Miracle-dz/UniFAR-FTUs huggingface dataset:
Time- and Query-optimal Quantum Algorithms Based on Decision Trees

It has recently been shown that starting with a classical query algorithm (decision tree) and a guessing algorithm that tries to predict the query answers, we can design a quantum algorithm with query complexity O(√ GT where T is the query complexity of the classical algorithm (depth of the decision tree) and G is the maximum number of wrong answers by the guessing algorithm [3, 14]. In this article, we show that, given some constraints on the classical algorithms, this quantum algorithm can be implemented in time Õ(√ GT). Our algorithm is based on non-binary span programs and their efficient implementation. We conclude that various graph-theoretic problems including bipartiteness, cycle detection, and topological sort can be solved in time O(n3/2log2n) and with O(n3/2) quantum queries. Moreover, finding a maximal matching can be solved with O(n3/2) quantum queries in time O(n3/2log2n), and maximum bipartite matching can be solved in time O(n2log2n).
Deep Correlation Analysis for Audio-EEG Decoding

The electroencephalography (EEG), which is one of the easiest modes of recording brain activations in a non-invasive manner, is often distorted due to recording artifacts which adversely impacts the stimulus-response analysis. The most prominent techniques thus far attempt to improve the stimulus-response correlations using linear methods. In this paper, we propose a neural network based correlation analysis framework that significantly improves over the linear methods for auditory stimuli. A deep model is proposed for intra-subject audio-EEG analysis based on directly optimizing the correlation loss. Further, a neural network model with a shared encoder architecture is proposed for improving the inter-subject stimulus response correlations. These models attempt to suppress the EEG artifacts while preserving the components related to the stimulus. Several experiments are performed using EEG recordings from subjects listening to speech and music stimuli. In these experiments, we show that the deep models improve the Pearson correlation significantly over the linear methods (average absolute improvements of 7.4% in speech tasks and 29.3% in music tasks). We also analyze the impact of several model parameters on the stimulus-response correlation.
SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object Detection and 3D Mesh Reconstruction from Video Data

Extracting detailed 3D information of objects from video data is an important goal for holistic scene understanding. While recent methods have shown impressive results when reconstructing meshes of objects from a single image, results often remain ambiguous as part of the object is unobserved. Moreover, existing image-based datasets for mesh reconstruction don't permit to study models which integrate temporal information. To alleviate both concerns we present SAIL-VOS 3D: a synthetic video dataset with frame-by-frame mesh annotations which extends SAIL-VOS. We also develop first baselines for reconstruction of 3D meshes from video data via temporal models. We demonstrate efficacy of the proposed baseline on SAIL-VOS 3D and Pix3D, showing that temporal information improves reconstruction quality. Resources and additional information are available at http://sailvos.web. illinois.edu.
New magic textures of Majorana neutrinos and baryon asymmetry of the Universe

The magic texture is one of the successful textures of the flavor neutrino mass matrix for Majorana neutrinos. In this paper, it turns out that two new types of magic textures are also consistent with the neutrino oscillation experiments, observation of cosmic microwave background radiation, and neutrinoless double beta decay experiments. The connection between these new magic textures and the leptogenesis scenario of the origin of the baryon asymmetry of the Universe is also discussed.
On Convex Clustering Solutions

Convex clustering is an attractive clustering algorithm with favorable properties such as efficiency and optimality owing to its convex formulation. It is thought to generalize both k-means clustering and agglomerative clustering. However, it is not known whether convex clustering preserves desirable properties of these algorithms. A common expectation is that convex clustering may learn difficult cluster types such as non-convex ones. Current understanding of convex clustering is limited to only consistency results on well-separated clusters. We show new understanding of its solutions. We prove that convex clustering can only learn convex clusters. We then show that the clusters have disjoint bounding balls with significant gaps. We further characterize the solutions, regularization hyperparameters, inclusterable cases and consistency.
Stylized Story Generation with Style-Guided Planning

Current storytelling systems focus more ongenerating stories with coherent plots regard-less of the narration style, which is impor-tant for controllable text generation. There-fore, we propose a new task, stylized story gen-eration, namely generating stories with speci-fied style given a leading context. To tacklethe problem, we propose a novel generationmodel that first plans the stylized keywordsand then generates the whole story with theguidance of the keywords. Besides, we pro-pose two automatic metrics to evaluate theconsistency between the generated story andthe specified style. Experiments demonstratesthat our model can controllably generateemo-tion-driven orevent-driven stories based onthe ROCStories dataset (Mostafazadeh et al.,2016). Our study presents insights for stylizedstory generation in further research.
Can we assess research using open scientific knowledge graphs? A case study within the Italian National Scientific Qualification

The need for open scientific knowledge graphs is ever increasing. While there are large repositories of open access articles and free publication indexes, there are still few free knowledge graphs exposing citation networks, and often their coverage is partial. Consequently, most evaluation processes based on citation counts rely on commercial citation databases. Things are changing thanks to the Initiative for Open Citations (I4OC, ​https://i4oc.org​) and the Initiative for Open Abstracts (I4OA, ​https://i4oa.org​), whose goal is to campaign for scholarly publishers to open the reference lists and the other metadata of their articles. This paper investigates the growth of the open bibliographic metadata and open citations in two scientific knowledge graphs, OpenCitations’ COCI and Crossref, with an experiment on the Italian National Scientific Qualification (NSQ), the National process for University Professor qualification which uses data from commercial indexes. We simulated the procedure by only using such open data and explored similarities and differences with the official results. The outcomes of the experiment show that the amount of open bibliographic metadata and open citation data currently available in the two scientific knowledge graphs adopted is not yet enough for obtaining results similar to those provided using commercial databases.
Contrastive Model Inversion for Data-Free Knowledge Distillation

Model inversion, whose goal is to recover training data from a pre-trained model, has been recently proved feasible. However, existing inversion methods usually suffer from the mode collapse problem, where the synthesized instances are highly similar to each other and thus show limited effectiveness for downstream tasks, such as knowledge distillation. In this paper, we propose Contrastive Model Inversion~(CMI), where the data diversity is explicitly modeled as an optimizable objective, to alleviate the mode collapse issue. Our main observation is that, under the constraint of the same amount of data, higher data diversity usually indicates stronger instance discrimination. To this end, we introduce in CMI a contrastive learning objective that encourages the synthesizing instances to be distinguishable from the already synthesized ones in previous batches. Experiments of pre-trained models on CIFAR-10, CIFAR-100, and Tiny-ImageNet demonstrate that CMI not only generates more visually plausible instances than the state of the arts, but also achieves significantly superior performance when the generated data are used for knowledge distillation. Code is available at \url{https://github.com/zju-vipa/DataFree}.
Phase-sensitive quantum spectroscopy with high-frequency resolution

Classical sensors for spectrum analysis are widely used but lack micro- or nanoscale spatial resolution. On the other hand, quantum sensors, capable of working with nanoscale precision, do not provide precise frequency resolution over a wide range of frequencies. Using a single spin in diamond, we present a measurement protocol for quantum probes which enables full signal reconstruction on a nanoscale spatial resolution up to potentially 100\,GHz. We achieve $58\,\mathrm{nT/\sqrt{Hz}}$ amplitude and $0.095\,\mathrm{rad/\sqrt{Hz}}$ phase sensitivity and a relative frequency uncertainty of $10^{-12}$ for a $1.51\,\mathrm{GHz}$ signal within $10\,\mathrm{s}$ of integration. This technique opens the way to quantum spectrum analysis methods with potential applications in electron spin detection and nanocircuitry in quantum technologies.
Safe Occlusion-aware Autonomous Driving via Game-Theoretic Active Perception

Autonomous vehicles interacting with other traffic participants heavily rely on the perception and prediction of other agents' behaviors to plan safe trajectories. However, as occlusions limit the vehicle's perception ability, reasoning about potential hazards beyond the field of view is one of the most challenging issues in developing autonomous driving systems. This paper introduces a novel analytical approach that poses safe trajectory planning under occlusions as a hybrid zero-sum dynamic game between the autonomous vehicle (evader) and an initially hidden traffic participant (pursuer). Due to occlusions, the pursuer's state is initially unknown to the evader and may later be discovered by the vehicle's sensors. The analysis yields optimal strategies for both players as well as the set of initial conditions from which the autonomous vehicle is guaranteed to avoid collisions. We leverage this theoretical result to develop a novel trajectory planning framework for autonomous driving that provides worst-case safety guarantees while minimizing conservativeness by accounting for the vehicle's ability to actively avoid other road users as soon as they are detected in future observations. Our framework is agnostic to the driving environment and suitable for various motion planners. We demonstrate our algorithm on challenging urban and highway driving scenarios using the open-source CARLA simulator.
Learning-Based Robust Resource allocation for D2D Underlaying Cellular Network

In this paper, we study the resource allocation in D2D underlaying cellular network with uncertain channel state information (CSI). For satisfying the minimum rate requirement for cellular user and the reliability requirement for D2D user, we attempt to maximize the cellular user’s throughput whilst ensuring a chance constraint for D2D. Then, a robust resource allocation framework is proposed for solving the highly intractable chance constraint, where the CSI uncertainties are represented as a deterministic set and the reliability requirement is enforced to hold for any CSI within it. Then, a symmetrical-geometry-based learning approach is developed to model the uncertain CSI into polytope, ellipsoidal and box. After that, the chance constraint under these uncertainty sets is transformed into computation convenient convex constraints. To overcome the conservatism of symmetrical-geometry-based approach, we develop a support vector clustering (SVC)-based approach to model uncertain CSI as a compact convex uncertainty set. Based on that, the chance constraint is converted into a linear convex set. Then, we develop a bisection search-based power allocation algorithm for solving the resource allocation in D2D underlaying cellular network with the obtained convex constraints. Finally, we conduct the simulation to compare the proposed robust optimization approaches with the non-robust one.
Spectral and timing evolution of MAXI J1631–479 during the 2018–19 outburst with NICER

The X-ray transient MAXI J1631–479 went into outburst on 2018 December 21 and remained active for about seven months. Owing to various constraints it was monitored by NICER only during the decay phase of the outburst for about four months. The NICER observations were primarily in the soft state with a brief excursion to the hard intermediate state. While the soft state spectrum was dominated by thermal disk emission, the hard intermediate state spectrum had maximum contribution from the thermal Comptonization. Almost all intermediate-state power spectra had a Type-C low frequency quasi-periodic oscillation (within 4 10 Hz), often accompanied by a harmonic component. The frequency of these oscillations increased and the fractional rms decreased with inner-disk temperature suggesting a geometric origin. One observation in the middle of the outburst during the hard intermediate state had two non-harmonically related quasi-periodic oscillations. The rms versus QPO frequency relation suggests that while the higher frequency peak is definitely of Type-C, the lower frequency peak could be Type-B in nature. The rms spectra during the intermediate state had a hard shape from above 1 keV. Below 1 keV the shape could not be constrained in most cases, while only a few observations showed a rise in amplitude.
IN LIQUID NITROGEN

tained after thawing. There are exceptions, however, and some strains of species, often in the classes of Phycomycetes or Basidiomycetes, have exhibited negative or very weak viability following the freezing procedure. Details of the preservation methods employed by the American Type Culture Collection (ATCC) have been published elsewhere (Butterfield et al., 1974). The use of 2.0-ml screw-top polypropylene vials (Vangard International, Neptune, N. J.) for the past year has proved to be successful in freezing many of these problem strains. TABLE I lists representative strains of fungi that have shown vigorous viability after freezing in plastic vials and negative or very weak viability after freezing in glass ampoules. Several species of Pythium which formerly required growth on sterile hemp seeds to survive freezing, now can be grown on Difco #0386 cornmeal-agar plates (ATCC medium #307) and discs of the agar containing the organism can be frozen. This is a somewhat more convenient and time-saving method than that involving the hemp seeds. The sterile screw-cap vials are easily handled and may be dropped without breaking. However, they must be stored in the vapor phase of the liquid nitrogen refrigerators, because the liquid nitrogen can enter the vial around the threads of the cap when immersing in the liquid phase. Recently Elliott (1976) reported using polypropylene drinking straws for storage of strains of Agaricus bisporus (Lange) Imbach in liquid nitrogen. Like glass ampoules, they must be sealed and tested for leaks before storage in liquid nitrogen.
The Relationship between Internet Addiction and Insomnia in Student Class IX

Introduction: Internet addiction can cause unfulfilled hours of sleep, which if left unchecked will lead to disruption of sleep patterns often called insomnia. The purpose of the research was to determine the relationship between internet addiction and insomnia.Methods: The design of the research was descriptive-analytic with a cross-sectional approach. The population in the research consisted of as many as 217 respondents with a sample size of 141 respondents. The variables used in the research were insomnia as the dependent variable and internet addiction as the independent one. The instrument used was a questionnaire with a univariate and bivariate analysis.Results: The results of the study used the Spearman Rho test with a value of p = 0,000.Conclusion: From these results, it could be concluded that there is a relationship between internet addiction and the incidence of insomnia. Internet addiction can cause unfulfilled hours of sleep, which if left unchecked will lead to disturbed sleep patterns often called insomnia. The research suggests that students pay more attention to their health by limiting or reducing the use of the internet for unimportant issues as it can reduce learning productivity and academic value and cause insomnia.
Fission Product Separation from Pyrochemical Electrolyte by Cold Finger Melt Crystallization

This work contributes to the development of pyroprocessing technology as an economically viable means of separating used nuclear fuel from fission products and cladding materials. Electrolytic oxide reduction is used as a head-end step before electrorefining to reduce oxide fuel to metallic form. The electrolytic medium used in this technique is molten LiCl-Li2O. Groups I and II fission products, such as cesium (Cs) and strontium (Sr), have been shown to partition from the fuel into the molten LiCl-Li2O. Various approaches of separating these fission products from the salt have been investigated by different research groups. One promising approach is based on a layer crystallization method studied at the Korea Atomic Energy Research Institute (KAERI). Despite successful demonstration of this basic approach, there are questions that remain, especially concerning the development of economical and scalable operating parameters based on a comprehensive understanding of heat and mass transfer. This research explores these parameters through a series of experiments in which LiCl is purified, by concentrating CsCl in a liquid phase as purified LiCl is crystallized and removed via an argon-cooled cold finger.
The use of intelligent technologies of Business Intelligence Platforms and Data Science and Machine Learning Platforms for monitoring the socio-economic indicators of the administrative districts of Moscow

The article discusses the possibilities of studying the state of the social sphere according to the repository of the Moscow Government open data portal by administrative districts and city districts using Business Intelligence Platforms and Data Science and Machine Learning Platforms intellectual technologies. Opportunities are presented for using machine learning technologies for business analytics platforms to identify hidden patterns in order to make informed management decisions.
Effect of Calcium Fertilization on Processing Properties and Storability of Frozen French Fries

The United Graduate School of Agricultural Science, Iwate University, 3-18-8 Ueda, Morioka, Iwate, Japan Department of Food Science, Obihiro University of Agriculture and Veterinary Medicine, West 2-11, Inada, Obihiro, Hokkaido, Japan Department of Horticulture, University of Wisconsin–Madison, Madison, Wisconsin, USA Research Center for Global Agromedicine, Obihiro University of Agriculture and Veterinary Medicine, Obihiro, Hokkaido, Japan
Acquired Refractory Iron Deficiency Anemia

Anemia is a global health problem affecting one-third of the world population, and half of the cases are due to iron deficiency (ID). Iron deficiency anemia (IDA) is the leading cause of disability in several countries. Although multiple mechanisms may coexist, ID and IDA causes can be classified as i) insufficient iron intake for the body requirement, ii) reduced absorption, and iii) blood losses. Oral iron represents the mainstay of IDA treatment. IDA is defined as “refractory” when the hematologic response after 4 to 6 weeks of treatment with oral iron (an increase of >=1 g/dL of Hb) is absent. The cause of iron-refractory anemia is usually acquired and frequently related to gastrointestinal pathologies, although a rare genetic form called iron-refractory iron deficiency anemia (IRIDA) exists. In some pathological circumstances, either genetic or acquired, hepcidin increases, limiting the absorption in the gut, remobilization, and recycling of iron, thereby reducing iron plasma levels. Indeed, conditions with high hepcidin levels are often under-recognized as iron refractory, leading to inappropriate and unsuccessful treatments. This review provides an overview of the iron refractory anemia underlying conditions, from gastrointestinal pathologies to hepcidin dysregulation and iatrogenic or provoked conditions, and the specific diagnostic and treatment approach.
Potential Benefits of Pre-emptive PGx Testing in Male Breast Cancer Patients

Abstract The objective is to report a case of recurrent breast cancer in a poor CYP2D6 metabolizer male patient on tamoxifen, and how pharmacogenomic (PGx) testing can play an important role in selecting appropriate adjuvant endocrine therapy. The case examined here is a 60-year-old white male diagnosed with recurrence of breast cancer. The patient was prescribed tamoxifen four years prior as adjuvant endocrine therapy after initial treatment with surgery. PGx testing ordered at the time of recurrence revealed patient is a poor metabolizer of CYP2D6, which may decrease the efficacy of tamoxifen. The results prompted a change in therapy to an aromatase inhibitor (AI). This case illustrates the potential benefits of preemptive PGx testing in a male breast cancer patient to assist in selecting appropriate adjuvant therapy based on how the patient metabolizes medications. In addition, PGx testing encourages patient involvement by emphasizing the association of genetics in determining treatment. The ultimate goal in performing these tests is to individualize treatment to improve safety and efficacy while minimizing adverse drug reactions.
Managing Genetic Hemochromatosis: An Overview of Dietary Measures, Which May Reduce Intestinal Iron Absorption in Persons With Iron Overload

Genetic hemochromatosis causes iron overload by excess absorption of dietary iron, due to a decreased expression of hepcidin. The objective was to elaborate dietary recommendations that can reduce intestinal iron absorption in hemochromatosis patients, based on our present knowledge of the iron contained in nutrients and the mechanisms of iron uptake. This is a narrative review. Literature search in PubMed and Google Scholar of papers dealing with iron absorption from the diet was conducted. Most important proposed dietary recommendations are: 1) Choose a varied vegetarian, semi-vegetarian or flexitarian diet. A “veggie-lacto-ovo-poultry-pescetarian” diet seems optimal. Avoid iron enriched foods and iron supplements. 2) Eat many vegetables and fruits, at least 600 g per day. Choose protein rich pulses and legumes (e.g., kidney- and soya beans). Fresh fruits should be eaten between meals. 3) Abstain from red meat from mammals and choose the lean, white meat from poultry. Avoid processed meat, offal and blood containing foods. Eat no more than 200 g meat from poultry per week. Choose fish, eggs, vegetables and protein rich legumes the other days. Eat fish two to four times a week as main course, 350 - 500 g fish per week, of which half should be fat fish. 4) Choose whole grain products in cereals and bread. Avoid iron enriched grains. Choose non-sourdough, yeast-fermented bread with at least 50% whole grain. 5) Choose vegetable oils, and low-fat dairy products. 6) Eat less sugar and salt. Choose whole foods and foods with minimal processing and none or little added sugar or salt. 7) Quench your thirst in water. Drink green- or black tea, coffee, or low-fat milk with the meals, alternatively water or non-alcoholic beer. Fruit juices must be consumed between meals. Abstain from alcoholic beverages. Drink soft drinks, non-alcoholic beer, or non-alcoholic wine instead. These advices are close to the official Danish dietary recommendations in 2021. In the management of hemochromatosis, dietary modifications that lower iron intake and decrease iron bioavailability may provide additional measures to reduce iron uptake from the foods and reduce the number of phlebotomies. However, there is a need for large, prospective, randomized studies that specifically evaluate the effect of dietary interventions.