Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic Control through Approximate Bayesian Input Inference

Optimal control under uncertainty is a prevailing challenge in control, due to the difficulty in producing tractable solutions for the stochastic optimization problem. By framing the control problem as one of input estimation, advanced approximate inference techniques can be used to handle the statistical approximations in a principled and practical manner. Analyzing the Gaussian setting, we present a solver capable of several stochastic control methods, and was found to be superior to popular baselines on nonlinear simulated tasks. We draw connections that relate this inference formulation to previous approaches for stochastic optimal control, and outline several advantages that this inference view brings due to its statistical nature.
CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From the State-of-The-Art to DynamicNet

The accurate detection of motor imagery (MI) from electroencephalography (EEG) is a fundamental, as well as challenging, task to provide reliable control of robotic devices to support people suffering from neuro-motor impairments, e.g., in brain-computer interface (BCI) applications. Recently, deep learning approaches have been able to extract subject-independent features from EEG, to cope with its poor SNR and high intra-subject and cross-subject variability. In this paper, we first present a review of the most recent studies using deep learning for MI classification, with particular attention to their cross-subject performance. Second, we propose DynamicNet, a Python-based tool for quick and flexible implementations of deep learning models based on convolutional neural networks. We showcase the potentiality of DynamicNet by implementing EEGNet, a well-established architecture for effective EEG classification. Finally, we compare its performance with the filter bank common spatial pattern (FBCSP) in a 4-class MI task (data from a public dataset). To infer cross-subject classification performance, we applied three different cross-validation schemes. From our results, we show that EEGNet implemented with DynamicNet outperforms FBCSP by about 25 %, with a statistically significant difference when cross-subject validation schemes are applied. We conclude that deep learning approaches might be particularly helpful to provide higher cross-subject classification performance in multiclass MI classification scenarios. In the future, it is expected to improve DynamicNet to implement new architectures to further investigate cross-subject classification of MI tasks in real-world scenarios.
Dynamics of 2D material membranes

The dynamics of suspended two-dimensional (2D) materials has received increasing attention during the last decade, yielding new techniques to study and interpret the physics that governs the motion of atomically thin layers. This has led to insights into the role of thermodynamic and nonlinear effects as well as the mechanisms that govern dissipation and stiffness in these resonators. In this review, we present the current state-of-the-art in the experimental study of the dynamics of 2D membranes. The focus will be both on the experimental measurement techniques and on the interpretation of the physical phenomena exhibited by atomically thin membranes in the linear and nonlinear regimes. We will show that resonant 2D membranes have emerged both as sensitive probes of condensed matter physics in ultrathin layers, and as sensitive elements to monitor small external forces or other changes in the environment. New directions for utilizing suspended 2D membranes for material characterization, thermal transport, and gas interactions will be discussed and we conclude by outlining the challenges and opportunities in this upcoming field.
On stochastic stabilization in sample-and-hold sense

Sample-and-hold refers to implementation of control policies in a digital mode, in which the dynamical system is treated continuous, whereas the control actions are held constant in predefined time steps. In such a setup, special attention should be paid to the sample-to-sample behavior of the involved Lyapunov function. This paper extends on the stochastic stability results specifically to address for the sampleand-hold mode. We show that if a Markov policy stabilizes the system in a suitable sense, then it also practically stabilizes it in the sample-and-hold sense. This establishes a bridge from an idealized continuous application of the policy to its digital implementation. The central results applies to dynamical systems described by stochastic differential equations driven by the standard Brownian motion. Some generalizations are discussed, including the case of non-smooth Lyapunov functions, given that the controls are bounded or, alternatively, the driving noise possesses bounded realizations. A brief overview of models of such processes is given.
Global Wheat Head Dataset 2021: an update to improve the benchmarking wheat head localization with more diversity

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4,700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience in 2020, a few avenues for improvements have been identified, especially from the perspective of data size, head diversity and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and augmented by adding 1,722 images from 5 additional countries, allowing for 81,553 additional wheat heads to be added. We would hence like to release a new version of the Global Wheat Head Detection (GWHD) dataset in 2021, which is bigger, more diverse, and less noisy than the 2020 version. The GWHD 2021 is now publicly available at http://www.global-wheat.com/ and a new data challenge has been organized on AIcrowd to make use of this updated dataset.
A CCG-Based Version of the DisCoCat Framework

While the DisCoCat model (Coecke et al., 2010) has been proved a valuable tool for studying compositional aspects of language at the level of semantics, its strong dependency on pregroup grammars poses important restrictions: first, it prevents large-scale experimentation due to the absence of a pregroup parser; and second, it limits the expressibility of the model to context-free grammars. In this paper we solve these problems by reformulating DisCoCat as a passage from Combinatory Categorial Grammar (CCG) to a category of semantics. We start by showing that standard categorial grammars can be expressed as a biclosed category, where all rules emerge as currying/uncurrying the identity; we then proceed to model permutation-inducing rules by exploiting the symmetry of the compact closed category encoding the word meaning. We provide a proof of concept for our method, converting “Alice in Wonderland” into DisCoCat form, a corpus that we make available to the community.
Crofton formulas in pseudo-Riemannian space forms

Crofton formulas on simply connected Riemannian space forms allow the volumes, or more generally the Lipschitz–Killing curvature integrals of a submanifold with corners, to be computed by integrating the Euler characteristic of its intersection with all geodesic submanifolds. We develop a framework of Crofton formulas with distributions replacing measures, which has in its core Alesker's Radon transform on valuations. We then apply this framework, and our recent Hadwiger-type classification, to compute explicit Crofton formulas for all isometry-invariant valuations on all pseudospheres, pseudo-Euclidean and pseudohyperbolic spaces. We find that, in essence, a single measure which depends analytically on the metric, gives rise to all those Crofton formulas through its distributional boundary values at parts of the boundary corresponding to the different indefinite signatures. In particular, the Crofton formulas we obtain are formally independent of signature.
Automatic Fake News Detection: Are Models Learning to Reason?

Most fact checking models for automatic fake news detection are based on reasoning: given a claim with associated evidence, the models aim to estimate the claim veracity based on the supporting or refuting content within the evidence. When these models perform well, it is generally assumed to be due to the models having learned to reason over the evidence with regards to the claim. In this paper, we investigate this assumption of reasoning, by exploring the relationship and importance of both claim and evidence. Surprisingly, we find on political fact checking datasets that most often the highest effectiveness is obtained by utilizing only the evidence, as the impact of including the claim is either negligible or harmful to the effectiveness. This highlights an important problem in what constitutes evidence in existing approaches for automatic fake news detection.
Storage Aware Routing for Generalized Delay Tolerant Networks

This paper presents a novel storage aware routing (STAR) protocol designed to provide a general networking solution over a broad range of wired and wireless usage scenarios. STAR enables routing policies which adapt seamlessly from a well-connected wired network to a disconnected wireless network. STAR uses a 2-Dimensional routing metric composed of a short and a long term route cost and storage availability on downstream routers to make store or forward routing decisions. Temporary in-network storage is preferred over forwarding along a path that is slower than average and opportunistic transmission is encouraged when faster than average routes become available. Results from ns2 based simulations show that STAR achieves 40 − 50% higher throughput compared to OLSR in mobile vehicular and DTN scenarios and does 12 − 20% better than OLSR in the static mesh case. Experimental evaluation of STAR on the ORBIT testbed validates the protocol implementation, and demonstrates significant performance improvements with 25% higher peak throughput compared to OLSR in a wireless mesh network.
Learning User Embeddings from Temporal Social Media Data: A Survey

User-generated data on social media contain rich information about who we are, what we like and how we make decisions. In this paper, we survey representative work on learning a concise latent user representation (a.k.a. user embedding) that can capture the main characteristics of a social media user. The learned user embeddings can later be used to support different downstream user analysis tasks such as personality modeling, suicidal risk assessment and purchase decision prediction. The temporal nature of user-generated data on social media has largely been overlooked in much of the existing user embedding literature. In this survey, we focus on research that bridges the gap by incorporating temporal/sequential information in user representation learning. We categorize relevant papers along several key dimensions, identify limitations in the current work and suggest future research directions.
Complex Frequency

The paper introduces the concept of complex frequency. The imaginary part of the complex frequency is the variation with respect of a synchronous reference of the local bus frequency as commonly defined in power system studies. The real part is defined based on the variation of the voltage magnitude. The latter term is crucial for the correct interpretation and analysis of the variation of the frequency at each bus of the network. The paper also develops a set of differential equations that describe the link between complex powers and complex frequencies at network buses in transient conditions. No simplifications are assumed except for the usual approximations of the models utilized for the transient stability analysis of power systems. A variety of analytical and numerical examples show the applications and potentials of the proposed concept.
High frame-rate phase camera for high-resolution wavefront sensing in gravitational-wave detectors

We present a novel way of wavefront sensing using a commercially available, continuous-wave time-of-flight camera with QVGA-resolution. This CMOS phase camera is capable of sensing externally modulated light sources with frequencies up to 100 MHz. The high-spatial-resolution of the sensor, combined with our integrated control electronics, allows the camera to image power modulation index as low as -62 dBc/second/pixel. The phase camera is applicable to problems where alignment and mode-mismatch sensing is needed and suited for diagnostic and control applications in gravitational-wave detectors. Specifically, we explore the use of the phase camera in sensing the beat signals due to thermal distortions from point-like heat absorbers on the test masses in the Advanced LIGO detectors. The camera is capable of sensing optical path distortions greater than about two nanometers in the Advanced LIGO input mirrors, limited by the phase resolution. In homodyne readout, the performance can reach up to 0.1 nm, limited by the modulation amplitude sensitivity.
DISCO Verification: Division of Input Space into COnvex polytopes for neural network verification

The impressive results of modern neural networks partly come from their non linear behaviour. Unfortunately, this property makes it very difficult to apply formal verification tools, even if we restrict ourselves to networks with a piecewise linear structure. However, such networks yields subregions that are linear and thus simpler to analyse independently. In this paper, we propose a method to simplify the verification problem by operating a partitionning into multiple linear subproblems. To evaluate the feasibility of such an approach, we perform an empirical analysis of neural networks to estimate the number of linear regions, and compare them to the bounds currently known. We also present the impact of a technique aiming at reducing the number of linear regions during training.
Parton wave function for the fractional quantum Hall effect at 
ν=6/17

We consider the fractional quantum Hall effect at the filling ν = 6/17, where experiments have observed features of incompressibility in the form of a minimum in the longitudinal resistance. We propose a parton state denoted as “32̄1” and show it to be a feasible candidate to capture the ground state at ν = 6/17. We work out the low-energy effective theory of the 32̄1 edge and make several predictions for experimentally measurable properties of the state which can help detect its underlying topological order. Intriguingly, we find that the 32̄1 state likely lies in the same universality class as the state obtained from composite-fermionizing the 1+1/5 Laughlin state.
Computational methods for large-scale inverse problems: a survey on hybrid projection methods

This paper surveys an important class of methods that combine iterative projection methods and variational regularization methods for large-scale inverse problems. Iterative methods such as Krylov subspace methods are invaluable in the numerical linear algebra community and have proved important in solving inverse problems due to their inherent regularizing properties and their ability to handle large-scale problems. Variational regularization describes a broad and important class of methods that are used to obtain reliable solutions to inverse problems, whereby one solves a modified problem that incorporates prior knowledge. Hybrid projection methods combine iterative projection methods with variational regularization techniques in a synergistic way, providing researchers with a powerful computational framework for solving very large inverse problems. Although the idea of a hybrid Krylov method for linear inverse problems goes back to the 1980s, several recent advances on new regularization frameworks and methodologies have made this field ripe for extensions, further analyses, and new applications. In this paper, we provide a practical and accessible introduction to hybrid projection methods in the context of solving large (linear) inverse problems.
Herriott-cavity-assisted closed-loop Xe isotope comagnetometer

We present in this paper a Herriott-cavity-assisted closed-loop Xe isotope gas comagnetometer. In this system, $^{129}$Xe and $^{131}$Xe atoms are pumped and probed by polarized Rb atoms, and continuously driven by oscillating magnetic fields, whose frequencies are kept on resonance by phase-locked loops (PLLs). Different from other schemes, we use a Herriott cavity to improve the Rb magnetometer sensitivity instead of the parametric modulation method, and this passive method is aimed to improve the system stability while maintaining the sensitivity. This system has demonstrated an angle random walk (ARW) of 0.06 $^\circ$/h$^{1/2}$, and a bias instability of 0.2 $^\circ$/h (0.15 $\mu$Hz) with a bandwidth of 1.5 Hz. By adding a closed-loop Rb isotope comagnetometer, we can extend this system to dual simultaneously working comagnetometers sharing the same cell. This extended system has wide applications in precision measurements, where we can simultaneously and independently measure the coupling of anomalous fields with proton spin and neutron spin.
Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges

The Non-Fungible Token (NFT) market is mushrooming in recent years. The concept of NFT originally comes from a token standard of Ethereum, aiming to distinguish each token with distinguishable signs. This type of token can be bound with virtual/digital properties as their unique identifications. With NFTs, all marked properties can be freely traded with customized values according to their ages, rarity, liquidity, etc. It has greatly stimulated the prosperity of the decentralized application (DApp) market. At the time of writing (May 2021), the total money used on completed NFT sales has reached $34,530,649.86$ USD. The thousandfold return on its increasing market draws huge attention worldwide. However, the development of the NFT ecosystem is still in its early stage, and the technologies of NFTs are pre-mature. Newcomers may get lost in their frenetic evolution due to the lack of systematic summaries. In this technical report, we explore the NFT ecosystems in several aspects. We start with an overview of state-of-the-art NFT solutions, then provide their technical components, protocols, standards, and desired proprieties. Afterward, we give a security evolution, with discussions on the perspectives of their design models, opportunities, and challenges. To the best of our knowledge, this is the first systematic study on the current NFT ecosystems.
Dependency Parsing as MRC-based Span-Span Prediction

Higher-order methods for dependency parsing can partially but not fully address the issue that edges in dependency trees should be constructed at the text span/subtree level rather than word level. In this paper, we propose a new method for dependency parsing to address this issue. The proposed method constructs dependency trees by directly modeling span-span (in other words, subtree-subtree) relations. It consists of two modules: the text span proposal module which proposes candidate text spans, each of which represents a subtree in the dependency tree denoted by (root, start, end); and the span linking module, which constructs links between proposed spans. We use the machine reading comprehension (MRC) framework as the backbone to formalize the span linking module, where one span is used as query to extract the text span/subtree it should be linked to. The proposed method has the following merits: (1) it addresses the fundamental problem that edges in a dependency tree should be constructed between subtrees; (2) the MRC framework allows the method to retrieve missing spans in the span proposal stage, which leads to higher recall for eligible spans. Extensive experiments on the PTB, CTB and Universal Dependencies (UD) benchmarks demonstrate the effectiveness of the proposed method. The code is available at https://github.com/ShannonAI/mrc-for-dependency-parsing
A study of submillimeter methanol absorption toward PKS 1830−211:

Context. Methanol is an important tracer to probe physical and chemical conditions in the interstellar medium of galaxies. Methanol is also the most sensitive target molecule for probing potential space-time variations of the proton-electron mass ratio, μ, a dimensionless constant of nature. Aims. We present an extensive study of the strongest submillimeter absorption lines of methanol (with rest frequencies between 300 and 520 GHz) in the z = 0.89 molecular absorber toward PKS 1830−211, the only high-redshift object in which methanol has been detected. Our goals are to constrain the excitation of the methanol lines and to investigate the cosmological invariance of μ based on their relative kinematics. Methods. We observed 14 transitions of methanol, five of the A-form and nine of the E-form, and three transitions of A-13CH3OH, with ALMA. We analyzed the line profiles with a Gaussian fitting and constructed a global line profile that is able to match all observations after allowing for variations of the source covering factor, line opacity scaling, and relative bulk velocity offsets. We explore methanol excitation by running the non local thermal equilibrium radiative transfer code RADEX on a grid of kinetic temperatures and H2 volume densities. Results. Methanol absorption is detected in only one of the two lines of sight (the southwest) to PKS 1830−211. There, the excitation analysis points to a cool (∼10 − 20 K) and dense (∼104 − 5 cm−3) methanol gas. Under these conditions, several methanol transitions become anti-inverted, with excitation temperatures below the temperature of the cosmic microwave background. In addition, we measure an abundance ratio A/E = 1.0 ± 0.1, an abundance ratio CH3OH/H2 ∼ 2 × 10−8, and a 12CH3OH/13CH3OH ratio 62 ± 3. Our analysis shows that the bulk velocities of the different transitions are primarily correlated with the observing epoch due to morphological changes in the background quasar’s emission. There is a weaker correlation between bulk velocities and the lower level energies of the transitions, which could be a signature of temperature-velocity gradients in the absorbing gas. As a result, we do not find evidence for variations of μ, and we estimate Δμ/μ=(−1.8 ± 1.2) × 10−7 at 1-σ from our multivariate linear regression. Conclusions. We set a robust upper limit |Δμ/μ| < 3.6 × 10−7 (3σ) for the invariance of μ at a look-back time of half the present age of the Universe. Our analysis highlights that systematics need to be carefully taken into account in future radio molecular absorption studies aimed at testing Δμ/μ below the 10−7 horizon.
Bounds on Negative Binomial Approximation to Call Function

In this paper, we develop Stein’s method for negative binomial distribution using call function defined by fz(k) = (k − z) + = max{k − z, 0}, for k ≥ 0 and z ≥ 0. We obtain error bounds between E[fz(Nr,p)] and E[fz(V )], where Nr,p follows negative binomial distribution and V is the sum of locally dependent random variables, using certain conditions on moments. We demonstrate our results through an interesting application, namely, collateralized debt obligation (CDO), and compare the bounds with the existing bounds.