Miracle-dz/UniFAR-FTUs huggingface dataset:
TUJU21: nuclear PDFs with electroweak-boson data at NNLO

Nuclear parton distribution functions (nPDFs) can be determined in a global QCD analysis using a wide range of experimental data. In addition to older ﬁxed-target deep inelastic scattering and Drell-Yan (DY) dilepton production data, several analyses from p+Pb collisions at the LHC provide further constraints and extend the kinematic reach of applicable data. Here we present an update of our previous TUJU19 analysis where we now include also electroweak-boson production data recently measured by ATLAS and CMS. For the ﬁrst time, LHC data are included in a nPDF analysis performed at next-to-next-to-leading order (NNLO) in perturbative QCD. As before, our setup is based on the open-source analysis framework xFitter and we ﬁt our own proton baseline, ensuring a fully consistent setup. We ﬁnd good agreement with the applied data and that the resulting χ 2 /N df is signiﬁcantly smaller in case of the NNLO analysis (0.84) compared to our NLO analysis (0.94). Also, we present comparisons between our NNLO calculations and electroweak-boson production data in Pb+Pb collisions from ATLAS and CMS and DY data recently measured by CMS where NNLO corrections are found signiﬁcant.
Strategic Choices of Migrants and Smugglers in the Central Mediterranean Sea

The sea crossing from Libya to Italy is one of the world’s most dangerous and politically contentious migration routes, and yet over half a million people have attempted the crossing since 2014. Leveraging data on aggregate migration flows and individual migration incidents, we estimate how migrants and smugglers have reacted to changes in border enforcement, namely the rise in interceptions by the Libyan Coast Guard starting in 2017 and the corresponding decrease in the probability of rescue at sea. We find support for a deterrence effect in which attempted crossings along the Central Mediterranean route declined, and a diversion effect in which some migrants substituted to the Western Mediterranean route. At the same time, smugglers adapted their tactics. Using a strategic model of the smuggler’s choice of boat size, we estimate how smugglers trade off between the short-run payoffs to launching overcrowded boats and the long-run costs of making less successful crossing attempts under different levels of enforcement. Taken together, these analyses shed light on how the integration of incidentand flow-level datasets can inform ongoing migration policy debates and identify potential consequences of changing enforcement regimes.
The Mean Dimension of Neural Networks - What causes the interaction effects?

Owen and Hoyt recently showed that the effective dimension offers key structural information about the input-output mapping underlying an artiﬁcial neural network. Along this line of research, this work proposes an estimation procedure that allows the calculation of the mean dimension from a given dataset, without resampling from external distributions. The design yields total indices when features are independent and a variant of total indices when features are correlated. We show that this variant possesses the zero independence property. With synthetic datasets, we analyse how the mean dimension evolves layer by layer and how the activation function impacts the magnitude of interactions. We then use the mean dimension to study some of the most widely employed convolutional architectures for image recognition (LeNet, ResNet, DenseNet). To account for pixel correlations, we propose calculating the mean dimension after the addition of an inverse PCA layer that allows one to work on uncorrelated PCA-transformed features, without the need to retrain the neural network. We use the generalized total indices to produce heatmaps for post-hoc explanations, and we employ the mean dimension on the PCA-transformed features for cross comparisons of the artiﬁcial neural networks structures. Results provide several insights on the difference in magnitude of interactions across the architectures, as well as indications on how the mean dimension evolves during training.
Reinforcement Learning of CPG-Regulated Locomotion Controller for a Soft Snake Robot

Intelligent control of soft robots is challenging due to the nonlinear and difficult-to-model dynamics. One promising model-free approach for soft robot control is reinforcement learning (RL). However, model-free RL methods tend to be computationally expensive and data-inefficient and may not yield natural and smooth locomotion patterns for soft robots. In this work, we develop a bioinspired design of a learning-based goal-tracking controller for a soft snake robot. The controller is composed of two modules: An RL module for learning goal-tracking behaviors given the unmodeled and stochastic dynamics of the robot, and a central pattern generator (CPG) with the Matsuoka oscillators for generating stable and diverse locomotion patterns. We theoretically investigate the maneuverability of Matsuoka CPG's oscillation bias, frequency, and amplitude for steering control, velocity control, and sim-to-real adaptation of the soft snake robot. Based on this analysis, we proposed a composition of RL and CPG modules such that the RL module regulates the tonic inputs to the CPG system given state feedback from the robot, and the output of the CPG module is then transformed into pressure inputs to pneumatic actuators of the soft snake robot. This design allows the RL agent to naturally learn to entrain the desired locomotion patterns determined by the CPG maneuverability. We validated the optimality and robustness of the control design in both simulation and real experiments, and performed extensive comparisons with state-of-art RL methods to demonstrate the benefit of our bioinspired control design.
FCC-ee, an Accelerator for New Physics Searches in the Heavy Quark Sector

This manuscript is devoted to the description of the proposed Future Circular Collider (FCC) project and its physics program focused on measurements involving heavy quarks. It summarizes the report submitted to the US 2021 Snowmass Process and European Strategy on Particle Physics. The proposed electron-positron collider based on established technologies will provide high instantaneous luminosities at center-of-mass energies from the Z resonance through the ZH and WW and up to the $t\bar{t}$ threshold. This will allow a very rich set of fundamental measurements as well as the study of heavy-flavor and tau physics in ultra-rare decays beyond the LHC reach.
Efficient Backward Reachability Using the Minkowski Difference of Constrained Zonotopes

Backward reachability analysis is essential to synthesizing controllers that ensure the correctness of closed-loop systems. This article is concerned with developing scalable algorithms that underapproximate the backward reachable sets, for discrete-time uncertain linear and nonlinear systems. Our algorithm sequentially linearizes the dynamics and uses constrained zonotopes for set representation and computation. The main technical ingredient of our algorithm is an efficient way to underapproximate the Minkowski difference between a constrained zonotopic minuend and a zonotopic subtrahend, which consists of all possible values of the uncertainties and the linearization error. This Minkowski difference needs to be represented as a constrained zonotope to enable subsequent computation, but, as we show, it is impossible to find a polynomial-size representation for it in polynomial time. Our algorithm finds a polynomial-size underapproximation in polynomial time. We further analyze the conservatism of this underapproximation technique and show that it is exact under some conditions. Based on the developed Minkowski difference technique, we detail two backward reachable set computation algorithms to control the linearization error and incorporate nonconvex state constraints. Several examples illustrate the effectiveness of our algorithms.
A4T: Hierarchical Affordance Detection for Transparent Objects Depth Reconstruction and Manipulation

Transparent objects are widely used in our daily lives and therefore robots need to be able to handle them. However, transparent objects suffer from light reflection and refraction, which makes it challenging to obtain the accurate depth maps required to perform handling tasks. In this letter, we propose a novel affordance-based framework for depth reconstruction and manipulation of transparent objects, named A4T. A hierarchical AffordanceNet is first used to detect the transparent objects and their associated affordances that encode the relative positions of an object's different parts. Then, given the predicted affordance map, a multi-step depth reconstruction method is used to progressively reconstruct the depth maps of transparent objects. Finally, the reconstructed depth maps are employed for the affordance-based manipulation of transparent objects. To evaluate our proposed method, we construct a real-world dataset TRANS-AFF with affordances and depth maps of transparent objects, which is the first of its kind. Extensive experiments show that our proposed methods can predict accurate affordance maps, and significantly improve the depth reconstruction of transparent objects compared to the state-of-the-art method, with the Root Mean Squared Error in meters significantly decreased from 0.097 to 0.042. Furthermore, we demonstrate the effectiveness of our proposed method with a series of robotic manipulation experiments on transparent objects.
Parameterized Complexity of Streaming Diameter and Connectivity Problems

We initiate the investigation of the parameterized complexity of Diameter and Connectivity in the streaming paradigm. On the positive end, we show that knowing a vertex cover of size $k$ allows for algorithms in the Adjacency List (AL) streaming model whose number of passes is constant and memory is $O(\log n)$ for any fixed $k$. Underlying these algorithms is a method to execute a breadth-first search in $O(k)$ passes and $O(k \log n)$ bits of memory. On the negative end, we show that many other parameters lead to lower bounds in the AL model, where $\Omega(n/p)$ bits of memory is needed for any $p$-pass algorithm even for constant parameter values. In particular, this holds for graphs with a known modulator (deletion set) of constant size to a graph that has no induced subgraph isomorphic to a fixed graph $H$, for most $H$. For some cases, we can also show one-pass, $\Omega(n \log n)$ bits of memory lower bounds. We also prove a much stronger $\Omega(n^2/p)$ lower bound for Diameter on bipartite graphs. Finally, using the insights we developed into streaming parameterized graph exploration algorithms, we show a new streaming kernelization algorithm for computing a vertex cover of size $k$. This yields a kernel of $2k$ vertices (with $O(k^2)$ edges) produced as a stream in $\text{poly}(k)$ passes and only $O(k \log n)$ bits of memory.
The Power of Fragmentation: A Hierarchical Transformer Model for Structural Segmentation in Symbolic Music Generation

Symbolic music generation relies on the contextual representation capabilities of the generative model, where the most prevalent approach is the Transformer-based model. Learning contextual representations are also related to the structural elements in music, i.e., intro, verse, and chorus, which have not received much attention of scientific publications. In this paper, we propose a hierarchical Transformer model to learn multiscale contexts in music. In the encoding phase, we first design a fragment scope localization module to separate the music parts into chords and sections. Then, we use a multiscale attention mechanism to learn note-, chord-, and section-level contexts. In the decoding phase, we propose a hierarchical Transformer model that uses fine decoders to generate sections in parallel and a coarse decoder to decode the combined music. We also designed a music style normalization layer to achieve a consistent music style between the generated sections. Our model is evaluated on two open MIDI datasets. Experiments show that our model outperforms other comparative models in 50% (6 out of 12 metrics) and 83.3% (10 out of 12 metrics) of the quantitative metrics for short- and long-term music generation, respectively. Preliminary visual analysis also suggests its potential in following compositional rules, such as reuse of rhythmic patterns and critical melodies, which are associated with improved music quality.
UM4: Unified Multilingual Multiple Teacher-Student Model for Zero-Resource Neural Machine Translation

Most translation tasks among languages belong to the zero-resource translation problem where parallel corpora are unavailable. Multilingual neural machine translation (MNMT) enables one-pass translation using shared semantic space for all languages compared to the two-pass pivot translation but often underperforms the pivot-based method. In this paper, we propose a novel method, named as Unified Multilingual Multiple teacher-student Model for NMT (UM4). Our method unifies source-teacher, target-teacher, and pivot-teacher models to guide the student model for the zero-resource translation. The source teacher and target teacher force the student to learn the direct source-target translation by the distilled knowledge on both source and target sides. The monolingual corpus is further leveraged by the pivot-teacher model to enhance the student model. Experimental results demonstrate that our model of 72 directions significantly outperforms previous methods on the WMT benchmark.
Human-Centric Research for NLP: Towards a Definition and Guiding Questions

With Human-Centric Research (HCR) we can steer research activities so that the research outcome is beneﬁcial for human stakeholders, such as end users. But what exactly makes research human-centric ? We address this question by providing a working deﬁnition and deﬁne how a research pipeline can be split into different stages in which human-centric components can be added. Additionally, we discuss existing NLP with HCR components and deﬁne a series of guiding questions , which can serve as starting points for researchers interested in exploring human-centric research approaches. We hope that this work would inspire researchers to reﬁne the proposed deﬁnition and to pose other questions that might be meaningful for achieving HCR.
Multilingual Persuasion Detection: Video Games as an Invaluable Data Source for NLP

Role-playing games (RPGs) have a considerable amount of text in video game dialogues. Quite often this text is semi-annotated by the game developers. In this paper, we extract a multilingual dataset of persuasive dialogue from several RPGs. We show the viability of this data in building a persuasion detection system using a natural language processing (NLP) model called BERT. We believe that video games have a lot of unused potential as a datasource for a variety of NLP tasks. The code and data described in this paper are available on Zenodo.
CougaR: fast and eclipse-resilient dissemination for blockchain networks

Despite their development for over a decade, a key problem block-chains are still facing is scalability in terms of throughput, typically limited to a few transactions per second. A fundamental factor limiting this metric is the propagation latency of blocks through the underlying peer-to-peer network, which is typically constructed by means of random connectivity. Disseminating blocks fast improves not only the transaction throughput, but also the security of the system as it reduces the probability of forks. In this paper we present CougaR: a simple yet efficient, eclipse-resistant, decentralized protocol that substantially reduces the block dissemination time in blockchain networks. CougaR's key advantages stem from its link selection policy, which combines a network latency criterion with randomness to offer fast and reliable block dissemination to the entire network. Moreover, CougaR is eclipse-resistant by design, as nodes are protected from having all their links directly or indirectly imposed on them by others, which is the typical vulnerability exploited to deploy eclipse attacks. We rigorously evaluate CougaR by an extensive set of experiments, both against a wide spectrum of parameter settings, and in comparison to the current state of the art.
Numerical Approximations of the Allen-Cahn-Ohta-Kawasaki (ACOK) Equation with Modified Physics Informed Neural Networks (PINNs)

The physics informed neural networks (PINNs) has been widely utilized to numerically approximate PDE problems. While PINNs has achieved good results in producing solutions for many partial differential equations, studies have shown that it does not perform well on phase field models. In this paper, we partially address this issue by introducing a modified physics informed neural networks. In particular, they are used to numerically approximate Allen-Cahn-Ohta-Kawasaki (ACOK) equation with a volume constraint.
Magnetic structure and spin dynamics of the quasi-2D antiferromagnet

Magnetic properties of the antiferromagnet Zn 0 . 15 Cu 1 . 85 V 2 O 7 (ZnCVO) have been thoroughly in- vestigated on powder and single-crystal samples. The crystal structure determination using powder x-ray and neutron diﬀraction conﬁrms that ZnCVO with Zn = 0.15 is isostructural with β -Cu 2 V 2 O 7 ( β -CVO) with small deviation in the lattice parameters. Macroscopic magnetic properties measurements also conﬁrm the similarity between the two compounds. The Cu 2+ spins were found to align along the crystallographic c -axis, antiparallel to their nearest neighbors connected by the leading exchange interaction J 1 . Spin dynamics reveals a typical symmetric spin-wave dispersion with strong interactions in the bc -plane and weak interplane coupling. The exchange interaction analysis indi-cates that the spin network of ZnCVO is topologically consistent with the previous DFT prediction but the values of leading exchange interactions are contradictory. Furthermore, rather than the predicted 2D honeycomb structure, the spin network in ZnCVO could be better described by the anisotropic 2D spin network composing of J 1 , J 5 , and J 6 interactions, four bonds per one spin site, coupled by weak interplane interactions.
Wasserstein Graph Distance based on L1-Approximated Tree Edit Distance between Weisfeiler-Lehman Subtrees

The Weisfeiler-Lehman (WL) test is a widely used algorithm in graph machine learning, including graph kernels, graph metrics, and graph neural networks. However, it focuses only on the consistency of the graph, which means that it is unable to detect slight structural differences. Consequently, this limits its ability to capture structural information, which also limits the performance of existing models that rely on the WL test. This limitation is particularly severe for traditional metrics defined by the WL test, which cannot precisely capture slight structural differences. In this paper, we propose a novel graph metric called the Wasserstein WL Subtree (WWLS) distance to address this problem. Our approach leverages the WL subtree as structural information for node neighborhoods and defines node metrics using the L1-approximated tree edit distance (L1-TED) between WL subtrees of nodes. Subsequently, we combine the Wasserstein distance and the L1-TED to define the WWLS distance, which can capture slight structural differences that may be difficult to detect using conventional metrics. We demonstrate that the proposed WWLS distance outperforms baselines in both metric validation and graph classification experiments.
Randomized Douglas-Rachford methods for linear systems: Improved accuracy and efficiency

The Douglas-Rachford (DR) method is a widely used method for finding a point in the intersection of two closed convex sets (feasibility problem). However, the method converges weakly and the associated rate of convergence is hard to analyze in general. In addition, the direct extension of the DR method for solving more-than-two-sets feasibility problems, called the $r$-sets-DR method, is not necessarily convergent. To improve the efficiency of the optimization algorithms, the introduction of randomization and the momentum technique has attracted increasing attention. In this paper, we propose the randomized $r$-sets-DR (RrDR) method for solving the feasibility problem derived from linear systems, showing the benefit of the randomization as it brings linear convergence in expectation to the otherwise divergent $r$-sets-DR method. Furthermore, the convergence rate does not depend on the dimension of the coefficient matrix. We also study RrDR with heavy ball momentum and establish its accelerated rate. Numerical experiments are provided to confirm our results and demonstrate the notable improvements in accuracy and efficiency of the DR method, brought by the randomization and the momentum technique.
Turbulent Regimes in Collisions of 3D Alfvén-wave Packets

Using three-dimensional gyrofluid simulations, we revisit the problem of Alfvén-wave (AW) collisions as building blocks of the Alfvénic turbulent cascade and their interplay with magnetic reconnection at magnetohydrodynamic (MHD) scales. Depending on the large-scale value of the nonlinearity parameter χ 0 (the ratio between the AW linear propagation time and nonlinear turnover time), different regimes are observed. For strong nonlinearities (χ 0 ∼ 1), turbulence is consistent with a dynamically aligned, critically balanced cascade—fluctuations exhibit a scale-dependent alignment sinθk⊥∝k⊥−1/4 , resulting in a k⊥−3/2 spectrum and k∥∝k⊥1/2 spectral anisotropy. At weaker nonlinearities (small χ 0), a spectral break marking the transition between a large-scale weak regime and a small-scale k⊥−11/5 tearing-mediated range emerges, implying that dynamic alignment occurs also for weak nonlinearities. At χ 0 < 1 the alignment angle θk⊥ shows a stronger scale dependence than in the χ 0 ∼ 1 regime, namely sinθk⊥∝k⊥−1/2 at χ 0 ∼ 0.5, and sinθk⊥∝k⊥−1 at χ 0 ∼ 0.1. Dynamic alignment in the weak regime also modifies the large-scale spectrum, scaling approximately as k⊥−3/2 for χ 0 ∼ 0.5 and as k⊥−1 for χ 0 ∼ 0.1. A phenomenological theory of dynamically aligned turbulence at weak nonlinearities that can explain these spectra and the transition to the tearing-mediated regime is provided; at small χ 0, the strong scale dependence of the alignment angle combines with the increased lifetime of turbulent eddies to allow tearing to onset and mediate the cascade at scales that can be larger than those predicted for a critically balanced cascade by several orders of magnitude. Such a transition to tearing-mediated turbulence may even supplant the usual weak-to-strong transition.
A System Based on Artificial Neural Networks for Automatic Classification of Hydro-generator Stator Windings Partial Discharges

Partial discharge (PD) monitoring is widely used in rotating machines to evaluate the condition of stator winding insulation, but its practice on a large scale requires the development of intelligent systems that automatically process these measurement data. In this paper, it is proposed a methodology of automatic PD classification in hydro-generator stator windings using neural networks. The database is formed from online PD measurements in hydro-generators in a real setting. Noise filtering techniques are applied to these data. Then, based on the concept of image projection, novel features are extracted from the filtered samples. These features are used as inputs for training several neural networks. The best performance network, obtained using statistical procedures, presents a recognition rate of 98%.