Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimalist and High-performance Conversational Recommendation with Uncertainty Estimation for User Preference

Conversational recommendation system (CRS) is emerging as a user-friendly way to capture users' dynamic preferences over candidate items and attributes. Multi-shot CRS is designed to make recommendations multiple times until the user either accepts the recommendation or leaves at the end of their patience. Existing works are trained with reinforcement learning (RL), which may suffer from unstable learning and prohibitively high demands for computing. In this work, we propose a simple and efficient CRS, MInimalist Non-reinforced Interactive COnversational Recommender Network (MINICORN). MINICORN models the epistemic uncertainty of the estimated user preference and queries the user for the attribute with the highest uncertainty. The system employs a simple network architecture and makes the query-vs-recommendation decision using a single rule. Somewhat surprisingly, this minimalist approach outperforms state-of-the-art RL methods on three real-world datasets by large margins. We hope that MINICORN will serve as a valuable baseline for future research.
Integrability of planar-algebraic models

The quantum inverse scattering method is a scheme for solving integrable models in 1+1 dimensions, building on an R-matrix that satisfies the Yang–Baxter equation (YBE) and in terms of which one constructs a commuting family of transfer matrices. In the standard formulation, this R-matrix acts on a tensor product of vector spaces. Here, we relax this tensorial property and develop a framework for describing and analysing integrable models based on planar algebras, allowing non-separable R-operators satisfying generalised YBEs. We also re-evaluate the notion of integrals of motion and characterise when an (algebraic) transfer operator is polynomial in a single integral of motion. We refer to such models as polynomially integrable. In an eight-vertex model, we demonstrate that the corresponding transfer operator is polynomial in the natural Hamiltonian. In the Temperley–Lieb loop model with loop fugacity β∈C , we likewise find that, for all but finitely many β-values, the transfer operator is polynomial in the usual Hamiltonian element of the Temperley–Lieb algebra TLn(β) , at least for n⩽17 . Moreover, we find that this model admits a second canonical Hamiltonian, and that this Hamiltonian also acts as a polynomial integrability generator for small n and all but finitely many β-values.
Collecting high-quality adversarial data for machine reading comprehension tasks with humans and models in the loop

We present our experience as annotators in the creation of high-quality, adversarial machine-reading-comprehension data for extractive QA for Task 1 of the First Workshop on Dynamic Adversarial Data Collection (DADC). DADC is an emergent data collection paradigm with both models and humans in the loop. We set up a quasi-experimental annotation design and perform quantitative analyses across groups with different numbers of annotators focusing on successful adversarial attacks, cost analysis, and annotator confidence correlation. We further perform a qualitative analysis of our perceived difficulty of the task given the different topics of the passages in our dataset and conclude with recommendations and suggestions that might be of value to people working on future DADC tasks and related annotation interfaces.
ALP dark matter from kinetic fragmentation: opening up the parameter window

The main mechanism responsible for Axion-Like-Particle (ALP) production in the early universe is the so-called misalignment mechanism. Three regimes have been investigated in this context: standard misalignment, large misalignment and kinetic misalignment. The latter applies if the axion inherits a large initial velocity in the early universe, such that the field rolls through many wiggles during its evolution, before it gets trapped in one minimum. This largely opens the region of parameter space for ALP dark matter towards higher values for the axion-photon coupling, which can be probed by the whole set of next decade's upcoming experiments. In fact, almost the entire parameter space in the [mass, decay constant] plane can now accommodate dark matter. In this paper, we show that in kinetic misalignment, the axion field is almost always entirely fragmented, meaning that the energy density of the homogeneous field is redistributed over higher-mode axions. We present a general model-independent analytical description of kinetic fragmentation, including discussion of the modified initial conditions for the mode functions due to the axion's initial velocity, and how they impact the growth of the adiabatic fluctuations. We calculate precisely the parameter regions corresponding respectively to standard misalignment, kinetic misalignment with weak fragmentation, fragmentation after trapping and fragmentation before trapping. While axion fragmentation can impact the precise determination of the relic abundance, another main observational implication is the formation of much denser compact axion halos, that is described in a companion paper. We also point out a new gravitational-wave signature that arises in the large misalignment regime with complete fragmentation and could be seen in measurements of μ distortions in the Cosmic Microwave Background.
Dual structures on Coxeter and Artin groups of rank three

We extend the theory of dual Coxeter and Artin groups to all rank-three Coxeter systems, beyond the previously studied spherical and affine cases. Using geometric, combinatorial, and topological techniques, we show that rank-three noncrossing partition posets are EL-shellable lattices and give rise to Garside groups isomorphic to the associated standard Artin groups. Within this framework, we prove the $K(\pi, 1)$ conjecture, the triviality of the center, and the solubility of the word problem for rank-three Artin groups. Some of our constructions apply to general Artin groups; we hope they will help develop complete solutions to the $K(\pi, 1)$ conjecture and other open problems in the area.
Crack propagation mechanisms in human cortical bone on different paired anatomical locations : biomechanical, tomographic and biochemical approaches

A fracture is estimated every three seconds in the world, leading to an increased risk of impairment or even mortality. The biomechanical knowledge of bone fracture mechanisms in a fall configuration of loading is of great interests for the development of clinical method for the prediction of the risk of fracture. Toughness seems to be a good candidate to investigate this fracture process as it corresponds to the energy needed to propagate a crack through cortical bone complex microstructure. The aim of this study was thus to evaluate human cortical bone toughness parameter under both quasi-static and fall-like loading conditions paired anatomical locations. Micro-computed tomography images using synchrotron radiation and collagen cross-links maturation measurements were performed to investigate the influence of the tissue architecture on crack propagation. Results found showed that under quasi-static condition, the different anatomical locations present different mechanical behavior. Radius significantly better resist crack propagation than the other studied location. Considering a fall-like loading condition, no more difference is observed between the locations but a significant decreased is measured compare to the first configuration. Human cortical bone has a better capacity to resist crack propagation under a standard quasi-static loading condition. By investigating the tissue morphometric and biochemical parameters, we observed different organization from a location to another that explains the mechanical differences. The architectural features appear to be determinant for crack propagation mechanisms under quasi-static condition, but they play a lesser role under fall-like condition. These results imply that the tissue microstructure is not a determinant when dealing with the prediction of the risk of fracture. Further work has to be done to reach out which parameters are more determinants under a specific fall-like loading condition
Fair Machine Learning in Healthcare: A Review

Beneﬁting from the digitization of healthcare data and the development of computing power, machine learning methods are increasingly used in the healthcare domain. Fairness problems have been identiﬁed in machine learning for healthcare, resulting in an unfair allocation of limited healthcare resources or excessive health risks for certain groups. Therefore, addressing the fairness problems has recently attracted increasing attention from the healthcare community. However, the intersection of machine learning for healthcare and fairness in machine learning remains understudied. In this review, we build the bridge by exposing fairness problems, summarizing possible biases, sorting out mitigation methods and pointing out challenges along with opportunities for the future.
Nonparametric signal detection with small values of type I and type II error probabilities

We consider problem of signal detection in Gaussian white noise. Test statistics are linear combinations of squares of estimators of Fourier coefficients or $\mathbb{L}_2$-norms of kernel estimators. We point out necessary and sufficient conditions when nonparametric sets of alternatives have a given rate of exponential decay for type II error probabilities.
Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision

Commercial and industrial deployments of robot fleets at Amazon, Nimble, Plus One, Waymo, and Zoox query remote human teleoperators when robots are at risk or unable to make task progress. With continual learning, interventions from the remote pool of humans can also be used to improve the robot fleet control policy over time. A central question is how to effectively allocate limited human attention. Prior work addresses this in the single-robot, single-human setting; we formalize the Interactive Fleet Learning (IFL) setting, in which multiple robots interactively query and learn from multiple human supervisors. We propose Return on Human Effort (ROHE) as a new metric and Fleet-DAgger, a family of IFL algorithms. We present an open-source IFL benchmark suite of GPU-accelerated Isaac Gym environments for standardized evaluation and development of IFL algorithms. We compare a novel Fleet-DAgger algorithm to 4 baselines with 100 robots in simulation. We also perform a physical block-pushing experiment with 4 ABB YuMi robot arms and 2 remote humans. Experiments suggest that the allocation of humans to robots significantly affects the performance of the fleet, and that the novel Fleet-DAgger algorithm can achieve up to 8.8x higher ROHE than baselines. See https://tinyurl.com/fleet-dagger for supplemental material.
DeepMB: Deep neural network for real-time model-based optoacoustic image reconstruction with adjustable speed of sound

: Multispectral optoacoustic tomography (MSOT) is a high-resolution functional imaging modality that can non-invasively access a broad range of pathophysiological phenomena by quantifying the contrast of endogenous chromophores in tissue. Real-time imaging is imperative to translate MSOT into clinical imaging, visualize dynamic pathophysiological changes associated with disease progression, and enable in situ diagnoses. Model-based reconstruction affords state-of-the-art optoacoustic images; however, the advanced image quality provided by model-based reconstruction remains inaccessible during real-time imaging because the algorithm is iterative and computationally demanding. Deep-learning may afford faster reconstructions for real-time optoacoustic imaging, but existing approaches only support oversimplified imaging settings and fail to generalize to in vivo data. In this work, we introduce a novel deep-learning framework, termed DeepMB, to learn the model-based reconstruction operator and infer optoacoustic images with state-of-the-art quality in less than 10 ms per image. DeepMB accurately generalizes to in vivo data after training on synthesized sinograms that are derived from real-world images. The framework affords in-focus images for a broad range of anatomical locations with various acoustic properties because it supports dynamic adjustment of the reconstruction speed of sound during imaging. Furthermore, DeepMB is compatible with the data rates and image sizes of modern multispectral optoacoustic tomography scanners, thus enabling straightforward adoption into clinical routine. We evaluate DeepMB both qualitatively and quantitatively on a diverse dataset of in vivo images and demonstrate that the framework reconstructs images 3000 times faster than the iterative model-based reference method while affording near-identical image qualities. Accurate and real-time image reconstructions with DeepMB can enable full access to the high-resolution and multispectral contrast of handheld optoacoustic tomography in deep tissue, thus facilitating advanced dynamic imaging applications.
No imputation without representation

Imputation allows datasets to be used with algorithms that cannot handle missing values by themselves. However, missing values may in principle contribute useful information that is lost through imputation. The missing-indicator approach can be used to preserve this information. There are several theoretical considerations why missing-indicators may or may not be beneficial, but there has not been any large-scale practical experiment on real-life datasets to test this question for machine learning predictions. We perform this experiment for three imputation strategies and a range of different classification algorithms, on the basis of twenty real-life datasets. We find that missing-indicators generally increase classification performance, and that nearest neighbour and iterative imputation do not lead to better performance than simple mean/mode imputation. Therefore, we recommend the use of missing-indicators with mean/mode imputation as a safe default, with the caveat that for decision trees, pruning is necessary to prevent overfitting.
A Survey on SDN \& SDCN Traffic Measurement: Existing Approaches and Research Challenge

Software Deﬁned Network (SDN) is the next generation network that decouples the control plane from the data plane of forwarding devices by utilizing the OpenFlow protocol as a communication link between the data plane and the control plane. However, there are some security issues might be in actions on SDN that the attackers can take control over the SDN control plane. Thus, trafﬁc measurement is a fundamental technique of protecting SDN against the high-security threats such as DDoS, heavy hitter, superspreader as well as live video calling, QoS control, high bandwidth requirement, resource management are also inevitable in SDN/Software Deﬁned Cellular Network (SDCN). In such a scenario, we survey SDN trafﬁc measurement solutions, in order to assess how these solutions can make a secured, efﬁcient and robust SDN/SDCN architecture. In this paper, various types of SDN trafﬁc measurement solutions have been categorized based on network applications behaviour. Furthermore, we ﬁnd out the challenges related to SDN/SDCN trafﬁc measurement and future scope of research, which will guide to design and develop more advanced trafﬁc measurement solutions for a scalable, heterogeneous, hierarchical and widely deployed SDN/SDCN in future prospects. More in details, we list out kinds of practical machine learning (ML) approaches to analyze how we can make improvement in the trafﬁc measurement performances. We conclude that using ML in SDN trafﬁc measurement solutions will give beneﬁt to get secured SDN/SDCN network in complementary ways.
Integrating IoT-Sensing and Crowdsensing with Privacy: Privacy-Preserving Hybrid Sensing for Smart Cities

Data sensing and gathering is an essential task for various information-driven services in smart cities. On the one hand, Internet of Things (IoT) sensors can be deployed at certain fixed locations to capture data reliably but suffer from limited sensing coverage. On the other hand, data can also be gathered dynamically through crowdsensing contributed by voluntary users but suffer from its unreliability and the lack of incentives for users’ contributions. In this article, we explore an integrated paradigm called “hybrid sensing” that harnesses both IoT-sensing and crowdsensing in a complementary manner. In hybrid sensing, users are incentivized to provide sensing data not covered by IoT sensors and provide crowdsourced feedback to assist in calibrating IoT-sensing. Their contributions will be rewarded with credits that can be redeemed to retrieve synthesized information from the hybrid system. In this article, we develop a hybrid sensing system that supports explicit user privacy—IoT sensors are obscured physically to prevent capturing private user data, and users interact with a crowdsensing server via a privacy-preserving protocol to preserve their anonymity. A key application of our system is smart parking, by which users can inquire and find the available parking spaces in outdoor parking lots. We implemented our hybrid sensing system for smart parking and conducted extensive empirical evaluations. Finally, our hybrid sensing system can be potentially applied to other information-driven services in smart cities.
Chinese Word Sense Embedding with SememeWSD and Synonym Set

Word embedding is a fundamental natural language processing task which can learn feature of words. However, most word embedding methods assign only one vector to a word, even if polysemous words have multi-senses. To address this limitation, we propose SememeWSD Synonym (SWSDS) model to assign a diﬀerent vector to every sense of polysemous words with the help of word sense disambiguation (WSD) and synonym set in OpenHowNet. We use the SememeWSD model, an unsupervised word sense disambiguation model based on OpenHowNet, to do word sense disambiguation and annotate the polysemous word with sense id. Then, we obtain top 10 synonyms of the word sense from OpenHowNet and calculate the average vector of synonyms as the vector of the word sense. In experiments, We evaluate the SWSDS model on semantic similarity calculation with Gensim’s wmdistance method. It achieves improvement of accuracy. We also examine the SememeWSD model on diﬀerent BERT models to ﬁnd the more eﬀective model.
Exponential Mixing by Shear Flows

We prove a version of Bressan's mixing conjecture where the advecting field is constrained to be a shear at each time. Also, inspired by recent work of Blumenthal, Coti Zelati and Gvalani, we construct a particularly simple example of a shear flow which mixes at the optimal rate. The constructed vector field alternates randomly in time between just two distinct shears.
Towards a Data-Driven Requirements Engineering Approach: Automatic Analysis of User Reviews

We are concerned by Data Driven Requirements Engineering, and in particular the consideration of user's reviews. These online reviews are a rich source of information for extracting new needs and improvement requests. In this work, we provide an automated analysis using CamemBERT, which is a state-of-the-art language model in French. We created a multi-label classification dataset of 6000 user reviews from three applications in the Health&Fitness field. The results are encouraging and suggest that it's possible to identify automatically the reviews concerning requests for new features. Dataset is available at: https://github.com/Jl-wei/APIA2022-French-user-reviews-classification-dataset.
From Kernel Methods to Neural Networks: A Unifying Variational Formulation

The minimization of a data-fidelity term and an additive regularization functional gives rise to a powerful framework for supervised learning. In this paper, we present a unifying regularization functional that depends on an operator $$\textrm{L}$$ L and on a generic Radon-domain norm. We establish the existence of a minimizer and give the parametric form of the solution(s) under very mild assumptions. When the norm is Hilbertian, the proposed formulation yields a solution that involves radial-basis functions and is compatible with the classical methods of machine learning. By contrast, for the total-variation norm, the solution takes the form of a two-layer neural network with an activation function that is determined by the regularization operator. In particular, we retrieve the popular ReLU networks by letting the operator be the Laplacian. We also characterize the solution for the intermediate regularization norms $$\Vert \cdot \Vert =\Vert \cdot \Vert _{L_p}$$ ‖ · ‖ = ‖ · ‖ L p with $$p\in (1,2]$$ p ∈ ( 1 , 2 ] . Our framework offers guarantees of universal approximation for a broad family of regularization operators or, equivalently, for a wide variety of shallow neural networks, including the cases (such as ReLU) where the activation function is increasing polynomially. It also explains the favorable role of bias and skip connections in neural architectures.
Visual Foresight With a Local Dynamics Model

Model-free policy learning has been shown to be capable of learning manipulation policies which can solve long-time horizon tasks using single-step manipulation primitives. However, training these policies is a time-consuming process requiring large amounts of data. We propose the Local Dynamics Model (LDM) which efficiently learns the state-transition function for these manipulation primitives. By combining the LDM with model-free policy learning, we can learn policies which can solve complex manipulation tasks using one-step lookahead planning. We show that the LDM is both more sample-efficient and outperforms other model architectures. When combined with planning, we can outperform other model-based and model-free policies on several challenging manipulation tasks in simulation.
Systematic solitary waves from their linear limits in two-component Bose–Einstein condensates with unequal dispersion coefficients

We systematically construct vector solitary waves in harmonically trapped one-dimensional two-component Bose–Einstein condensates with unequal dispersion coefficients by a numerical continuation in chemical potentials from the respective analytic low-density linear limits to the high-density nonlinear Thomas-Fermi regime. The main feature of the linear states herein is that the component with the larger quantum number has instead a smaller linear eigenenergy, enabled by suitable unequal dispersion coefficients, leading to new series of solutions compared with the states similarly obtained in the equal dispersion setting. Particularly, the lowest-lying series gives the well-known dark-anti-dark waves, and the second series yields the dark-multi-dark states, and the following series become progressively more complex in their wave structures. The Bogoliubov-de Gennes spectra analysis shows that most of these states bear unstable modes, but they can be long-lived and remarkably all of them can be fully stabilized in suitable parameter regimes.
DrumGAN VST: A Plugin for Drum Sound Analysis/Synthesis With Autoencoding Generative Adversarial Networks

In contemporary popular music production, drum sound design is commonly performed by cumbersome browsing and processing of pre-recorded samples in sound libraries. One can also use specialized synthesis hardware, typically controlled through low-level, musically meaningless parameters. Today, the field of Deep Learning offers methods to control the synthesis process via learned high-level features and allows generating a wide variety of sounds. In this paper, we present DrumGAN VST, a plugin for synthesizing drum sounds using a Generative Adversarial Network. DrumGAN VST operates on 44.1 kHz sample-rate audio, offers independent and continuous instrument class controls, and features an encoding neural network that maps sounds into the GAN's latent space, enabling resynthesis and manipulation of pre-existing drum sounds. We provide numerous sound examples and a demo of the proposed VST plugin.