Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Invariant Representation of Tasks for Robust Surgical State Estimation

Surgical state estimators in robot-assisted surgery (RAS)-especially those trained via learning techniques-rely heavily on datasets that capture surgeon actions in laboratory or real-world surgical tasks. Real-world RAS datasets are costly to acquire, are obtained from multiple surgeons who may use different surgical strategies, and are recorded under uncontrolled conditions in highly complex environments. The combination of high diversity and limited data calls for new learning methods that are robust and invariant to operating conditions and surgical techniques. We propose StiseNet, a Surgical Task Invariance State Estimation Network with an invariance induction framework that minimizes the effects of variations in surgical technique and operating environments inherent to RAS datasets. StiseNet's adversarial architecture learns to separate nuisance factors from information needed for surgical state estimation. StiseNet is shown to outperform state-of-the-art state estimation methods on three datasets (including a new real-world RAS dataset: HERNIA-20).
Combining Events and Frames Using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction

Event cameras are novel vision sensors that report per-pixel brightness changes as a stream of asynchronous “events”. They offer significant advantages compared to standard cameras due to their high temporal resolution, high dynamic range and lack of motion blur. However, events only measure the varying component of the visual signal, which limits their ability to encode scene context. By contrast, standard cameras measure absolute intensity frames, which capture a much richer representation of the scene. Both sensors are thus complementary. However, due to the asynchronous nature of events, combining them with synchronous images remains challenging, especially for learning-based methods. This is because traditional recurrent neural networks (RNNs) are not designed for asynchronous and irregular data from additional sensors. To address this challenge, we introduce Recurrent Asynchronous Multimodal (RAM) networks, which generalize traditional RNNs to handle asynchronous and irregular data from multiple sensors. Inspired by traditional RNNs, RAM networks maintain a hidden state that is updated asynchronously and can be queried at any time to generate a prediction. We apply this novel architecture to monocular depth estimation with events and frames where we show an improvement over state-of-the-art methods by up to 30% in terms of mean absolute depth error. To enable further research on multimodal learning with events, we release EventScape, a new dataset with events, intensity frames, semantic labels, and depth maps recorded in the CARLA simulator.
Core-softened water-alcohol mixtures: the solute-size effects.

Water is the most anomalous material on Earth, with a long list of thermodynamic, dynamic and structural behaviors that deviate from what is expected. Recent studies have indicated that these anomalies may be related to a competition between two liquids, which means that water has a potential liquid-liquid phase transition (LLPT) that ends at a liquid-liquid critical point (LLCP). In a recent study [J. Mol. Liq. , 2020, 320, 114420], using molecular dynamics simulations and a core-softened potential approach, we have shown that adding a simple solute such as methanol can "kill" the density-anomalous behavior as the LLCP is suppressed by spontaneous crystallization in a hexagonal close packing (HCP) crystal near the LLPT. Now, we extend this work to realize how longer-chain alcohols will affect the complex behavior of water-alcohol mixtures in the supercooled regime. Besides core-softened (CS) methanol, ethanol and 1-propanol were added to a system of identical particles that interact through the continuous shouldered well (CSW) potential. We observed that the density anomaly gradually decreases its extension in phase diagrams until it disappears with the growth of the non-polar chain and the alcohol concentration, different from the liquid-liquid phase transition (and the LLCP), which remained present in all analyzed mixtures, according to Nature, 2001, 409, 692. For our model, the longer non-polar chains and higher concentrations gradually impact the competition between the scales in the CS potential, leading to a gradual disappearing of the anomalies until the TMD total disappearance is observed when the first coordination shell structure is also affected: short-range ordering is favored, leading to less competition between short- and long-range ordering and, consequently, to the extinction of anomalies. Also, the non-polar chain size and concentration have an effect on the solid phases, favoring the hexagonal close packed (HCP) solid and the amorphous solid phase over the body-centered cubic (BCC) crystal. These findings help to elucidate the behavior of water solutions in the supercooled regime and indicate that the LLCP can be observed in systems without density-anomalous behavior.
Electron Spectrum for the Prompt Emission of Gamma-Ray Bursts in the Synchrotron Radiation Scenario

Growing evidence indicates that the synchrotron radiation mechanism may be responsible for the prompt emission of gamma-ray bursts (GRBs). In the synchrotron radiation scenario, the electron energy spectrum of the prompt emission is diverse in theoretical works and has not been estimated from observations in a general way (i.e., without specifying a certain physical model for the electron spectrum). In this paper, we creatively propose a method to directly estimate the electron spectrum for the prompt emission, without specifying a certain physical model for the electron spectrum in the synchrotron radiation scenario. In this method, an empirical function (i.e., a four-order Bézier curve jointed with a linear function at high energy) is applied to describe the electron spectrum in log–log coordinates. It is found that our empirical function can well mimic the electron spectra obtained in many numerical calculations or simulations. Then, our method can figure out the electron spectrum for the prompt emission without specifying a model. By employing our method on observations, taking GRB 180720B and GRB 160509A as examples, it is found that the obtained electron spectra are generally different from that in the standard fast-cooling scenario and even a broken power law. Moreover, the morphology of electron spectra in its low-energy regime varies with time in a burst and even in a pulse. Our proposed method provides a valuable way to confront the synchrotron radiation mechanism with observations.
VAE Approximation Error: ELBO and Conditional Independence

The importance of Variational Autoencoders reaches far beyond standalone generative models — the approach is also used for learning latent representations and can be generalized to semi-supervised learning. This requires a thorough analysis of their commonly known short-comings: posterior collapse and approximation errors. This paper analyzes VAE approximation errors caused by the combination of the ELBO objective with the choice of the encoder probability family, in particular under conditional in-dependence assumptions. We identify the sub-class of generative models consistent with the encoder family. We show that the ELBO optimizer is pulled from the likelihood optimizer towards this consistent subset. Furthermore, this subset can not be enlarged, and the respective error cannot be decreased, by only considering deeper encoder networks.
Strategic bidding in freight transport using deep reinforcement learning

This paper presents a multi-agent reinforcement learning algorithm to represent strategic bidding behavior in freight transport markets. Using this algorithm, we investigate whether feasible market equilibriums arise without any central control or communication between agents. Studying behavior in such environments may serve as a stepping stone towards self-organizing logistics systems like the Physical Internet. We model an agent-based environment in which a shipper and a carrier actively learn bidding strategies using policy gradient methods, posing bidand ask prices at the individual container level. Both agents aim to learn the best response given the expected behavior of the opposing agent. A neutral broker allocates jobs based on bid-ask spreads. Our game-theoretical analysis and numerical experiments focus on behavioral insights. To evaluate system performance, we measure adherence to Nash equilibria, fairness of reward division and utilization of transport capacity. We observe good performance both in predictable, deterministic settings (∼95% adherence to Nash equilibria) and highly stochastic environments (∼85% adherence). Risk-seeking behavior may increase an agent’s reward share, as long as the strategies are not overly aggressive. The results suggest a potential for full automation and decentralization of freight transport markets. 1 ar X iv :2 10 2. 09 25 3v 1 [ cs . L G ] 1 8 Fe b 20 21
Generalization in Quantum Machine Learning: a Quantum Information Perspective

Quantum classification and hypothesis testing are two tightly related subjects, the main difference being that the former is data driven: how to assign to quantum states $\rho(x)$ the corresponding class $c$ (or hypothesis) is learnt from examples during training, where $x$ can be either tunable experimental parameters or classical data"embedded"into quantum states. Does the model generalize? This is the main question in any data-driven strategy, namely the ability to predict the correct class even of previously unseen states. Here we establish a link between quantum machine learning classification and quantum hypothesis testing (state and channel discrimination) and then show that the accuracy and generalization capability of quantum classifiers depend on the (R\'enyi) mutual informations $I(C{:}Q)$ and $I_2(X{:}Q)$ between the quantum state space $Q$ and the classical parameter space $X$ or class space $C$. Based on the above characterization, we then show how different properties of $Q$ affect classification accuracy and generalization, such as the dimension of the Hilbert space, the amount of noise, and the amount of neglected information from $X$ via, e.g., pooling layers. Moreover, we introduce a quantum version of the Information Bottleneck principle that allows us to explore the various tradeoffs between accuracy and generalization. Finally, in order to check our theoretical predictions, we study the classification of the quantum phases of an Ising spin chain, and we propose the Variational Quantum Information Bottleneck (VQIB) method to optimize quantum embeddings of classical data to favor generalization.
A Novel Non-Invasive Estimation of Respiration Rate from Photoplethysmograph Signal Using Machine Learning Model

Respiratory ailments such as asthma, chronic obstructive pulmonary disease (COPD), pneumonia, and lung cancer are life-threatening. Respiration rate (RR) is a vital indicator of the wellness of a patient. Continuous monitoring of RR can provide early indication and thereby save lives. However, a real-time continuous RR monitoring facility is only available at the intensive care unit (ICU) due to the size and cost of the equipment. Recent researches have proposed Photoplethysmogram (PPG) and/ Electrocardiogram (ECG) signals for RR estimation however, the usage of ECG is limited due to the unavailability of it in wearable devices. Due to the advent of wearable smartwatches with built-in PPG sensors, it is now being considered for continuous monitoring of RR. This paper describes a novel approach to RR estimation using machine learning (ML) models with the PPG signal features. Feature selection algorithms were used to reduce computational complexity and the chance of overfitting. The best ML model and the best feature selection algorithm combination was fine-tuned to optimize its performance using hyperparameter optimization. Gaussian Process Regression (GPR) with fitrgp feature selection algorithm outperformed all other combinations and exhibits a root mean squared error (RMSE), mean absolute error (MAE), and two-standard deviation (2SD) of 2.57, 1.91, and 5.13 breaths per minute, respectively. This ML model based RR estimation can be embedded in wearable devices for real-time continuous monitoring of the patient.
X-raying the galaxy pair Arp 41: no collision in NGC 1232 and three ultraluminous sources in NGC 1232A

We studied the apparent galaxy pair NGC 1232/NGC 1232A with Chandra, looking for evidence of interactions and collisions. We report that there is no cloud of diffuse emission in NGC 1232, contrary to previous claims in the literature. Instead, we find that the small ‘companion’ galaxy NGC 1232A contains three ultraluminous X-ray sources with peak 0.3–10 keV luminosities above 1040 erg s−1 (assuming a cosmological distance of ≈93 Mpc for this galaxy). For its mass, morphology, metal abundance, and bright ULX population, NGC 1232A is analogous to the more nearby late-type spiral NGC 1313.
Learning Continuous Exponential Families Beyond Gaussian

We address the problem of learning of continuous exponential family distributions with unbounded support. While a lot of progress has been made on learning of Gaussian graphical models, we still lack scalable algorithms for reconstructing general continuous exponential families modeling higher-order moments of the data beyond the mean and the covariance. Here, we introduce a computationally efficient method for learning continuous graphical models based on the Interaction Screening approach. Through a series of numerical experiments, we show that our estimator maintains similar requirements in terms of accuracy and sample complexity scalings compared to alternative approaches such as maximization of conditional likelihood, while considerably improving upon the algorithm's run-time.
AggFT: Low-Cost Fault-Tolerant Smart Meter Aggregation with Proven Termination and Privacy

Smart meter data aggregation protocols have been developed to address rising privacy threats against customers' consumption data. However, these protocols do not work satisfactorily in the presence of failures of smart meters or network communication links. In this paper, we propose a lightweight and fault-tolerant aggregation algorithm that can serve as a solid foundation for further research. We revisit an existing error-resilient privacy-preserving aggregation protocol based on masking and improve it by: (i) performing changes in the cryptographic parts that lead to a reduction of computational costs, (ii) simplifying the behaviour of the protocol in the presence of faults, and showing a proof of proper termination under a well-defined failure model, (iii) decoupling the computation part from the data flow so that the algorithm can also be used with homomorphic encryption as a basis for privacy-preservation. To best of our knowledge, this is the first algorithm that is formulated for both, masking and homomorphic encryption. (iv) Finally, we provide a formal proof of the privacy guarantee under failure. The systematic treatment with strict proofs and the established connection to graph theory may also serve as a starting point for possible generalizations and improvements with respect to increased resilience.
Latency Modeling of Hyperledger Fabric for Blockchain-based IoT (BC-IoT) Networks

—With the worldwide growth of IoT industry, the need for a strong security level for IoT networks has also increased, leading to blockchain-based IoT (BC-IoT) networks. While blockchain technology is leveraged to ensure data integrity in a distributed manner, Hyperledger Fabric (HLF) attracts attention with its distinctive strong point without requiring the power-consuming consensus protocol, that is, proof-of-work (PoW). However, even though such security concerns can be mitigated using HLF, the additional processing time spent in HLF may emerge as another issue because most IoT devices handle real-time and latency critical jobs. This problem still remains unresolved because of the absence of a HLF latency model and a parameter setup guideline to reducing the mean latency. In this paper, therefore, we develop a HLF latency model for HLF-based IoT networks based on probability distribution ﬁtting, by which mean latency prediction is facilitated once probable conﬁguration environments are determined, in terms of the block size, block-generation timeout, and transaction generation rate parameters. Furthermore, we conclude by analyzing the impacts of inﬂuential HLF parameters on the mean latency, in order to provide insights not only on optimizing the mean latency, but also on coping with long mean latency.
Regular Expressions for Fast-response COVID-19 Text Classification

Text classifiers are at the core of many NLP applications and use a variety of algorithmic approaches and software. This paper introduces infrastructure and methodologies for text classifiers based on large-scale regular expressions. In particular, we describe how Facebook determines if a given piece of text - anything from a hashtag to a post - belongs to a narrow topic such as COVID-19. To fully define a topic and evaluate classifier performance we employ human-guided iterations of keyword discovery, but do not require labeled data. For COVID-19, we build two sets of regular expressions: (1) for 66 languages, with 99% precision and recall >50%, (2) for the 11 most common languages, with precision >90% and recall >90%. Regular expressions enable low-latency queries from multiple platforms. Response to challenges like COVID-19 is fast and so are revisions. Comparisons to a DNN classifier show explainable results, higher precision and recall, and less overfitting. Our learnings can be applied to other narrow-topic classifiers.
Coherent scattering-mediated correlations between levitated nanospheres

We explore entanglement generation between multiple optically levitated nanospheres interacting with a common optical cavity via the coherent scattering optomechanical interaction. We derive the many-particle Hamiltonian governing the unitary evolution of the system and show that it gives rise to quantum correlations among the various partitions of the setup, following a non-Markovian dynamics of entanglement birth, death and revivals. We also consider the effects of coupling the system to external environments and show that under reasonable experimental conditions entanglement between the mechanical modes can survive even at room temperature. Its dependence upon the number of nanoparticles, their initial temperature and coupling strength is studied. A numerical toolbox to simulate the closed and open dynamics of Gaussian optomechanical states and their informational measures is developed.
Clockwork Variational Autoencoders for Video Prediction

Deep learning has enabled algorithms to generate realistic images. However, accurately predicting long video sequences requires understanding long-term dependencies and remains an open challenge. While existing video prediction models succeed at generating sharp images, they tend to fail at accurately predicting far into the future. We introduce the Clockwork VAE (CW-VAE), a video prediction model that leverages a hierarchy of latent sequences, where higher levels tick at slower intervals. We demonstrate the beneﬁts of both hierarchical latents and temporal abstraction on 4 diverse video prediction datasets with sequences of up to 1000 frames, where CW-VAE outperforms top video prediction models. Additionally, we propose a Minecraft benchmark for long-term video prediction. We conduct several experiments to gain insights into CW-VAE and conﬁrm that slower levels learn to represent objects that change more slowly in the video, and faster levels learn to represent faster objects.
An Enhanced Adversarial Network with Combined Latent Features for Spatio-Temporal Facial Affect Estimation in the Wild

Affective Computing has recently attracted the attention of the research community, due to its numerous applications in diverse areas. In this context, the emergence of video-based data allows to enrich the widely used spatial features with the inclusion of temporal information. However, such spatio-temporal modelling often results in very high-dimensional feature spaces and large volumes of data, making training difficult and time consuming. This paper addresses these shortcomings by proposing a novel model that efficiently extracts both spatial and temporal features of the data by means of its enhanced temporal modelling based on latent features. Our proposed model consists of three major networks, coined Generator, Discriminator, and Combiner, which are trained in an adversarial setting combined with curriculum learning to enable our adaptive attention modules. In our experiments, we show the effectiveness of our approach by reporting our competitive results on both the AFEW-VA and SEWA datasets, suggesting that temporal modelling improves the affect estimates both in qualitative and quantitative terms. Furthermore, we find that the inclusion of attention mechanisms leads to the highest accuracy improvements, as its weights seem to correlate well with the appearance of facial movements, both in terms of temporal localisation and intensity. Finally, we observe the sequence length of around 160\,ms to be the optimum one for temporal modelling, which is consistent with other relevant findings utilising similar lengths.
Hypergraph Discretization of the Cauchy Problem in General Relativity via Wolfram Model Evolution

Although the traditional form of the Einstein field equations is intrinsically four-dimensional, the field of numerical general relativity focuses on the reformulation of these equations as a 3 + 1-dimensional Cauchy problem, in which Cauchy initial data is specified on a three-dimensional spatial hypersurface, and then evolved forwards in time. The Wolfram model offers an inherently discrete formulation of the Einstein field equations as an a priori Cauchy problem, in which Cauchy initial data is specified on a single spatial hypergraph, and then evolved by means of hypergraph substitution rules, with the resulting causal network corresponding to the conformal structure of spacetime. This article introduces a new numerical general relativity code based upon the conformal and covariant Z4 (CCZ4) formulation with constraint-violation damping, with the option to reduce to the standard BSSN formalism if desired, with Cauchy data defined over hypergraphs; the code incorporates an unstructured generalization of the adaptive mesh refinement technique proposed by Berger and Colella, in which the topology of the hypergraph is refined or coarsened based upon local conformal curvature terms. We validate this code numerically against a variety of standard spacetimes, including Schwarzschild black holes, Kerr black holes, maximally extended Schwarzschild black holes, and binary black hole mergers (both rotating and non-rotating), and explicitly illustrate the relationship between the discrete hypergraph topology and the continuous Riemannian geometry that is being approximated. Finally, we compare the results produced by this code to the results obtained by means of pure Wolfram model evolution (without the underlying PDE system), using a hypergraph substitution rule that provably satisfies the Einstein field equations in the continuum limit.
Deep Extreme Value Copulas for Estimation and Sampling

We propose a new method for modeling the distribution function of high dimensional extreme value distributions. The Pickands dependence function models the relationship between the covariates in the tails, and we learn this function using a neural network that is designed to satisfy its required properties. Moreover, we present new methods for recovering the spectral representation of extreme distributions and propose a generative model for sampling from extreme copulas. Numerical examples are provided demonstrating the efficacy and promise of our proposed methods.