Miracle-dz/UniFAR-FTUs huggingface dataset:
Creating a Universal Dependencies Treebank of Spoken Frisian-Dutch Code-switched Data

This paper explores the difficulties of annotating transcribed spoken Dutch-Frisian code-switch utterances into Universal Dependencies. We make use of data from the FAME! corpus, which consists of transcriptions and audio data. Besides the usual annotation difficulties, this dataset is extra challenging because of Frisian being low-resource, the informal nature of the data, code-switching and non-standard sentence segmentation. As a starting point, two annotators annotated 150 random utterances in three stages of 50 utterances. After each stage, disagreements where discussed and resolved. An increase of 7.8 UAS and 10.5 LAS points was achieved between the first and third round. This paper will focus on the issues that arise when annotating a transcribed speech corpus. To resolve these issues several solutions are proposed.
Effects of metal and porphyrin structure on the yield and chemoselectivity of naphthalene oxidation; a comparative study for manganese and iron

Oxidation of naphthalene was performed with various manganese and iron porphyrins as catalysts and metachloroperbenzoic acid, tert-butylhydroperoxide, and hydrogenperoxide as oxidants, in the presence of imidazole as cocatalyst. With m-CPBA, in low concentration of catalyst, iron porphyrin shows a higher catalytic activity than manganese porphyrin, while a greater amount of manganese porphyrin is needed for having a good catalyst. In the presence of excess amount of H2O2 oxidant in CH3OH solvent, manganese porphyrin acts better than iron porphyrin; however with the same oxidant and in aprotic solvent such as CH3CN:CH2Cl2 again iron shows a higher activity than manganese. Moreover, the effects of metalloporphyrin concentration on the selectivity, activity and stability of catalyst were studied in H2O2-catalytic oxidation.
Nonlinear dimension reduction for surrogate modeling using gradient information

We introduce a method for the nonlinear dimension reduction of a high-dimensional function $u:\mathbb{R}^d\rightarrow\mathbb{R}$, $d\gg1$. Our objective is to identify a nonlinear feature map $g:\mathbb{R}^d\rightarrow\mathbb{R}^m$, with a prescribed intermediate dimension $m\ll d$, so that $u$ can be well approximated by $f\circ g$ for some profile function $f:\mathbb{R}^m\rightarrow\mathbb{R}$. We propose to build the feature map by aligning the Jacobian $\nabla g$ with the gradient $\nabla u$, and we theoretically analyze the properties of the resulting $g$. Once $g$ is built, we construct $f$ by solving a gradient-enhanced least squares problem. Our practical algorithm makes use of a sample $\{x^{(i)},u(x^{(i)}),\nabla u(x^{(i)})\}_{i=1}^N$ and builds both $g$ and $f$ on adaptive downward-closed polynomial spaces, using cross validation to avoid overfitting. We numerically evaluate the performance of our algorithm across different benchmarks, and explore the impact of the intermediate dimension $m$. We show that building a nonlinear feature map $g$ can permit more accurate approximation of $u$ than a linear $g$, for the same input data set.
Vector-Boson scattering at the LHC: Unraveling the electroweak sector

Vector-boson scattering (VBS) processes probe the innermost structure of electroweak interactions in the Standard Model, and provide a unique sensitivity for new physics phenomena affecting the gauge sector. In this review, we report on the salient aspects of this class of processes, both from the theory and experimental point of view. We start by discussing recent achievements relevant for their theoretical description, some of which have set important milestones in improving the precision and accuracy of the corresponding simulations. We continue by covering the development of experimental techniques aimed at detecting these rare processes and improving the signal sensitivity over large backgrounds. We then summarise the details of the most relevant VBS signatures and review the related measurements available to date, along with their comparison with Standard-Model predictions. We conclude by discussing the perspective at the upcoming Large Hadron Collider runs and at future hadron facilities. E-mail: roberto.covarelli@unito.it E-mail: mathieu.pellen@physik.uni-freiburg.de E-mail: marco.zaro@mi.infn. it 1 ar X iv :2 10 2. 10 99 1v 2 [ he pph ] 1 A pr 2 02 1
Machine learning-enabled camera free speckle wavemeter

: Recovering the wavelength from disordered speckle patterns has become an exciting prospect as a wavelength measurement method due to its high resolution and simple design. In previous studies, panel cameras have been used to detect the subtle differences between speckle patterns. However, the volume, bandwidth, sensitivity, and cost (in non-visible bands) associated with panel cameras have hindered their utility in broader applications, especially in high speed and low-cost measurements. In this work, we broke the limitations imposed by panel cameras by using a quadrant detector (QD) to capture the speckle images. In the scheme of QD detection, speckle images are directly filtered by convolution, where the kernel is equal to one quarter of a speckle pattern. First, we proposed an up-sampling algorithm to pre-process the QD data. Then a new convolution neural network (CNN) based algorithm, shallow residual network (SRN), was proposed to train the up-sampled images. The experimental results show that a resolution of 4 fm (~ 0.5 MHz) was achieved at 1550nm with an updating speed of ~ 1 kHz. More importantly, the SRN shows excellent robustness. The wavelength can be precisely reconstructed from raw QD data without any averaging, even where there exists apparent noise. The low-cost, simple structure, high speed and robustness of this design promote the speckle-based wavemeter to the industrial grade. In addition, without the restriction of panel cameras, it is believed that this wavemeter opens new routes in many other fields, such as distributed optical fiber sensors, optical communications, and laser frequency stabilization.
CDA: a Cost Efficient Content-based Multilingual Web Document Aligner

We introduce a Content-based Document Alignment approach (CDA), an efficient method to align multilingual web documents based on content in creating parallel training data for machine translation (MT) systems operating at the industrial level. CDA works in two steps: (i) projecting documents of a web domain to a shared multilingual space; then (ii) aligning them based on the similarity of their representations in such space. We leverage lexical translation models to build vector representations using TF×IDF. CDA achieves performance comparable with state-of-the-art systems in the WMT-16 Bilingual Document Alignment Shared Task benchmark while operating in multilingual space. Besides, we created two web-scale datasets to examine the robustness of CDA in an industrial setting involving up to 28 languages and millions of documents. The experiments show that CDA is robust, cost-effective, and is significantly superior in (i) processing large and noisy web data and (ii) scaling to new and low-resourced languages.
An Agile and Distributed Mechanism for Inter-Domain Network Slicing in Next Generation Mobile Networks

Network slicing is emerging as a promising method to provide sought after versatility and flexibility to cope with ever-increasing demands. To realize such potential advantages and to meet the challenging requirements of various network slices in an on-demand fashion, we need to develop an agile and distributed mechanism for resource provisioning to different network slices in a heterogeneous multi-resource multi-domain mobile network environment. We formulate inter-domain resource provisioning to network slices in such an environment as an optimization problem which maximizes social welfare among network slice tenants (so that maximizing tenants’ satisfaction), while minimizing operational expenditures for infrastructure service providers at the same time. To solve the envisioned problem, we implement an iterative auction game among network slice tenants, on one hand, and a plurality of price-taking subnet service providers, on the other hand. We show that the proposed solution method results in a distributed privacy-saving mechanism which converges to the optimal solution of the described optimization problem. In addition to providing analytical results to characterize the performance of the proposed mechanism, we also employ numerical evaluations to validate the results, demonstrate convergence of the presented algorithm, and show the enhanced performance of the proposed approach (in terms of resource utilization, fairness and operational costs) against the existing solutions.
Agent-Based Campus Novel Coronavirus Infection and Control Simulation

Corona Virus Disease 2019 (COVID-19), due to its extremely high infectivity, has been spreading rapidly around the world and bringing huge influence to socioeconomic development and people’s daily life. Taking for example the virus transmission that may occur after college students return to school, we analyze the quantitative influence of the key factors on the virus spread, including crowd density and self-protection. One Campus Virus Infection and Control Simulation (CVICS) model of the novel coronavirus is proposed in this article, fully considering the characteristics of repeated contact and strong mobility of crowd in the closed environment. Specifically, we build an agent-based infection model, introduce the mean field theory to calculate the probability of virus transmission, and microsimulate the daily prevalence of infection among individuals. The experimental results show that the proposed model in this article efficiently simulates how the virus spreads in the dense crowd in frequent contact under a closed environment. Furthermore, preventive and control measures, such as self-protection, crowd decentralization, and isolation during the epidemic, can effectively delay the arrival of infection peak, reduce the prevalence, and, finally, lower the risk of COVID-19 transmission after the students return to school.
Communication Efficient Parallel Reinforcement Learning

We consider the problem where M agents interact with M identical and independent environments with S states and A actions using reinforcement learning for T rounds. The agents share their data with a central server to minimize their regret. We aim to ﬁnd an algorithm that allows the agents to minimize the regret with infrequent communication rounds. We provide DIST -UCRL which runs at each agent and prove that the total cumulative regret of M agents is upper bounded as ˜ O ( DS √ MAT ) for a Markov Decision Process with diameter D , number of states S , and number of actions A . The agents synchronize after their visitations to any state-action pair exceeds a cer-tain threshold. Using this, we obtain a bound of O ( MSA log( MT )) on the total number of communications rounds. Finally, we evaluate the algorithm against multiple environments and demonstrate that the proposed algorithm performs at par with an always communication version of the UCRL2 algorithm, while with signiﬁcantly lower communication.
Cornus: One-Phase Commit for Cloud Databases with Storage Disaggregation

Two-phase commit (2PC) has been widely used in distributed databases to ensure atomicity for distributed transactions. However, 2PC suffers from two limitations. First, 2PC incurs long latency as it requires two logging operations on the critical path. Second, when a coordinator fails, a participant may be blocked waiting for the coordinator’s decision, leading to indefinitely long latency and low throughput. We make a key observation that modern cloud databases feature a storage disaggregation architecture, which allows a transaction’s final decision to not rely on the central coordinator. We propose Cornus, a one-phase commit (1PC) protocol specifically designed for this architecture. Cornus can solve the two problems mentioned above by leveraging the fact that all compute nodes are able to access and modify the log data on any storage node. We present Cornus in detail, formally prove its correctness, develop certain optimization techniques, and evaluate against 2PC on YCSB and TPC-C workloads. The results show that Cornus can achieve 1.5 × speedup in latency.
Generalized Non-adaptive Group Testing

In the problem of classical group testing one aims to identify a small subset (of expected size d) diseased individuals/defective items in a large population (of size n) based on a minimal number of suitably-designed group non-adaptive tests on subsets of items, where the test outcome is governed by an “OR” function, i.e., the test outcome is positive iff the given test contains at least one defective item. Motivated by physical considerations we consider a generalized scenario (that includes as special cases multiple other group-testing-like models in the literature) wherein the test outcome is governed by an arbitrary monotone (stochastic) test function f(·), with the test outcome being positive with probability f(x), where x is the number of defectives tested in that pool. For any monotone test function f(·) we present a non-adaptive generalized group-testing scheme that identifies all defective items with high probability. Our scheme requires at most O(d log(n)) tests for any monotone test function f(·), and at most O(d log(n)) in the physically relevant sub-class of sensitive test functions (and hence is information-theoretically order-optimal for this sub-class). A short video describing this paper (narrated by the first author) is at [1].
Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human Player

This paper presents a sensor-level mapless collision avoidance algorithm for use in mobile robots that map raw sensor data to linear and angular velocities and navigate in an unknown environment without a map. An efficient training strategy is proposed to allow a robot to learn from both human experience data and self-exploratory data. A game format simulation framework is designed to allow the human player to tele-operate the mobile robot to a goal and human action is also scored using the reward function. Both human player data and self-playing data are sampled using prioritized experience replay algorithm. The proposed algorithm and training strategy have been evaluated in two different experimental configurations: Environment 1, a simulated cluttered environment, and Environment 2, a simulated corridor environment, to investigate the performance. It was demonstrated that the proposed method achieved the same level of reward using only 16% of the training steps required by the standard Deep Deterministic Policy Gradient (DDPG) method in Environment 1 and 20% of that in Environment 2. In the evaluation of 20 random missions, the proposed method achieved no collision in less than 2 h and 2.5 h of training time in the two Gazebo environments respectively. The method also generated smoother trajectories than DDPG. The proposed method has also been implemented on a real robot in the real-world environment for performance evaluation. We can confirm that the trained model with the simulation software can be directly applied into the real-world scenario without further fine-tuning, further demonstrating its higher robustness than DDPG. The video and code are available: https://youtu.be/BmwxevgsdGc https://github.com/hanlinniu/turtlebot3_ddpg_collision_avoidance
Phase diagram of the spin-
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 Yukawa–Sachdev-Ye-Kitaev model: Non-Fermi liquid, insulator, and superconductor

We analyze the phase diagram of the spin-1/2 Yukawa-Sachdev-Ye-Kitaev model, which describes complex spin-1/2 fermions randomly interacting with real bosons via a Yukawa coupling, at ﬁnite temperatures and varying fermion density. In a recent work [PhysRevResearch.2. 033084] it has been shown that, upon varying ﬁlling or chemical potential, there exists a ﬁrst-order quantum phase transition between a non-Fermi liquid (nFL) phase and an insulating phase. Here we show that in such a model with time-reversal symmetry this quantum phase transition is preempted by a pairing phase that develops as a low-temperature instability. As a remnant of the would-be nFL-insulator transition, the superconducting critical temperature rapidly decreases beyond a certain chemical potential. On the other hand, depending on parameters, the ﬁrst-order quantum phase transition extends to ﬁnite-temperatures and terminate at a thermal critical point, beyond which the nFL and the insulator become the same phase, similar to that of the liquid-gas and metal-insulator transition in real materials. We determine the pairing phase boundary and the location of the thermal critical point via combined analytic and quantum Monte Carlo numeric eﬀorts. Our results provide the model realization of the transition of nFL’s towards superconductivity and insulating states, therefore oﬀer a controlled platform for future investigations of the generic phase diagram that hosts nFL, insulator and superconductor and their phase transitions.
Group Inverse-Gamma Gamma Shrinkage for Sparse Regression with Block-Correlated Predictors

Heavy-tailed continuous shrinkage priors, such as the horseshoe prior, are widely used for sparse estimation problems. However, there is limited work extending these priors to predictors with grouping structures. Of particular interest in this article, is regression coefficient estimation where pockets of high collinearity in the covariate space are contained within known covariate groupings. To assuage variance inflation due to multicollinearity we propose the group inverse-gamma gamma (GIGG) prior, a heavy-tailed prior that can trade-off between local and group shrinkage in a data adaptive fashion. A special case of the GIGG prior is the group horseshoe prior, whose shrinkage profile is correlated withingroup such that the regression coefficients marginally have exact horseshoe regularization. We show posterior consistency for regression coefficients in linear regression models and posterior concentration results for mean parameters in sparse normal means models. The full conditional distributions corresponding to GIGG regression can be derived in closed form, leading to straightforward posterior computation. We show that GIGG regression results in low mean-squared error across a wide range of correlation structures and withingroup signal densities via simulation. We apply GIGG regression to data from the National Health and Nutrition Examination Survey for associating environmental exposures with liver functionality.
Gerbes in Geometry, Field Theory, and Quantisation

Abstract This is a mostly self-contained survey article about bundle gerbes and some of their recent applications in geometry, field theory, and quantisation. We cover the definition of bundle gerbes with connection and their morphisms, and explain the classification of bundle gerbes with connection in terms of differential cohomology. We then survey how the surface holonomy of bundle gerbes combines with their transgression line bundles to yield a smooth bordism-type field theory. Finally, we exhibit the use of bundle gerbes in geometric quantisation of 2-plectic as well as 1- and 2-shifted symplectic forms. This generalises earlier applications of gerbes to the prequantisation of quasi-symplectic groupoids.
Mutation Testing framework for Machine Learning

This is an article or technical note which is intended to provides an insight journey of Machine Learning Systems (MLS) testing, its evolution, current paradigm and future work. Machine Learning Models, used in critical applications such as healthcare industry[1], Automobile [2], [3] and Air Traffic control, Share Trading etc., and failure of ML Model can lead to severe consequences in terms of loss of life or property. To remediate this, developers, scientists, and ML community around the world, must build a highly reliable test architecture for critical ML application. At the very foundation layer, any test model must satisfy the core testing attributes such as test properties and its components. This attribute comes from the software engineering [5], [6], but the same cannot be applied in as-is form to the ML testing and we will tell you “why”.
Multi-Domain Adaptation in Neural Machine Translation Through Multidimensional Tagging

While NMT has achieved remarkable results in the last 5 years, production systems come with strict quality requirements in arbitrarily niche domains that are not always adequately covered by readily available parallel corpora. This is typically addressed by training domain specific models, using fine-tuning methods and some variation of back-translation on top of in-domain monolingual corpora. However, industrial practitioners can rarely afford to focus on a single domain. A far more typical scenario includes a set of closely related, yet succinctly different sub-domains. At Booking.com, we need to translate property descriptions, user reviews, as well as messages, (for example those sent between a customer and an agent or property manager). An editor might need to translate articles across a set of different topics. An e-commerce platform would typically need to translate both the description of each item and the user generated content related to them. To this end, we propose MDT: a novel method to simultaneously fine-tune on several sub-domains by passing multidimensional sentence-level information to the model during training and inference. We show that MDT achieves results competitive to N specialist models each fine-tuned on a single constituent domain, while effectively serving all N sub-domains, therefore cutting development and maintenance costs by the same factor. Besides BLEU (industry standard automatic evaluation metric known to only weakly correlate with human judgement) we also report rigorous human evaluation results for all models and sub-domains as well as specific examples that better contextualise the performance of each model in terms of adequacy and fluency. To facilitate further research, we plan to make the code available upon acceptance.
Decoupling Value and Policy for Generalization in Reinforcement Learning

Standard deep reinforcement learning algorithms use a shared representation for the policy and value function, especially when training directly from images. However, we argue that more information is needed to accurately estimate the value function than to learn the optimal policy. Consequently, the use of a shared representation for the policy and value function can lead to overfitting. To alleviate this problem, we propose two approaches which are combined to create IDAAC: Invariant Decoupled Advantage Actor-Critic. First, IDAAC decouples the optimization of the policy and value function, using separate networks to model them. Second, it introduces an auxiliary loss which encourages the representation to be invariant to task-irrelevant properties of the environment. IDAAC shows good generalization to unseen environments, achieving a new state-of-the-art on the Procgen benchmark and outperforming popular methods on DeepMind Control tasks with distractors. Our implementation is available at https://github.com/rraileanu/idaac.