorithms for estimating the Kullback–Leibler divergence and the total variation distance to obtain a fault-tolerant quantum algorithm for estimating another f-divergence, namely, the Pearson divergence.
Discovering Knowledge in Data Using Formal Concept Analysis

Formal Concept Analysis FCA has been successfully applied to data in a number of problem domains. However, its use has tended to be on an ad hoc, bespoke basis, relying on FCA experts working closely with domain experts and requiring the production of specialised FCA software for the data analysis. The availability of generalised tools and techniques, that might allow FCA to be applied to data more widely, is limited. Two important issues provide barriers: raw data is not normally in a form suitable for FCA and requires undergoing a process of transformation to make it suitable, and even when converted into a suitable form for FCA, real data sets tend to produce a large number of results that can be difficult to manage and interpret. This article describes how some open-source tools and techniques have been developed and used to address these issues and make FCA more widely available and applicable. Three examples of real data sets, and real problems related to them, are used to illustrate the application of the tools and techniques and demonstrate how FCA can be used as a semantic technology to discover knowledge. Furthermore, it is shown how these tools and techniques enable FCA to deliver a visual and intuitive means of mining large data sets for association and implication rules that complements the semantic analysis. In fact, it transpires that FCA reveals hidden meaning in data that can then be examined in more detail using an FCA approach to traditional data mining methods.
Fixed-Order ℋ2-Conic Control

ℋ2-conic controller design seeks to minimize the closed-loop ℋ2 norm for a nominal linear system while satisfying the Conic Sector Theorem for nonlinear stability. This problem has only been posed with limited design freedom, as opposed to fixed-order design where all controller parameters except the number of state estimates are free variables. Here, the fixed-order ℋ2-conic design problem is reformulated as a convergent series of convex approximations using iterative convex overbounding. A synthesis algorithm and various initializations are proposed. The synthesis is applied to a passivity-violated system with uncertain parameters and compared to benchmark controller designs.
Text analysis and deep learning: A network approach

Much information available to applied researchers is contained within written language or spoken text. Deep language models such as BERT have achieved unprecedented success in many applications of computational linguistics. However, much less is known about how these models can be used to analyze existing text. We propose a novel method that combines transformer models with network analysis to form a self-referential representation of language use within a corpus of interest. Our approach produces linguistic relations strongly consistent with the underlying model as well as mathematically well-defined operations on them, while reducing the amount of discretionary choices of representation and distance measures. It represents, to the best of our knowledge, the first unsupervised method to extract semantic networks directly from deep language models. We illustrate our approach in a semantic analysis of the term"founder". Using the entire corpus of Harvard Business Review from 1980 to 2020, we find that ties in our network track the semantics of discourse over time, and across contexts, identifying and relating clusters of semantic and syntactic relations. Finally, we discuss how this method can also complement and inform analyses of the behavior of deep learning models.
The Exact Modulus of the Generalized Concave Kurdyka-Łojasiewicz Property

We introduce a generalized version of the concave Kurdyka-Łojasiewicz (KL) property by employing nonsmooth desingularizing functions. We also present the exact modulus of the generalized concave KL property, which provides an answer to the open question regarding the optimal concave desingularizing function. The exact modulus is designed to be the smallest among all possible concave desingularizing functions. Examples are given to illustrate this pleasant property. In turn, using the exact modulus, we provide the sharpest upper bound for the total length of iterates generated by the celebrated Bolte-Sabach-Teboulle proximal alternating linearized minimization algorithm.
3D MHD simulation of a pulsationally-driven MRI decretion disc

We explore the pulsationally driven orbital mass ejection mechanism for Be star disc formation using isothermal, 3D magnetohydrodynamic (MHD) and hydrodynamic simulations. Non-radial pulsations are added to a star rotating at 95 per cent of critical as an inner boundary condition that feeds gas into the domain. In MHD, the initial magnetic field within the star is weak. The hydrodynamics simulation has limited angular momentum transport, resulting in repeating cycles of mass accumulation into a rotationally-supported disc at small radii followed by fall-back on to the star. The MHD simulation, conversely, has efficient (Maxwell αM ∼ 0.04) angular momentum transport provided by both of turbulent and coherent magnetic fields; a slowly decreting midplane driven by the magnetorotational instability and a supersonic wind on the surface of the disc driven by global magnetic torques. The angle and time-averaged properties near the midplane agree reasonably well with a 1D viscous decretion disc model with a modified $\tilde{\alpha }=0.5$, in which the gas transitions from a subsonic thin disc to a supersonic spherical wind at the critical point. 1D models, however, cannot capture the multi-phase decretion/angular structure seen in our simulations. Our results demonstrate that, at least under certain conditions, non-radial pulsations on the surface of a rapidly rotating, weakly magnetized star can drive a Keplerian disc with the basic properties of the viscous decretion disc paradigm, albeit coupled to a laminar wind away from the midplane. Future modeling of Be star discs should consider the possible existence of such a surface wind.
DeepECMP: Predicting Extracellular Matrix Proteins using Deep Learning

Introduction: The extracellular matrix (ECM) is a networkof proteins and carbohydrates that has a structural and bio-chemical function. The ECM plays an important role in dif-ferentiation, migration and signaling. Several studies havepredicted ECM proteins using machine learning algorithmssuch as Random Forests, K-nearest neighbours and supportvector machines but is yet to be explored using deep learn-ing. Method: DeepECMP was developed using several previ-ously used ECM datasets, asymmetric undersampling andan ensemble of 11 feed-forward neural networks. Results: The performance of DeepECMP was 83.6% bal-anced accuracy which outperformed several algorithms. Inaddition, the pipeline of DeepECMP has been shown to behighly efficient. Conclusion: This paper is the first to focus on utilizingdeep learning for ECM prediction. Several limitations areovercome by DeepECMP such as computational expense,availability to the public and usability outside of the humanspecies
Multiwavelength Spectral Analysis and Neural Network Classification of Counterparts to 4FGL Unassociated Sources

The Fermi-LAT unassociated sources represent some of the most enigmatic gamma-ray sources in the sky. Observations with the Swift-XRT and -UVOT telescopes have identified hundreds of likely X-ray and UV/optical counterparts in the uncertainty ellipses of the unassociated sources. In this work we present spectral fitting results for 205 possible X-ray/UV/optical counterparts to 4FGL unassociated targets. Assuming that the unassociated sources contain mostly pulsars and blazars, we develop a neural network classifier approach that applies gamma-ray, X-ray, and UV/optical spectral parameters to yield a descriptive classification of unassociated spectra into pulsars and blazars. From our primary sample of 174 Fermi sources with a single X-ray/UV/optical counterpart, we present 132 P bzr > 0.99 likely blazars and 14 P bzr < 0.01 likely pulsars, with 28 remaining ambiguous. These subsets of the unassociated sources suggest a systematic expansion to catalogs of gamma-ray pulsars and blazars. Compared to previous classification approaches our neural network classifier achieves significantly higher validation accuracy and returns more bifurcated P bzr values, suggesting that multiwavelength analysis is a valuable tool for confident classification of Fermi unassociated sources.
YY Hya and its interstellar environment

During a search for previously unknown Galactic emission nebulae, we discovered a faint 36' diameter Halpha emission nebula centered around the periodic variable YY Hya. Although this star has been classified as RR-Lyr variable, such a classification is inconsistent Gaia distance of ~450 pc. GALEX image data also shows YY Hya to have a strong UV excess, suggesting the existence of a hot, compact binary companion. In addition to our discovery image data, we obtained image of the region with CHILESCOPE time-series spectroscopy at MDM observatory. Also, we used data from various space missions to derive an exact orbital period and a SED. We find that YY Hya is a compact binary system containing a K dwarf star which is strongly irradiated by a hot WD companion. The spectral characteristics of the emission lines, visible only during maximum light of the perfectly sinusoidal optical}light curve shows signatures much like members of the BE UMa variable family. These are post common envelope pre-cataclysmic variables. However the companion star here is more massive than found in other group members and the progenitor of the white dwarf must have been a 3 to 4 Mo star. The nebula seems to be an ejected common envelope shell with a mass in the order of one Mo and an age of 500000 years. This makes it to be the biggest hitherto known such shell. Alignment of neighboring nebulosities some 45' to the northeast and southwest of YY Hya suggests that the system had strong bipolar outflows. We briefly speculate it might be related to the 1065 BP"guest-star"reported in ancient Chinese records as well.
Medical Dead-ends and Learning to Identify High-risk States and Treatments

Machine learning has successfully framed many sequential decision making problems as either supervised prediction, or optimal decision-making policy identification via reinforcement learning. In data-constrained offline settings, both approaches may fail as they assume fully optimal behavior or rely on exploring alternatives that may not exist. We introduce an inherently different approach that identifies possible"dead-ends"of a state space. We focus on the condition of patients in the intensive care unit, where a"medical dead-end"indicates that a patient will expire, regardless of all potential future treatment sequences. We postulate"treatment security"as avoiding treatments with probability proportional to their chance of leading to dead-ends, present a formal proof, and frame discovery as an RL problem. We then train three independent deep neural models for automated state construction, dead-end discovery and confirmation. Our empirical results discover that dead-ends exist in real clinical data among septic patients, and further reveal gaps between secure treatments and those that were administered.
Multi Proxy Anchor Loss and Effectiveness of Deep Metric Learning Performance Metrics

Deep metric learning (DML) learns the mapping, which maps into embedding space in which similar data is near and dissimilar data is far. However, conventional proxy-based losses for DML have two problems: gradient problems and applying the real-world dataset with multiple local centers. Besides, DML performance metrics also have some issues have stability and ﬂexibility. This paper proposes multi-proxies anchor (MPA) loss and normalized discounted cumulative gain (nDCG@k) metric. This study contributes three following: (1) MPA loss is able to learn the real-world dataset with multi-local centers. (2) MPA loss improves the training capacity of a neural network owing to solving the gradient issues. (3) nDCG@k metric encourages complete evaluation for various datasets. Finally, we demonstrate MPA loss’s effectiveness, and MPA loss achieves higher accuracy on two datasets for ﬁne-grained images.
Acute Extrapyramidal Side Effects from Smoked Haloperidol

Introduction Haloperidol is a dopamine receptor antagonist used to treat patients with psychotic disorders. Especially at high doses, haloperidol carries a higher risk of extrapyramidal symptoms (EPS) compared to second-generation antipsychotics. Few cases of haloperidol misuse are found in the medical literature. Case Presentation. We describe a patient with schizophrenia who smoked marijuana mixed with crushed haloperidol tablets. After the smoking of cannabis and haloperidol, the patient presented to the emergency department (ED) with suicidal ideation, psychosis, and acute dystonia. With the administration of intramuscular diphenhydramine at the ED, the dystonia resolved in less than an hour. To our knowledge, this is the first report on haloperidol misuse by smoking. Conclusion Clinicians should be aware that patients might misuse prescribed antipsychotics via unconventional routes, potentially combined with other substances.
“Nanopatterned and Nanoparticle-Modified Electrodes”

“Nanopatterned and Nanoparticle-Modified Electrodes” is the 17th monograph in the Advances in Electrochemical Science and Engineering series edited by Richard C. Alkire (University of Illinois, USA), Philip N. Bartlett (University of Southampton, UK) and Jacek Lipkowski (University of Guelph, Canada). This monograph covers the progress made in electrochemical nanoscience with an emphasis on applications in medicinal and analytical chemistry. There are ten chapters authored by experts in the respective fields covering areas including nanopatterned and nanoparticle modified electrodes for analytical detection, surface spectroscopy, electrocatalysis and influence of electrode structure on the properties.
Chitosan/Polyvinyl Alcohol/Lauramidopropyl Betaine/2D-HOF Mixed Film With Abundant Hydrogen Bonds Acts as High Mechanical Strength Artificial Skin.

The mechanical properties of artificial skins are complicated to maintain under ensuring air permeability and antimicrobial. Thus, a series of hydrophilic antimicrobial polymer networks are prepared by crosslinking chitosan and polyvinyl alcohol with the lauramidopropyl betaine and Hydrogen bond organic framework (CS/PVA/LPB/2D-HOF). The mechanical performance of the control groups and the complex are systematically evaluated to attain an artificial strength skin. The CS/PVA/LPB/2D-HOF complex exhibits strong mechanical abilities than other control groups. By analyzing the IR spectra and the morphology, The synergistic effect of hydrogen bonds between molecules and cracks significantly improves the mechanical properties of the complex. Its maximum tensile strength can reach 29 MPa, and its maximum load capacity can reach 3700 g. Notably, the composite membrane also performs an excellent antimicrobial activity. In vivo and in vitro experiments show that the hybrid membrane can promote tissue regeneration and wound healing (95༅). These results may open up the opportunity for future composite material investigations in the artificial skin and tissue engineering field. This article is protected by copyright. All rights reserved.
Intérêt du marquage par l’anticorps anti-CD207 (Langerine) pour différencier l’histiocytose langerhansienne cutanée d’une hyperplasie des cellules CD1a+ : application d’une nouvelle méthode de comptage des cellules

Introduction : l’hyperplasie des cellules dendritiques CD1a+ est frequente dans la peau, en particulier chez l’enfant, chez lequel elle peut poser de difficiles problemes diagnostiques avec une histiocytose langerhansienne (HL), neoplasie clonale potentiellement grave. Les marqueurs des cellules dendritiques CD1a et CD207 (Langerine) sont consideres comme specifiques des cellules de Langerhans et sont utiles pour confirmer un diagnostic d’HL. Le but de ce travail est de montrer l’interet du CD207, pour differencier ces deux entites, en comparant le nombre de cellules CD1a+/CD207+ et CD1a+/CD207- dans une serie de lesions inflammatoires versus d’HL a l’aide d’une nouvelle technique de comptage. Methode : 34 patients ont ete selectionnes comprenant 29 dermatoses inflammatoires et 5 HL. Les lames ont ete marquees avec les anticorps anti-CD1a et anti-CD207 puis numerisees pour visualisation et comptage par pointage des cellules marquees sur tablette numerique avec une application dediee. Resultats : le CD207 est exprime chez nos 5 temoins d’HL, parallelement au CD1a, sans difference significative. Les infiltrats de cellules dendritiques CD1a+ dans les dermatoses inflammatoires n’expriment pas ou peu le CD207, avec une difference statistiquement significative (p<0,05). Conclusion : le CD207 apparait comme une aide diagnostique robuste en cas de doute entre dermatoses inflammatoires riches en cellules dendritiques CD1a+ et HL. Cela renforce sa specificite comme marqueur immunohistochimique de l'HL.
Asterope: A Cross-Platform Optimization Method for Fast Memory Copy

Critical operations are often implemented in roughly the same way across multiple platforms, but differently by software systems running on the same platform. This observation is arguably justified by the potential restrictions of each software system, but it is surprising given the operation sensitivity to numerous platform-specific software and hardware parameters. With initial focus on the memory copy operation (memcpy), we introduce a methodology based on exhaustive search to optimize the performance across different platforms. We design and implement the Asterope algorithm to experimentally generate optimal memcpy parameters for two x86-64 processor models from different vendors. With experiments on microbenchmarks and two production systems, we demonstrate that Asterope respectively achieves up to 2.4x and 1.9x higher function and system performance in comparison to using the Linux kernel memcpy.
Sustainable Metallocavitand for Flue Gas-Selective Sorption: A Multiscale Study

We have used density functional theory, Grand canonical Monte Carlo (GCMC), and ideal adsorption solution theory (IAST) to understand the selective adsorption of flue within the cavity of porous metallocavitand pillarplex (PPX) molecule. Energies associated with the guest@PPX complex formation depict the effectiveness of encapsulation of guest within PPX. PPX is noted to have high selectivity toward the adsorption of Br2, HBr, CS2, H2S, and NO2 over their respective congeners. The strength of bonding and nature of the interaction is deciphered via quantum theory of atoms in molecule, noncovalent interaction, and energy decomposition analysis scheme. The molecules containing acidic hydrogen, viz., H2O, H2S, HF, HCl, and HBr, make hydrogen bonding with the −N atom of the pyrazole ring, and F2 and Cl2 make partially covalent bonding interaction with the −Au atom of PPX. The interaction is mostly of the van der Waals type, except in F2, Cl2, NO, and HF, in which the cumulative contribution of orbital, electro.
AnyCall: Fast and Flexible System-Call Aggregation

Operating systems rely on system calls to allow the controlled communication of isolated processes with the kernel and other processes. Every system call includes a processor mode switch from the unprivileged user mode to the privileged kernel mode. Although processor mode switches are the essential isolation mechanism to guarantee the system's integrity, they induce direct and indirect performance costs as they invalidate parts of the processor state. In recent years, high-performance networks and storage hardware has made the user/kernel transition overhead the bottleneck for IO-heavy applications. To make matters worse, security vulnerabilities in modern processors (e.g., Meltdown) have prompted kernel mitigations that further increase the transition overhead. To decouple system calls from user/kernel transitions we propose AnyCall, which uses an in-kernel compiler to execute safety-checked user bytecode in kernel mode. This allows for very fast system calls interleaved with error checking and processing logic using only a single user/kernel transition. We have implemented AnyCall based on the Linux kernel's extended Berkeley Packet Filter (eBPF) subsystem. Our evaluation demonstrates that system call bursts are up to 55 times faster using AnyCall and that real-world applications can be sped up by 24 % even if only a minimal part of their code is run by AnyCall.
Within-item response processes as indicators of test-taking effort and motivation

ABSTRACT The present study used process data from a computer-based problem-solving task as indications of behavioural level of test-taking effort, and explored how behavioural item-level effort related to overall test performance and self-reported effort. Variables were extracted from raw process data and clustered. Four distinct clusters were obtained and characterised as high effort, medium effort, low effort, and planner. Regression modelling indicated that among students that failed to solve the task, level of effort invested before giving up positively predicted overall test performance. Among students that solved the task, level of effort was instead weakly negatively related to test performance. A low level of behavioural effort before giving up the task was also related to lower self-reported effort. Results suggest that effort invested before giving up provides information about test-takers’ motivation to spend effort on the test. We conclude that process data could augment existing methods of assessing test-taking effort.