Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Asynchronous Policy Iteration for Sequential Zero-Sum Games and Minimax Control

We introduce a contractive abstract dynamic programming framework and related policy iteration algorithms, specifically designed for sequential zero-sum games and minimax problems with a general structure. Aside from greater generality, the advantage of our algorithms over alternatives is that they resolve some long-standing convergence difficulties of the"natural"policy iteration algorithm, which have been known since the Pollatschek and Avi-Itzhak method [PoA69] for finite-state Markov games. Mathematically, this"natural"algorithm is a form of Newton's method for solving Bellman's equation, but Newton's method, contrary to the case of single-player DP problems, is not globally convergent in the case of a minimax problem, because the Bellman operator may have components that are neither convex nor concave. Our algorithms address this difficulty by introducing alternating player choices, and by using a policy-dependent mapping with a uniform sup-norm contraction property, similar to earlier works by Bertsekas and Yu [BeY10], [BeY12], [YuB13]. Moreover, our algorithms allow a convergent and highly parallelizable implementation, which is based on state space partitioning, and distributed asynchronous policy evaluation and policy improvement operations within each set of the partition. Our framework is also suitable for the use of reinforcement learning methods based on aggregation, which may be useful for large-scale problem instances.
Distributed Saddle-Point Problems Under Similarity

We study solution methods for (strongly-)convex-(strongly)-concave Saddle-Point Problems (SPPs) over networks of two type - master/workers (thus centralized) architectures and meshed (thus decentralized) networks. The local functions at each node are assumed to be similar, due to statistical data similarity or otherwise. We establish lower complexity bounds for a fairly general class of algorithms solving the SPP. We show that a given suboptimality $\epsilon>0$ is achieved over master/workers networks in $\Omega\big(\Delta\cdot \delta/\mu\cdot \log (1/\varepsilon)\big)$ rounds of communications, where $\delta>0$ measures the degree of similarity of the local functions, $\mu$ is their strong convexity constant, and $\Delta$ is the diameter of the network. The lower communication complexity bound over meshed networks reads $\Omega\big(1/{\sqrt{\rho}} \cdot {\delta}/{\mu}\cdot\log (1/\varepsilon)\big)$, where $\rho$ is the (normalized) eigengap of the gossip matrix used for the communication between neighbouring nodes. We then propose algorithms matching the lower bounds over either types of networks (up to log-factors). We assess the effectiveness of the proposed algorithms on a robust logistic regression problem.
Neural Variational Gradient Descent

Particle-based approximate Bayesian inference approaches such as Stein Variational Gradient Descent (SVGD) combine the flexibility and convergence guarantees of sampling methods with the computational benefits of variational inference. In practice, SVGD relies on the choice of an appropriate kernel function, which impacts its ability to model the target distribution -- a challenging problem with only heuristic solutions. We propose Neural Variational Gradient Descent (NVGD), which is based on parameterizing the witness function of the Stein discrepancy by a deep neural network whose parameters are learned in parallel to the inference, mitigating the necessity to make any kernel choices whatsoever. We empirically evaluate our method on popular synthetic inference problems, real-world Bayesian linear regression, and Bayesian neural network inference.
Frost: Benchmarking and Exploring Data Matching Results

“Bad” data has a direct impact on 88% of companies, with the average company losing 12% of its revenue due to it [17]. Duplicates – multiple but different representations of the same real-world entities – are among the main reasons for poor data quality. Therefore, finding and configuring the right deduplication solution is essential. Various data matching benchmarks exist which address this issue. However, many of them focus on the quality of matching results and neglect other important factors, such as business requirements. Additionally, they often do not specify how to explore benchmark results, which helps understand matching solution behavior. To address this gap between the mere counting of record pairs vs. a comprehensive means to evaluate data matching approaches, we present the benchmark platform Frost. Frost combines existing benchmarks, established quality metrics, a benchmark dimension for soft KPIs, and techniques to systematically explore and understand matching results. Thus, it can be used to compare multiple matching solutions regarding quality, usability, and economic aspects, but also to compare multiple runs of the same matching solution for understanding its behavior. Frost is implemented and published in the open-source application Snowman, which includes the visual exploration of matching results.
Dynamic Realtime z-Shimming : A Feasibility Study

Respiration causes time-varying frequency offsets that can result in ghosting artifacts. We propose a solution, which we term dynamic realtime z-shimming, wherein linear gradients are adjusted dynamically (slice-wise) and in real-time, to reflect magnetic field inhomogeneities that arise during image acquisition. In dynamic z-shimming, a method that is commonly used to reduce static frequency offsets in MR images of the spinal cord and brain, in-plane (static) frequency offsets are assumed to be homogeneous. Here we investigate whether or not that same assumption can be made for time-varying frequency offsets in the cervical spinal cord region. In order to explore the feasibility of dynamic realtime z-shimming, we acquired images using a pneumatic phantom setup, as well as in-vivo. We then simulated the effects of timevarying frequency offsets on MR images acquired with and without dynamic realtime z-shimming in different scenarios. We found that dynamic realtime z-shimming can reduce ghosting if the time-varying frequency offsets have an in-plane variability (standard deviation) of 1 Hz. This scenario was achieved in our phantom setup, where we observed a 50.2% reduction in ghosting within multi-echo gradient echo images acquired with dynamic realtime z-shimming, compared to without. On the other hand, we observed that the in-plane variability of the time-varying frequency offsets is too high within the cervical spinal cord region for dynamic realtime zshimming to be successful. These results can serve as a guideline and starting point for future dynamic realtime z-shimming experiments in which the in-plane variability of frequency offsets are minimized.
Target-oriented Fine-tuning for Zero-Resource Named Entity Recognition

Zero-resource named entity recognition (NER) severely suffers from data scarcity in a specific domain or language. Most studies on zero-resource NER transfer knowledge from various data by fine-tuning on different auxiliary tasks. However, how to properly select training data and fine-tuning tasks is still an open problem. In this paper, we tackle the problem by transferring knowledge from three aspects, i.e., domain, language and task, and strengthening connections among them. Specifically, we propose four practical guidelines to guide knowledge transfer and task fine-tuning. Based on these guidelines, we design a target-oriented fine-tuning (TOF) framework to exploit various data from three aspects in a unified training manner. Experimental results on six benchmarks show that our method yields consistent improvements over baselines in both cross-domain and cross-lingual scenarios. Particularly, we achieve new state-of-the-art performance on five benchmarks.
Robust Topology Optimization Using Variational Autoencoders

Topology Optimization is the process of finding the optimal arrangement of materials within a design domain by minimizing a cost function, subject to some performance constraints. Robust topology optimization (RTO) also incorporates the effect of input uncertainties and produces a design with the best average performance of the structure while reducing the response sensitivity to input uncertainties. It is computationally expensive to carry out RTO using finite element and Monte Carlo sampling. In this work, we use neural network surrogates to enable a faster solution approach via surrogate-based optimization and build a Variational Autoencoder (VAE) to transform the the high dimensional design space into a low dimensional one. Furthermore, finite element solvers will be replaced by a neural network surrogate. Also, to further facilitate the design exploration, we limit our search to a subspace, which consists of designs that are solutions to deterministic topology optimization problems under different realizations of input uncertainties. With these neural network approximations, a gradient-based optimization approach is formed to minimize the predicted objective function over the low dimensional design subspace. We demonstrate the effectiveness of the proposed approach on two compliance minimization problems and show that VAE performs well on learning the features of the design from minimal training data, and that converting the design space into a low dimensional latent space makes the problem computationally efficient. The resulting gradient-based optimization algorithm produces optimal designs with lower robust compliances than those observed in the training set.
Universal properties of boundary and interface charges in continuum models of one-dimensional insulators

We study single-channel continuum models of one-dimensional insulators induced by periodic potential modulations which are either terminated by a hard wall (the boundary model) or feature a single region of dislocations and/or impurity potentials breaking translational invariance (the interface model). We investigate the universal properties of excess charges accumulated near the boundary and the interface, respectively. We find a rigorous analytic proof for the earlier observed linear dependence of the boundary charge on the phase of the periodic potential modulation as well as extend these results to the interface model. The linear dependence on the phase shows a universal value for the slope, and is intersected by discontinuous jumps by plus or minus one electron charge at the phase points where localized states enter or leave a band of extended states. Both contributions add up such that the periodicity of the excess charge in the phase over a 2πcycle is maintained. While in the boundary model this property is usually associated with the bulk-boundary correspondence, in the interface model a correspondence of scattering state and localized state contributions to the total interface charge is unveiled on the basis of the so-called nearsightedness principle.
COfEE: A Comprehensive Ontology for Event Extraction from text, with an online annotation tool

Data is published on the web over time in great volumes, but majority of the data is unstructured, making it hard to understand and difficult to interpret. Information Extraction (IE) methods obtain structured information from unstructured data. One of the challenging IE tasks is Event Extraction (EE) which seeks to derive information about specific incidents and their actors from the text. EE is useful in many domains such as building a knowledge base, information retrieval, summarization and online monitoring systems. In the past decades, some event ontologies like ACE, CAMEO and ICEWS were developed to define event forms, actors and dimensions of events observed in the text. These event ontologies still have some shortcomings such as covering only a few topics like political events, having inflexible structure in defining argument roles, lack of analytical dimensions, and insufficient gold-standard data especially in lowresource language such as Persian. To address these concerns, we propose an event ontology, namely COfEE, that incorporates both expert domain knowledge, previous ontologies and a data-driven approach for identifying events from text. COfEE consists of two hierarchy levels (event types and event sub-types) that include new categories relating to environmental issues, cyberspace, criminal activity and natural disasters which need to be monitored instantly. Also, dynamic roles according to each event sub-type are defined to capture various dimensions of events. In a follow-up experiment, the proposed ontology is evaluated on Wikipedia events, and it is shown to be general and comprehensive. Moreover, in order to facilitate the preparation of gold-standard data for event extraction, a language-independent online tool is presented based on COfEE. A gold-standard dataset annotated by 10 human experts is also prepared consisting 24K news articles in Persian language according to the COfEE ontology. In order to diversify the data, news articles from the Wikipedia event portal and 100 most popular Persian news agencies between the years 2008 and 2021 is collected. Finally, we present a supervised method based on deep learning techniques to automatically extract relevant events and corresponding actors.
Coagulation Instability in Protoplanetary Disks: A Novel Mechanism Connecting Collisional Growth and Hydrodynamical Clumping of Dust Particles

We present a new instability driven by a combination of coagulation and radial drift of dust particles. We refer to this instability as “coagulation instability” and regard it as a promising mechanism to concentrate dust particles and assist planetesimal formation in the very early stages of disk evolution. Because of dust-density dependence of collisional coagulation efficiency, dust particles efficiently (inefficiently) grow in a region of positive (negative) dust density perturbations, leading to a small radial variation of dust sizes and as a result radial velocity perturbations. The resultant velocity perturbations lead to dust concentration and amplify dust density perturbations. This positive feedback makes a disk unstable. The growth timescale of coagulation instability is a few tens of orbital periods even when dust-to-gas mass ratio is on the order of 10−3. In a protoplanetary disk, radial drift and coagulation of dust particles tend to result in dust depletion. The present instability locally concentrates dust particles even in such a dust-depleted region. The resulting concentration provides preferable sites for dust–gas instabilities to develop, which leads to further concentration. Dust diffusion and aerodynamical feedback tend to stabilize short-wavelength modes, but do not completely suppress the growth of coagulation instability. Therefore, coagulation instability is expected to play an important role in setting up the next stage for other instabilities, such as streaming instability or secular gravitational instability, to further develop toward planetesimal formation.
Differentially Private Algorithms for 2020 Census Detailed DHC Race \& Ethnicity

This article describes a proposed differentially private (DP) algorithms that the US Census Bureau is considering to release the Detailed Demographic and Housing Characteristics (DHC) Race&Ethnicity tabulations as part of the 2020 Census. The tabulations contain statistics (counts) of demographic and housing characteristics of the entire population of the US crossed with detailed races and tribes at varying levels of geography. We describe two differentially private algorithmic strategies, one based on adding noise drawn from a two-sided Geometric distribution that satisfies"pure"-DP, and another based on adding noise from a Discrete Gaussian distribution that satisfied a well studied variant of differential privacy, called Zero Concentrated Differential Privacy (zCDP). We analytically estimate the privacy loss parameters ensured by the two algorithms for comparable levels of error introduced in the statistics.
Equidistant Linear Codes in Projective Spaces

Linear codes in the projective space $\mathbb{P}_q(n)$, the set of all subspaces of the vector space $\mathbb{F}_q^n$, were first considered by Braun, Etzion and Vardy. The Grassmannian $\mathbb{G}_q(n,k)$ is the collection of all subspaces of dimension $k$ in $\mathbb{P}_q(n)$. We study equidistant linear codes in $\mathbb{P}_q(n)$ in this paper and establish that the normalized minimum distance of a linear code is maximum if and only if it is equidistant. We prove that the upper bound on the size of such class of linear codes is $2^n$ when $q=2$ as conjectured by Braun et al. Moreover, the codes attaining this bound are shown to have structures akin to combinatorial objects, viz. \emph{Fano plane} and \emph{sunflower}. We also prove the existence of equidistant linear codes in $\mathbb{P}_q(n)$ for any prime power $q$ using \emph{Steiner triple system}. Thus we establish that the problem of finding equidistant linear codes of maximum size in $\mathbb{P}_q(n)$ with constant distance $2d$ is equivalent to the problem of finding the largest $d$-intersecting family of subspaces in $\mathbb{G}_q(n, 2d)$ for all $1 \le d \le \lfloor \frac{n}{2}\rfloor$. Our discovery proves that there exist equidistant linear codes of size more than $2^n$ for every prime power $q>2$.
Non-locality of the turbulent electromotive force

The generation of large-scale magnetic fields ($\overline{\mathbf {B}}$) in astrophysical systems is driven by the mean turbulent electromotive force ($\overline{{\boldsymbol {\cal E}} {}}$), the cross correlation between local fluctuations of velocity and magnetic fields. This can depend non-locally on $\overline{\mathbf {B}}$ through a convolution kernel Kij. In a new approach to find Kij, we directly fit the time series data of $\overline{{\boldsymbol {\cal E}} {}}$ versus $\overline{\mathbf {B}}$ from a galactic dynamo simulation using singular value decomposition. We calculate the usual turbulent transport coefficients as moments of Kij, show the importance of including non-locality over eddy length-scales to fully capture their amplitudes and that higher order corrections to the standard transport coefficients are small in the present case.
Toward Observation Based Least Restrictive Collision Avoidance Using Deep Meta Reinforcement Learning

This letter presents the Observation-based Least-Restrictive Collision Avoidance Module (OLR-CAM) that can be added to any autonomous robot working in a shared environment and provide a high-level safety layer to the existing policy for each robot. The OLR-CAM takes raw sensory observations as input, evaluates the agents’ safety against dynamic and static obstacles, and only intervenes the default policy when needed – in a least-restrictive fashion – to avoid a potential collision. In our approach, we meta-train the OLR-CAM policy within a “2D Navigation Meta World System”. Furthermore, to endow the policy with a notion of safety in multi-agent environments with obstacles, we propose a novel reward function based on a safety value function derived from the Hamilton-Jacobi reachability theory and a local cost map. The proposed reward function does not need any additional information about the environment's map. This facilitates the adoption of the algorithm in a new environment at the meta test stage. The proposed algorithm is fully meta-trained in simulation and tested on a real multi-agent system without any additional training conducted in the real setting. Our results show that the OLR-CAM success rate outperforms a well-known classical baseline approach by 10 percent on average and reduces the interruptions/changes to the preferred velocity by 15 percent.
Paternal mitochondria from an rmd-2, rmd-3, rmd-6 triple mutant are properly positioned in the C. elegans zygote

RMD-1,2,3,6 (regulator of microtubule dynamics) is a family of homologous proteins conserved between humans and C. elegans. Human RMD-3/PTPIP51 is a mitochondrial protein that tethers mitochondria to the endoplasmic reticulum. C. elegans RMD-2, 3, and 6 are expressed in sperm. To test whether paternal RMD-2, 3, 6 might redundantly tether paternal mitochondria to maternal ER at fertilization, we generated an rmd-2, rmd-3, rmd-6 triple mutant. Paternal mitochondria derived from control or triple mutant worms were concentrated in a cloud around the paternal DNA at the future posterior end of zygotes during meiosis. No significant difference was detected in the position of paternal mitochondria within the zygote nor in the position of paternal mitochondria relative to paternal DNA within the zygote. There was also no reduction in progeny viability between control and triple mutant worms.
Microcatheter-Assisted Protection of the Posterior Inferior Cerebellar Artery During Parent Artery Sacrifice of a Vertebral Artery Dissecting Aneurysm

Branch vessel occlusion is a major cause of stroke in parent artery sacrifice (PAS) for vertebral artery dissecting aneurysms (VADA). There is now an increasing trend towards preservation of branch vessels during PAS. Stents are commonly employed to achieve this but bring with it the attendant risks of future thrombosis and lifelong antiplatelet use. Although a microcatheter protection technique has been utilised in branch artery protection of wide-necked saccular aneurysms, it has rarely been described in PAS for VADAs. We describe the use of a dual microcatheter technique in the protection and remodelling of the posterior inferior cerebellar artery (PICA) during PAS of the vertebral artery, which also served as a temporary scaffold to support placement of the coils during the embolisation process.
Unusual diagnosis in a patient presenting with fever and bilateral lung infiltrates

An 81-year-old former smoker, with a history of asthma and surgical treatment of abdominal aortic aneurysm, was admitted to hospital with a 1-month history of fever up to 38.6°C, purulent sputum and left-sided pleuritic chest pain. Chest computed tomography (CT) showed consolidation type infiltrates peripherally in both lungs. Flexible bronchoscopy revealed no endobronchial lesion and cytological and microbiological examination, as well as qualitative analysis of bronchoalveolar lavage (BAL) were non-diagnostic. The cytological examination of BAL revealed: macrophages 85%; lymphocytes 12%; eosinophils 2%; and other neutrophils 1%. Investigations for IgG4-related disease or any other autoimmune disorder were negative. The patient was treated with amoxicillin–clavulanic acid for 10 days before admission. He was treated with ciprofloxacin–clindamycin on admission, but due to clinical and radiological deterioration on chest CT, we administered wide-spectrum antibiotics (meropenem plus vancomycin) without clinically significant response (figure 1). Inflammatory myofibroblastic tumour (IMT) is a rare neoplasm, most commonly described in children and young adults. We present a case of IMT in an elderly man. https://bit.ly/355wf8X