Miracle-dz/UniFAR-FTUs huggingface dataset:
Classical analog of quantum models in synthetic dimensions

We introduce a classical analog of quantum matter in ultracold molecule -- or Rydberg atom -- synthetic dimensions, extending the Potts model to include interactions J1 between atoms adjacent in both real and synthetic space and studying its finite temperature properties. For intermediate values of J1, the resulting phases and phase diagrams are similar to those of the clock and Villain models, in which three phases emerge. There exists a sheet phase analogous to that found in quantum synthetic dimension models between the high temperature disordered phase and the low temperature ferromagnetic phase. We also employ machine learning to uncover non-trivial features of the phase diagram using the learning by confusion approach. The key result there is that the method is able to discern several successive phase transitions.
Toroidal Coordinates: Decorrelating Circular Coordinates With Lattice Reduction

The circular coordinates algorithm of de Silva, Morozov, and Vejdemo-Johansson takes as input a dataset together with a cohomology class representing a $1$-dimensional hole in the data; the output is a map from the data into the circle that captures this hole, and that is of minimum energy in a suitable sense. However, when applied to several cohomology classes, the output circle-valued maps can be"geometrically correlated"even if the chosen cohomology classes are linearly independent. It is shown in the original work that less correlated maps can be obtained with suitable integer linear combinations of the cohomology classes, with the linear combinations being chosen by inspection. In this paper, we identify a formal notion of geometric correlation between circle-valued maps which, in the Riemannian manifold case, corresponds to the Dirichlet form, a bilinear form derived from the Dirichlet energy. We describe a systematic procedure for constructing low energy torus-valued maps on data, starting from a set of linearly independent cohomology classes. We showcase our procedure with computational examples. Our main algorithm is based on the Lenstra--Lenstra--Lov\'asz algorithm from computational number theory.
Reinforcement Learning in System Identification

System identification, also known as learning forward models, transfer functions, system dynamics, etc., has a long tradition both in science and engineering in different fields. Particularly, it is a recurring theme in Reinforcement Learning research, where forward models approximate the state transition function of a Markov Decision Process by learning a mapping function from current state and action to the next state. This problem is commonly defined as a Supervised Learning problem in a direct way. This common approach faces several difficulties due to the inherent complexities of the dynamics to learn, for example, delayed effects, high non-linearity, non-stationarity, partial observability and, more important, error accumulation when using bootstrapped predictions (predictions based on past predictions), over large time horizons. Here we explore the use of Reinforcement Learning in this problem. We elaborate on why and how this problem fits naturally and sound as a Reinforcement Learning problem, and present some experimental results that demonstrate RL is a promising technique to solve these kind of problems.
Simplification of Forest Classifiers and Regressors

We study the problem of sharing as many branching conditions of a given forest classifier or regressor as possible while keeping classification performance. As a constraint for preventing from accuracy degradation, we first consider the one that the decision paths of all the given feature vectors must not change. For a branching condition that a value of a certain feature is at most a given threshold, the set of values satisfying such constraint can be represented as an interval. Thus, the problem is reduced to the problem of finding the minimum set intersecting all the constraint-satisfying intervals for each set of branching conditions on the same feature. We propose an algorithm for the original problem using an algorithm solving this problem efficiently. The constraint is relaxed later to promote further sharing of branching conditions by allowing decision path change of a certain ratio of the given feature vectors or allowing a certain number of non-intersected constraint-satisfying intervals. We also extended our algorithm for both the relaxations. The effectiveness of our method is demonstrated through comprehensive experiments using 21 datasets (13 classification and 8 regression datasets in UCI machine learning repository) and 4 classifiers/regressors (random forest, extremely randomized trees, AdaBoost and gradient boosting).
AI-enabled exploration of Instagram profiles predicts soft skills and personality traits to empower hiring decisions

It does not matter whether it is a job interview with Tech Giants, Wall Street firms, or a small startup; all candidates want to demonstrate their best selves or even present themselves better than they really are. Meanwhile, recruiters want to know the candidates' authentic selves and detect soft skills that prove an expert candidate would be a great fit in any company. Recruiters worldwide usually struggle to find employees with the highest level of these skills. Digital footprints can assist recruiters in this process by providing candidates' unique set of online activities, while social media delivers one of the largest digital footprints to track people. In this study, for the first time, we show that a wide range of behavioral competencies consisting of 16 in-demand soft skills can be automatically predicted from Instagram profiles based on the following lists and other quantitative features using machine learning algorithms. We also provide predictions on Big Five personality traits. Models were built based on a sample of 400 Iranian volunteer users who answered an online questionnaire and provided their Instagram usernames which allowed us to crawl the public profiles. We applied several machine learning algorithms to the uniformed data. Deep learning models mostly outperformed by demonstrating 70% and 69% average Accuracy in two-level and three-level classifications respectively. Creating a large pool of people with the highest level of soft skills, and making more accurate evaluations of job candidates is possible with the application of AI on social media user-generated data.
Verifying Term Graph Optimizations using Isabelle/HOL

Our objective is to formally verify the correctness of the hundreds of expression optimization rules used within the GraalVM compiler. When defining the semantics of a programming language, expressions naturally form abstract syntax trees, or, terms. However, in order to facilitate sharing of common subexpressions, modern compilers represent expressions as term graphs. Defining the semantics of term graphs is more complicated than defining the semantics of their equivalent term representations. More significantly, defining optimizations directly on term graphs and proving semantics preservation is considerably more complicated than on the equivalent term representations. On terms, optimizations can be expressed as conditional term rewriting rules, and proofs that the rewrites are semantics preserving are relatively straightforward. In this paper, we explore an approach to using term rewrites to verify term graph transformations of optimizations within the GraalVM compiler. This approach significantly reduces the overall verification effort and allows for simpler encoding of optimization rules.
3D Neuron Morphology Analysis

We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on neuron shapes. In neuroscience research, the skeleton representation is often used as a compact and abstract representation of neuron shapes. However, existing methods are limited to getting and analyzing”curve”skeletons which can only be applied for tubular shapes. This paper presents a 3D neuron morphology analysis method for more general and complex neuron shapes. First, we introduce the concept of skeleton mesh to represent general neuron shapes and propose a novel method for computing mesh representations from 3D surface point clouds. A skeleton graph is then obtained from skeleton mesh and is used to extract sub-cellular features. Finally, an unsupervised learning method is used to embed the skeleton graph for neuron classification. Extensive experiment results are provided and demonstrate the robustness of our method to analyze neuron morphology.
Know What You Don't Know: Consistency in Sliding Window Filtering With Unobservable States Applied to Visual-Inertial SLAM

Estimation algorithms, such as the sliding window filter, produce an estimate and uncertainty of desired states. This task becomes challenging when the problem involves unobservable states. In these situations, it is critical for the algorithm to “know what it doesn't know”, meaning that it must maintain the unobservable states as unobservable during algorithm deployment. This letter presents general requirements for maintaining consistency in sliding window filters involving unobservable states. The value of these requirements for designing navigation solutions is experimentally shown within the context of visual-inertial SLAM making use of IMU preintegration.
Modeling Supernovae as an Optically Thick Fireball

We calculate the properties of 135 stellar supernovae using data from the Open Supernova Catalog. We generate temperatures, radii, luminosities, and expansion velocities using a spherically symmetric optically thick fireball model. These modeled parameters reveal trends that are common across different types of supernovae. We have identified distinct phases that appear across Type Ia, II, II P, and IIb supernovae. We note that there is a long period of reasonable continuous growth (Phase 1), giving credence to our simple model of an optically thick fireball. The modeled radius reaches a maximum value beyond which it is flat or decreases (Phase 2). The temperature we observe at the maximum modeled radius, 4500 K, suggests that the loss of opacity due to electron recombination sets the timeline where our optically thick model no longer applies. We observe the fastest modeled fireball velocities, largest modeled fireball radii, and maximum modeled luminosities for Type Ia supernovae. As a group, Type Ia supernovae reach a maximum luminosity that is 8.5 times more luminous than Type II supernovae. We present a summary table that contains modeled parameters of supernovae and their timings by supernova classification type.
Pressure dependence of superconductivity in 
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In the recently discovered heavy fermion superconductor CeRh2As2, a magnetic field induced phase transition has been observed inside the superconducting state, which is proposed to be a transition from an even to an odd-parity superconducting state. The odd-parity superconducting state and its large upper critical field has been explained by local inversion symmetry breaking and consequent Rashba spin-orbit coupling. Here we report the experimental tuning of the parity of superconductivity in CeRh2As2 via applied pressure. Superconductivity is enhanced by pressure after its initial suppression, forming a second dome. However, the odd-parity state only exists in the first dome and the second dome is dominated by the even-parity state. This parity switch of superconductivity under pressure is consistent with the competition between Rashba spin-orbit coupling and interlayer hopping in determining the parity of superconductivity in systems with local inversion symmetry breaking. Our results provide insightful guidance on how to look for new odd-parity superconductors.
Connected correlations in partitioning protocols: A case study and beyond

The assumption of local relaxation in inhomogeneous quantum quenches allows to compute asymptotically the expectation value of local observables via hydrodynamic arguments known as generalized hydrodynamics (GHD). In this work we address formally the question of when an observable is local enough to be described by GHD using the playground of partitioning protocols and non-interacting time evolution. We show that any state evolving under a quadratic Hamiltonian can be described via a set of decoupled dynamical fields such that one of those fields can be identified with a space-time-dependent generalisation of the root density. By studying the contribution to a connected spin correlation of each of those fields independently, we derive the locality conditions under which an observable can be described using the root density only. That shows both the regime of validity for hydrodynamic approaches that aim at describing the asymptotic value of observables in term of the root density only, such as GHD, and the locality conditions necessary for Gaussianification to occur.
Trapping of Positrons at Vacancies in Metals

Measurements of the lifetime for positron annihilation in metals show a temperature dependence which has been ascribed to the trapping of positrons at vacancies. An explanation of this trapping process is presented in terms of an effective potential well, whose depth is the difference between the bottom of the positron energy band in the solid and the potential felt by the positron inside the vacancy.
Sequential Kernelized Independence Testing

Independence testing is a classical statistical problem that has been extensively studied in the batch setting when one fixes the sample size before collecting data. However, practitioners often prefer procedures that adapt to the complexity of a problem at hand instead of setting sample size in advance. Ideally, such procedures should (a) stop earlier on easy tasks (and later on harder tasks), hence making better use of available resources, and (b) continuously monitor the data and efficiently incorporate statistical evidence after collecting new data, while controlling the false alarm rate. Classical batch tests are not tailored for streaming data: valid inference after data peeking requires correcting for multiple testing which results in low power. Following the principle of testing by betting, we design sequential kernelized independence tests that overcome such shortcomings. We exemplify our broad framework using bets inspired by kernelized dependence measures, e.g., the Hilbert-Schmidt independence criterion. Our test is also valid under non-i.i. d. , time-varying settings. We demonstrate the power of our approaches on both simulated and real data.
Market Mechanisms for Low-Carbon Electricity Investments: A Game-Theoretical Analysis

Electricity markets are transforming from the dominance of conventional energy resources (CERs), e.g., fossil fuels, to low-carbon energy resources (LERs), e.g., renewables and energy storage. This work examines market mechanisms to incentivize LER investments, while ensuring adequate market revenues for investors, guiding investors' strategic investments towards social optimum, and protecting consumers from scarcity prices. To reduce the impact of excessive scarcity prices, we present a new market mechanism, which consists of a Penalty payment for lost load, a supply Incentive, and an energy price Uplift (PIU). We establish a game-theoretical framework to analyze market equilibrium. We prove that one Nash equilibrium under the penalty payment and supply incentive can reach the social optimum given quadratic supply costs of CERs. Although the price uplift can ensure adequate revenues, the resulting system cost deviates from the social optimum while the gap decreases as more CERs retire. Furthermore, under the traditional marginal-cost pricing (MCP) mechanism, investors may withhold investments to cause scarcity prices, but such behavior is absent under the PIU mechanism. Simulation results show that the PIU mechanism can reduce consumers' costs by over 30% compared with the MCP mechanism by reducing excessive revenues of low-cost CERs from scarcity prices.
Interactive Learning with Pricing for Optimal and Stable Allocations in Markets

Large-scale online recommendation systems must facilitate the allocation of a limited number of items among competing users while learning their preferences from user feedback. As a principled way of incorporating market constraints and user incentives in the design, we consider our objectives to be two-fold: maximal social welfare with minimal instability. To maximize social welfare, our proposed framework enhances the quality of recommendations by exploring allocations that optimistically maximize the rewards. To minimize instability, a measure of users' incentives to deviate from recommended allocations, the algorithm prices the items based on a scheme derived from the Walrasian equilibria. Though it is known that these equilibria yield stable prices for markets with known user preferences, our approach accounts for the inherent uncertainty in the preferences and further ensures that the users accept their recommendations under offered prices. To the best of our knowledge, our approach is the first to integrate techniques from combinatorial bandits, optimal resource allocation, and collaborative filtering to obtain an algorithm that achieves sub-linear social welfare regret as well as sub-linear instability. Empirical studies on synthetic and real-world data also demonstrate the efficacy of our strategy compared to approaches that do not fully incorporate all these aspects.
M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient Supervision

Automated medical image segmentation using deep neural networks typically requires substantial supervised training. However, these models fail to generalize well across different imaging modalities. This shortcoming, amplified by the limited availability of expert annotated data, has been hampering the deployment of such methods at a larger scale across modalities. To address these issues, we propose M-GenSeg, a new semi-supervised generative training strategy for cross-modality tumor segmentation on unpaired bi-modal datasets. With the addition of known healthy images, an unsupervised objective encourages the model to disentangling tumors from the background, which parallels the segmentation task. Then, by teaching the model to convert images across modalities, we leverage available pixel-level annotations from the source modality to enable segmentation in the unannotated target modality. We evaluated the performance on a brain tumor segmentation dataset composed of four different contrast sequences from the public BraTS 2020 challenge data. We report consistent improvement in Dice scores over state-of-the-art domain-adaptive baselines on the unannotated target modality. Unlike the prior art, M-GenSeg also introduces the ability to train with a partially annotated source modality.
Approximate Discrete Fréchet distance: simplified, extended and structured

The Fréchet distance is one of the most studied distance measures between curves P and Q . The data structure variant of the problem is a longstanding open problem: Eﬃciently preprocess P , so that for any Q given at query time, one can eﬃciently approximate their Fréchet distance. There exist conditional lower bounds that prohibit (1 + ε )-approximate Fréchet distance computations in subquadratic time, even when preprocessing P using any polynomial amount of time and space. As a consequence, the problem has been studied under various restrictions: restricting Q to be a (horizontal) segment, or requiring P and Q to be so-called realistic input curves. We give a data structure for (1 + ε )-approximate discrete Fréchet distance in any metric space X between a realistic input curve P and any query curve Q . After preprocessing the input curve P (of length | P | = n ) in O ( n log n ) time, we may answer queries specifying a query curve Q and an ε , and output a value d ( P, Q ) which is at most a (1 + ε )-factor away from the true Fréchet distance between Q and P . Thus, we give the ﬁrst data structure that adapts to ε -values speciﬁed at query time, and the ﬁrst data structure to handle query curves with arbitrarily many vertices. Our query time is asymptotically linear in | Q | = m , 1 ε , log n , and the realism parameter c or κ . The method presented in this paper simpliﬁes and generalizes previous contributions to the static problem variant. Particularly, we show how to go from approximately deciding the Fréchet distance, to computing an approximate value without the (typically used) Well Separated Pair Decomposition of ( P, Q ), which has O (2 d n + dn log n ) construction time in R d , or parametric search, which, when applied to sublinear decision algorithms, commonly squares the decision time. We obtain subquadratic total running time in any metric space, even R d for non-constant d . Our algorithm takes into account that one may not have oracle-access to exact distances in X , assuming oracle-access to approximate distances for some parameterised query cost. We obtain eﬃcient queries (and therefore static algorithms) for Fréchet distance computation in high-dimensional spaces and other metric spaces (e
On the fundamental groups of subelliptic varieties

We show that the fundamental group of any smooth subelliptic variety is ﬁnite. Moreover, it is also proved that every ﬁnite group can be realized as the fundamental group of a smooth subelliptic variety. As a consequence, it follows that there exists a smooth subelliptic variety homotopy equivalent to the n -sphere if and only if n > 1. This result can be considered as a negative answer to the algebraic version of Gromov’s problem on the homotopy types of Oka manifolds.
Probing Deep Speaker Embeddings for Speaker-related Tasks

Deep speaker embeddings have shown promising results in speaker recognition, as well as in other speaker-related tasks. However, some issues are still under explored, for instance, the information encoded in these representations and their influence on downstream tasks. Four deep speaker embeddings are studied in this paper, namely, d-vector, x-vector, ResNetSE-34 and ECAPA-TDNN. Inspired by human voice mechanisms, we explored possibly encoded information from perspectives of identity, contents and channels; Based on this, experiments were conducted on three categories of speaker-related tasks to further explore impacts of different deep embeddings, including discriminative tasks (speaker verification and diarization), guiding tasks (target speaker detection and extraction) and regulating tasks (multi-speaker text-to-speech). Results show that all deep embeddings encoded channel and content information in addition to speaker identity, but the extent could vary and their performance on speaker-related tasks can be tremendously different: ECAPA-TDNN is dominant in discriminative tasks, and d-vector leads the guiding tasks, while regulating task is less sensitive to the choice of speaker representations. These may benefit future research utilizing speaker embeddings.