Miracle-dz/UniFAR-FTUs huggingface dataset:
Variational approach to the quantum separability problem

We present the variational separability veriﬁer (VSV), which is a novel variational quantum algorithm (VQA) that determines the closest separable state (CSS) of an arbitrary quantum state with respect to the Hilbert–Schmidt distance (HSD). We ﬁrst assess the performance of the VSV by investigating the convergence of the optimization procedure for Greenberger–Horne–Zeilinger (GHZ) states of up to seven qubits, using both statevector and shot-based simulations. We also numerically determine the CSS of maximally-entangled mixed X -states ( X -MEMS), and subsequently use the results of the algorithm to surmise the analytical form of the aforementioned CSS. Our results indicate that current noisy intermediate-scale quantum (NISQ) devices may be useful in addressing the NP -hard full separability problem using the VSV, due to the shallow quantum circuit imposed by employing the destructive SWAP test to evaluate the HSD. The VSV may also possibly lead to the characterization of multipartite quantum states, once the algorithm is adapted and improved to obtain the closest k -separable state ( k -CSS) of a multipartite entangled state.
Interactive Question Answering Systems: Literature Review

Question answering systems are recognized as popular and frequently effective means of information seeking on the web. In such systems, information seekers can receive a concise response to their query by presenting their questions in natural language. Interactive question answering is a recently proposed and increasingly popular solution that resides at the intersection of question answering and dialogue systems. On the one hand, the user can ask questions in normal language and locate the actual response to her inquiry; on the other hand, the system can prolong the question-answering session into a dialogue if there are multiple probable replies, very few, or ambiguities in the initial request. By permitting the user to ask more questions, interactive question answering enables users to dynamically interact with the system and receive more precise results. This survey offers a detailed overview of the interactive question-answering methods that are prevalent in current literature. It begins by explaining the foundational principles of question-answering systems, hence defining new notations and taxonomies to combine all identified works inside a unified framework. The reviewed published work on interactive question-answering systems is then presented and examined in terms of its proposed methodology, evaluation approaches, and dataset/application domain. We also describe trends surrounding specific tasks and issues raised by the community, so shedding light on the future interests of scholars. Our work is further supported by a GitHub page with a synthesis of all the major topics covered in this literature study. https://sisinflab.github. io/interactive-question-answering-systems-survey/
Series composition of simulation-based assume-guarantee contracts for linear dynamical systems

We present assume-guarantee contracts for continuous-time linear dynamical systems with inputs and outputs. These contracts are used to express specifications on the dynamic behaviour of a system. Contrary to existing approaches, we use simulation to compare the dynamic behaviour of two systems. This has the advantage of being supported by efficient numerical algorithms for verification as well as being related to the rich literature on (bi)simulation based techniques for verification and control, such as those based on (discrete) abstractions. Using simulation, we define contract implementation and a notion of contract refinement. We also define a notion of series composition for contracts, which allows us to reason about the series interconnection of systems on the basis of the contracts on its components. Together, the notions of refinement and composition allow contracts to be used for modular design and analysis of interconnected systems.
Online B-Matchings for Reconfigurable Datacenters: The Power of Randomization

This paper studies the problem of how to dynamically optimize the topology of a reconﬁgurable datacenter network in a demand-aware manner: by accounting for the current traﬃc pattern and providing direct connectivity between frequently communicating racks, the datacenter’s bandwidth eﬃciency can be improved. The underlying algorithmic challenge can be described as an online maximum weight b - matching problem, a generalization of maximum weight matching where each node has at most b ≥ 1 incident matching edges. Recently, Bienkowski et al. presented an O ( b )- competitive deterministic algorithm and showed that this is asymptotically optimal. This paper initiates the study of randomized algorithms, and we present a O (log b )-competitive solution and show that this is asymptotically optimal. We complement our theoretical results with extensive trace-driven simulations, based on real-world datacenter workloads.
Constraint of $\xi$-moments calculated with QCD sum rules on the pion distribution amplitude models

So far, the behavior of the pionic leading-twist distribution amplitude (DA) $\phi_{2;\pi}(x,\mu)$ $-$ which is universal physical quantity and enters the high-energy processes involving pion based on the factorization theorem $-$ has not been completely consistent. The form of $\phi_{2;\pi}(x,\mu)$ is usually described by phenomenological models and constrained by the experimental data of the exclusive processes containing pion or the moments calculated with the QCD sum rules and lattice QCD theory. Obviously, an appropriate model is very important for us to determine the exact behavior of $\phi_{2;\pi}(x,\mu)$. In this paper, by adopting the least squares method to fit the $\xi$-moments calculated with QCD sum rules based on the background field theory, we perform an analysis for several commonly used models of the pionic leading-twist DA in the literature, such as the truncation form of the Gegenbauer polynomial series, the light-cone harmonic oscillator model, the form from the Dyson-Schwinger equations, the model from the light-front holographic AdS/QCD and a simple power-law parametrization form. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.
Quasicriticality explains variability of human neural dynamics across life span

Aging impacts the brain's structural and functional organization and over time leads to various disorders, such as Alzheimer's disease and cognitive impairment. The process also impacts sensory function, bringing about a general slowing in various perceptual and cognitive functions. Here, we analyze the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) resting-state magnetoencephalography (MEG) dataset—the largest aging cohort available—in light of the quasicriticality framework, a novel organizing principle for brain functionality which relates information processing and scaling properties of brain activity to brain connectivity and stimulus. Examination of the data using this framework reveals interesting correlations with age and gender of test subjects. Using simulated data as verification, our results suggest a link between changes to brain connectivity due to aging and increased dynamical fluctuations of neuronal firing rates. Our findings suggest a platform to develop biomarkers of neurological health.
Character formulas in Category $\mathcal O_p$

Let O p denote the characteristic p > 0 version of the ordinary category O for a semisimple complex Lie algebra. In this paper we give some (formal) character formulas in O p . First we concentrate on the irreducible characters. Here we give explicit formulas for how to obtain all irreducible characters from the characters of the ﬁnitely many restricted simple modules as well as the characters of a small number of inﬁnite dimensional simple modules in O p with speciﬁed highest weights. We next prove a strong linkage principle for Verma modules which allow us to split O p into a ﬁnite direct sum of linkage classes. There are corresponding translation functors and we use these to further cut down the set of irreducible characters needed for determining all others. Then we show that the twisting functors on O carry over to twisting functors on O p , and as an application we prove a character sum formula for Jantzen-type ﬁltrations of Verma modules with antidominant highest weights. Finally, we record formulas relating the characters of the two kinds of tilting modules in O p .
A repository of automatic GUI test patterns in Android applications: Specification and Analysis using Alloy modeling language

The software industry aims to provide customers with quality software. Testing software is a critical and sensitive stage in ensuring software quality. Due to the increasing popularity of mobile devices, the use of Android applications has increased. Almost all are equipped with Graphical User Interface to interact with users or systems. GUI is the most common tool to communicate with modern software. Therefore, the perfect GUI is a GUI that ensures the safety, strength, and usability of the whole software system. The GUI testing is a vital stage in ensuring the product quality because the GUI is the user s first impression and the final view of the final product. This paper has proposed a new technique to promote the model based test efficiency using Alloy modeling language. The findings showed that this approach needs less configuration and modeling time than previous methods. Moreover, using GUI patterns may decrease errors and violations.
A Study on Representation Transfer for Few-Shot Learning

Few-shot classification aims to learn to classify new object categories well using only a few labeled examples. Transferring feature representations from other models is a popular approach for solving few-shot classification problems. In this work we perform a systematic study of various feature representations for few-shot classification, including representations learned from MAML, supervised classification, and several common self-supervised tasks. We find that learning from more complex tasks tend to give better representations for few-shot classification, and thus we propose the use of representations learned from multiple tasks for few-shot classification. Coupled with new tricks on feature selection and voting to handle the issue of small sample size, our direct transfer learning method offers performance comparable to state-of-art on several benchmark datasets.
Exploiting Fairness to Enhance Sensitive Attributes Reconstruction

In recent years, a growing body of work has emerged on how to learn machine learning models under fairness constraints, often expressed with respect to some sensitive attributes. In this work, we consider the setting in which an adversary has black-box access to a target model and show that information about this model's fairness can be exploited by the adversary to enhance his reconstruction of the sensitive attributes of the training data. More precisely, we propose a generic reconstruction correction method, which takes as input an initial guess made by the adversary and corrects it to comply with some user-defined constraints (such as the fairness information) while minimizing the changes in the adversary's guess. The proposed method is agnostic to the type of target model, the fairness-aware learning method as well as the auxiliary knowledge of the adversary. To assess the applicability of our approach, we have conducted a thorough experimental evaluation on two state-of-the-art fair learning methods, using four different fairness metrics with a wide range of tolerances and with three datasets of diverse sizes and sensitive attributes. The experimental results demonstrate the effectiveness of the proposed approach to improve the reconstruction of the sensitive attributes of the training set.
Single and Attractive: Uniqueness and Stability of Economic Equilibria under Monotonicity Assumptions

This paper characterizes equilibrium properties of a broad class of economic models that allow multiple heterogeneous agents to interact in heterogeneous manners across several markets. Our key contribution is a new theorem providing sufficient conditions for uniqueness and stability of equilibria in this class of models. To illustrate the applicability of our theorem, we characterize the general equilibrium properties of two commonly used quantitative trade models. Specifically, our analysis provides a first proof of uniqueness and stability of the equilibrium in multi-country trade models featuring (i) multiple sectors, or (ii) heterogeneity across countries in terms of their labor cost shares. These examples also provide a practical toolkit for future research on how our theorem can be applied to establish uniqueness and stability of equilibria in a broad set of economic models.
Classifying Spatial Trajectories

We provide the first comprehensive study on how to classify trajectories using only their spatial representations, measured on 5 real-world data sets. Our comparison considers 20 distinct classifiers arising either as a KNN classifier of a popular distance, or as a more general type of classifier using a vectorized representation of each trajectory. We additionally develop new methods for how to vectorize trajectories via a data-driven method to select the associated landmarks, and these methods prove among the most effective in our study. These vectorized approaches are simple and efficient to use, and also provide state-of-the-art accuracy on an established transportation mode classification task. In all, this study sets the standard for how to classify trajectories, including introducing new simple techniques to achieve these results, and sets a rigorous standard for the inevitable future study on this topic.
The complexity of learning from a mixture of labeled and unlabeled examples

The learning of a pattern classification rule rests on acquiring information to constitute a decision rule that is close to the optimal Bayes rule. Among the various ways of conveying information, showing the learner examples from the different classes is an obvious approach and ubiquitous in the pattern recognition field. Basically there are two types of examples: labeled in which the learner is provided with the correct classification of the example and unlabeled in which this classification is missing. Driven by the reality that often unlabeled examples are plentiful whereas labeled examples are difficult or expensive to acquire we explore the tradeoff between labeled and unlabeled sample complexities (the number of examples required to learn to within a specified error). This problem was posed in this form by T. M. Cover and may be succinctly, if inexactly, stated as follows: How many unlabeled examples is one labeled example worth? The direction taken here focuses on the archetypal problem of learning a classification problem with two pattern classes that are typified by feature vectors, i.e., examples drawn from class conditional Gaussian distributions and where the learning approaches are parametric (Maximum Likelihood Estimation) and nonparametric (Kernel Density Estimation). Denoting the dimensionality of the example-space as N, and the number of labeled and unlabeled examples as m and n respectively, then for specific algorithms, it is shown that under a nonparametric scenario the classification error probability decreases roughly as $O((c\sb0n\sp{-2/N})\sp{\rm{lg}N})+O(e\sp{-c\sb1m}),$ and in the parametric scenario the error decreases roughly as $O(N\sp{3/5}n\sp{-1/5})+O(e\sp{-c\sb1m}),$ where $c\sb0,\ c\sb1 >0$ are constants with respect to N, m, and n. This shows that in both scenarios exponentially more unlabeled examples are needed than labeled examples for the same reduction in error. When considering the effect of the dimensionality N, a labeled example is worth exponentially more in the nonparametric than in the parametric scenario. Extensions to the k-means procedure and Neural-Networks are also investigated.
A Case Study on the Classification of Lost Circulation Events During Drilling using Machine Learning Techniques on an Imbalanced Large Dataset

In this study, we present machine learning classification models that forecast and categorize lost circulation severity preemptively using a large class imbalanced drilling dataset with good prediction accuracy. We demonstrate reproducible core techniques involved in tackling a large drilling engineering challenge utilizing easily interpretable machine learning approaches. We utilized a dataset of over 65,000 records with class imbalance problem from Azadegan oilfield formations in Iran. Eleven of the dataset's seventeen parameters are chosen to be used in the classification of five lost circulation events. To generate classification models, we used six basic machine learning algorithms and four ensemble learning methods. Linear Discriminant Analysis (LDA), Logistic Regression (LR), Support Vector Machines (SVM), Classification and Regression Trees (CART), k-Nearest Neighbors (KNN), and Gaussian Naive Bayes (GNB) are the six fundamental techniques. We also used bagging and boosting ensemble learning techniques in the investigation of solutions for improved predicting performance. The performance of these algorithms is measured using four metrics: accuracy, precision, recall, and F1-score. The F1-score weighted to represent the imbalance in the number of instances in the categories of drilling fluid lost circulation events in the dataset is chosen as the preferred evaluation criterion. When the prediction performance of the six basic models was compared, the CART model was found to be the best in class for identifying drilling fluid circulation loss events with an average weighted F1-score of 0.9904 and standard deviation of 0.0015. Upon application of ensemble learning techniques, a Random Forest ensemble of decision trees showed the best predictive performance. It identified and classified lost circulation events with a perfect weighted F1-score of 1.0. Using Permutation Feature Importance (PFI), the measured depth was found to be the most influential factor in accurately recognizing lost circulation events while drilling. be able to better plan and conduct corrective actions before drilling fluid losses occur. This capability decreases drilling costs and accelerates resource recovery.
Distributed Coverage Control of Multi-Agent Systems with Load Balancing in Non-convex Environments

It is always a challenging task to service sudden events in non-convex and uncertain environments, and multi-agent coverage control provides a powerful theoretical framework to investigate the deployment problem of mobile robotic networks for minimizing the cost of handling random events. Inspired by the divide-and-conquer methodology, this paper proposes a novel coverage formulation to control multi-agent systems in the non-convex region while equalizing the workload among subregions. Thereby, a distributed coverage controller is designed to drive each agent towards the desired configurations that minimize the service cost by integrating with the rotational partition strategy. In addition, a circular search algorithm is proposed to identify optimal solutions to the problem of lowering service cost. Moreover, it is proved that this search algorithm enables to approximate the optimal configuration of multi-agent systems with the arbitrary small tolerance. Finally, numerical simulations are implemented to substantiate the efficacy of proposed coverage control approach.
511 keV line and primordial black holes from first-order phase transitions

511 keV gamma-ray excess from the Galactic center is a long lasting anomaly without satisfying astrophysical explanation. Hawking evaporation of hypothetical primordial black hole (PBH) with mass 1.0 × 10-17 ≲ M PBH/M ⊙ ≲ 8.0 × 10-17 and fractional abundance 10-3 ≲ f PBH ≲ 1.0, gives rise substantial non-relativistic electrons/positrons annihilating into diphoton, well reproduces the 511 keV line. However, it is obscure of the mechanism behind to form PBH with meteoritical mass in the early Universe. In this work, we investigated the production mechanism of PBHs through a cosmological first-order phase transition induced by quartic effective thermal potential via a scalar field in dark sector. We found the phase transition with vacuum energy, 𝒪(1) ≲ B 1/4/MeV ≲ 𝒪 (100), produces the desired monochromatic PBH mass and abundance fraction. Correlated signatures of gravitational wave and extragalactic gamma-ray, respectively coming from phase transition and black hole evaporation, are within μAres and AMEGO/e-ASTROGAM/COSI/XGIS-THESUS projected sensitivities. Finally, we include the PBH mass function from FOPT and found it can not improve the explanation to 511 keV excess.
Synthesis of Ultra-Incompressible Carbon Nitrides Featuring Three-Dimensional Frameworks of CN4 Tetrahedra Recoverable at Ambient Conditions

More than thirty years ago, carbon nitrides featuring 3D frameworks of tetrahedral CN 4 units were identified as one of the great aspirations of materials science, expected to have a hardness greater than or comparable to diamond 1 . Since then, no unambiguous experimental evidence of their existence has been delivered. Here, we report the high-pressure high-temperature synthesis of the long-sought-after covalent carbon nitrides, tI 14-C 3 N 4 , hP 126-C 3 N 4 , and tI 24-CN 2 , in laser-heated diamond anvil cells. Their structures were solved and refined using synchrotron single-crystal X-ray diffraction. In these solids, carbon atoms— all sp 3 -hybridized—and nitrogen atoms are fully saturated, forming four and three covalent bonds, respectively, leading to three-dimensional arrangements of corner-sharing CN 4 tetrahedra. These carbon nitrides are ultra-incompressible, with hP 126-C 3 N 4 and tI 24-CN 2 even rivalling diamond’s incompressibility, and superhard. These novel compounds are recoverable to ambient conditions in crystalline form and chemically stable in air. Being wide-band gap semiconductors with intriguing features in their electronic structure, they are expected to exhibit multiple exceptional functionalities besides their mechanical properties, opening new perspectives for materials science. dimers, and repeating in the [001] direction, forming square-shaped channels along this direction. The four C-N bonds of the CN 4 tetrahedra are identical within their uncertainty, with two of 1.379(7) Å and two others of 1.389(7) Å; bond lengths very similar to those of oP 8-CN, tI 14-C 3 N 4 and hP 126-C 3 N 4 . The N-C-N bond angles of the CN 4 tetrahedra vary between 104.0(3)° and 116.7(6)°, with the average value being 109.5(3)°—in line with sp 3 hybridization within regular CN 4 tetrahedra. The N-N bond length is 1.272(11) Å; slightly shorter than what is expected from a single bond in 3D nitrogen polymeric solids, found to have a length of 1.3410(15) Å in cg-N (122 GPa) 42 and of 1.338(6) Å and 1.435(7) Å in bp-N (140 GPa) 39 . Still at 134 GPa, tI 24-CN 2 has an average angle formed by the C/N-N-C bonds of 117.3(4)°, suggesting predominantly sp 2 -hybridization of nitrogen.