Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting and Correcting IMU Movements During Joint Angle Estimation

Inertial measurement units (IMUs) increasingly function as a basic component of a wearable sensor network (WSN). The IMU-based joint angle estimation (JAE) is a typical usage of IMUs for WSN, with extensive applications. However, the issue that IMUs move with respect to their original placement during JAE is still a research gap and limits the robustness of deploying the technique in real-world application scenarios. In this study, we propose to detect and correct the IMU movement online in a computationally lightweight manner. In particular, we first experimentally investigate the influence of IMU movements. Second, we design the metrics for detecting IMU movements by mathematically formulating how the IMU movement affects the IMU’s measurements. Third, we determine the optimal thresholds of metrics by synthetic IMU data from a significantly amended simulation model. Finally, a correction method is proposed to correct the effects of IMU movements. We demonstrate our method on both synthetic data and real-user data. Our proposed method achieves a detection rate of 94.8%, a misdetection rate of 9.8%, and the calculation time of 2.15 ms for detecting IMU movements and can restore the accuracy of JAE. All of the results significantly outperform the intuitive method. The results demonstrate our method is a promising solution to detect and correct IMU movements during JAE. This study will aid the deployment of IMU-based JAE in real-world applications.
Stabilizing topological superfluidity of lattice fermions

Attractive interaction between spinless fermions in a two-dimensional lattice drives the formation of a topological superfluid. But the topological phase is dynamically unstable towards phase separation when the system has a high density of states and large interaction strength. This limits the critical temperature to an experimentally challenging regime where, for example, even ultracold atoms and molecules in optical lattices would struggle to realize the topological superfluid. We propose that the introduction of a weaker longer-range repulsion, in addition to the short-range attraction between lattice fermions, will suppress the phase separation instability. Taking the honeycomb lattice as an example, we show that our proposal significantly enlarges the stable portion of the topological superfluid phase and increases the critical temperature by an order of magnitude. Our work opens a route to enhance the stability of topological superfluids by engineering inter-particle interactions.
Scale Free Adversarial Multi Armed Bandits

We consider the Scale-Free Adversarial Multi Armed Bandits(MAB) problem. At the beginning of the game, the player only knows the number of arms $n$. It does not know the scale and magnitude of the losses chosen by the adversary or the number of rounds $T$. In each round, it sees bandit feedback about the loss vectors $l_1,\dots, l_T \in \mathbb{R}^n$. The goal is to bound its regret as a function of $n$ and norms of $l_1,\dots, l_T$. We design a bandit Follow The Regularized Leader (FTRL) algorithm, that uses an adaptive learning rate and give two different regret bounds, based on the exploration parameter used. With non-adaptive exploration, our algorithm has a regret of $\tilde{\mathcal{O}}(\sqrt{nL_2} + L_\infty\sqrt{nT})$ and with adaptive exploration, it has a regret of $\tilde{\mathcal{O}}(\sqrt{nL_2} + L_\infty\sqrt{nL_1})$. Here $L_\infty = \sup_t \| l_t\|_\infty$, $L_2 = \sum_{t=1}^T \|l_t\|_2^2$, $L_1 = \sum_{t=1}^T \|l_t\|_1$ and the $\tilde{\mathcal{O}}$ notation suppress logarithmic factors. These are the first MAB bounds that adapt to the $\|\cdot\|_2$, $\|\cdot\|_1$ norms of the losses. The second bound is the first data-dependent scale-free MAB bound as $T$ does not directly appear in the regret. We also develop a new technique for obtaining a rich class of local-norm lower-bounds for Bregman Divergences. This technique plays a crucial role in our analysis for controlling the regret when using importance weighted estimators of unbounded losses. This technique could be of independent interest.
SpeechBrain: A General-Purpose Speech Toolkit

SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to facilitate the research and development of neural speech processing technologies by being simple, flexible, user-friendly, and well-documented. This paper describes the core architecture designed to support several tasks of common interest, allowing users to naturally conceive, compare and share novel speech processing pipelines. SpeechBrain achieves competitive or state-of-the-art performance in a wide range of speech benchmarks. It also provides training recipes, pretrained models, and inference scripts for popular speech datasets, as well as tutorials which allow anyone with basic Python proficiency to familiarize themselves with speech technologies.
Optimising Hearing Aid Fittings for Speech in Noise with a Differentiable Hearing Loss Model

Current hearing aids normally provide amplification based on a general prescriptive fitting, and the benefits provided by the hearing aids vary among different listening environments despite the inclusion of noise suppression feature. Motivated by this fact, this paper proposes a data-driven machine learning technique to develop hearing aid fittings that are customised to speech in different noisy environments. A differentiable hearing loss model is proposed and used to optimise fittings with back-propagation. The customisation is reflected on the data of speech in different noise with also the consideration of noise suppression. The objective evaluation shows the advantages of optimised custom fittings over general prescriptive fittings.
Limit Cycles from Planar Piecewise Linear Hamiltonian differential Systems with Two or Three Zones

In this paper, we study the existence of limit cycles in continuous and discontinuous planar piecewise linear Hamiltonian differential systems with two or three zones separated by straight lines and such that the linear systems that define the piecewise one have isolated singular points, i.e. centers or saddles. In this case, we show that if the planar piecewise linear Hamiltonian differential system is either continuous or discontinuous with two zones, then it has no limit cycles. Now, if the planar piecewise linear Hamiltonian differential system is discontinuous with three zones, then it has at most one limit cycle, and there are examples with one limit cycle. More precisely, without taking into account the position of the singular points in the zones, we present examples with the unique limit cycle for all possible combinations of saddles and centers.
Stability of topological purity under random local unitaries

In this work, we provide an analytical proof of the robustness of a form of topological entanglement under a model of random local perturbations. We define the notion of topological purity and show that, in the context of quantum double models, this quantity does detect topological order and is robust under the action of a random shallow quantum circuit.
LB4OMP: A Dynamic Load Balancing Library for Multithreaded Applications

Exascale computing systems will exhibit high degrees of hierarchical parallelism, with thousands of computing nodes and hundreds of cores per node. Efficiently exploiting hierarchical parallelism is challenging due to load imbalance that arises at multiple levels. OpenMP is the most widely-used standard for expressing and exploiting the ever-increasing node-level parallelism. The scheduling options in OpenMP are insufficient to address the load imbalance that arises during the execution of multithreaded applications. The limited scheduling options in OpenMP hinder research on novel scheduling techniques which require comparison with others from the literature. This work introduces LB4OMP, an open-source dynamic load balancing library that implements successful scheduling algorithms from the literature. LB4OMP is a research infrastructure designed to spur and support present and future scheduling research, for the benefit of multithreaded applications performance. Through an extensive performance analysis campaign, we assess the effectiveness and demystify the performance of all loop scheduling techniques in the library. We show that, for numerous applications-systems pairs, the scheduling techniques in LB4OMP outperform the scheduling options in OpenMP. Node-level load balancing using LB4OMP leads to reduced cross-node load imbalance and to improved MPI+OpenMP applications performance, which is critical for Exascale computing.
Independent mechanism analysis, a new concept?

Independent component analysis provides a principled framework for unsupervised representation learning, with solid theory on the identifiability of the latent code that generated the data, given only observations of mixtures thereof. Unfortunately, when the mixing is nonlinear, the model is provably nonidentifiable, since statistical independence alone does not sufficiently constrain the problem. Identifiability can be recovered in settings where additional, typically observed variables are included in the generative process. We investigate an alternative path and consider instead including assumptions reflecting the principle of independent causal mechanisms exploited in the field of causality. Specifically, our approach is motivated by thinking of each source as independently influencing the mixing process. This gives rise to a framework which we term independent mechanism analysis. We provide theoretical and empirical evidence that our approach circumvents a number of nonidentifiability issues arising in nonlinear blind source separation.
Unipotent nearby cycles and the cohomology of shtukas

We give cases in which nearby cycles commute with pushforward from sheaves on the moduli stack of shtukas to a product of curves over a finite field. The proof systematically uses the property that taking nearby cycles of Satake sheaves on the Beilinson–Drinfeld Grassmannian with parahoric reduction is a central functor together with a ‘Zorro's lemma’ argument similar to that of Xue [Smoothness of cohomology sheaves of stacks of shtukas, Preprint (2020), arXiv:2012.12833]. As an application, for automorphic forms at the parahoric level, we characterize the image of tame inertia under the Langlands correspondence in terms of two-sided cells.
Scientometric engineering: Revealing spatiotemporal citation dynamics via open eprints

With the ever-increasing speed and volume of knowledge production and consumption, scholarly communication systems have been rapidly transformed into digitised and networked open ecosystems, where preprint servers have played a pivotal role. However, evidence is scarce regarding how this paradigm shift has affected the dynamics of collective attention on scientific knowledge. Herein, we address this issue by investigating the citation dynamics of more than 1.5 million eprints on arXiv, the most prominent and oldest eprint archive. The discipline-average citation history curves are estimated by applying a nonlinear regression model to the long-term citation data. The revealed spatiotemporal characteristics, including the growth and obsolescence patterns, are shown to vary across disciplines, reflecting the different publication and citation practices. The results are used to develop a spatiotemporally normalised citation index, called the γ-index, with an approximately normal distribution. It can be used to compare the citational impact of individual papers across disciplines and time periods, providing a less biased measure of research impact than those widely used in the literature and in practice. Further, a stochastic model for the observed spatiotemporal citation dynamics is derived, reproducing both the Lognormal Law for the cumulative citation distribution and the time trajectory of average citations in a unified formalism.
It Takes Two to Tango: Mixup for Deep Metric Learning

Metric learning involves learning a discriminative representation such that embeddings of similar classes are encouraged to be close, while embeddings of dissimilar classes are pushed far apart. State-of-the-art methods focus mostly on sophisticated loss functions or mining strategies. On the one hand, metric learning losses consider two or more examples at a time. On the other hand, modern data augmentation methods for classification consider two or more examples at a time. The combination of the two ideas is under-studied. In this work, we aim to bridge this gap and improve representations using mixup, which is a powerful data augmentation approach interpolating two or more examples and corresponding target labels at a time. This task is challenging because unlike classification, the loss functions used in metric learning are not additive over examples, so the idea of interpolating target labels is not straightforward. To the best of our knowledge, we are the first to investigate mixing both examples and target labels for deep metric learning. We develop a generalized formulation that encompasses existing metric learning loss functions and modify it to accommodate for mixup, introducing Metric Mix, or Metrix. We also introduce a new metric - utilization, to demonstrate that by mixing examples during training, we are exploring areas of the embedding space beyond the training classes, thereby improving representations. To validate the effect of improved representations, we show that mixing inputs, intermediate representations or embeddings along with target labels significantly outperforms state-of-the-art metric learning methods on four benchmark deep metric learning datasets.
Fragmented and Valuable: Following Sentiment Changes in Food Tweets

We analysed sentiment and frequencies related to smell, taste and temperature expressed by food tweets in the Latvian language. To get a better understanding of the role of smell, taste and temperature in the mental map of food associations, we looked at such categories as 'tasty' and 'healthy', which turned out to be mutually exclusive. By analysing the occurrence frequency of words associated with these categories, we discovered that food discourse overall was permeated by `tasty' while the category of 'healthy' was relatively small. Finally, we used the analysis of temporal dynamics to see if we can trace seasonality or other temporal aspects in smell, taste and temperature as reflected in food tweets. Understanding the composition of social media content with relation to smell, taste and temperature in food tweets allows us to develop our work further - on food culture/seasonality and its relation to temperature, on our limited capacity to express smell-related sentiments, and the lack of the paradigm of taste in discussing food healthiness.
Dynamic Software Updates for Unmodified Browsers through Multi-Version Execution

In this paper, we present the design, implementation, and evaluation of SINATRA, which supports instantaneous browser updates that do not result in any data loss through a novel Multi-Version eXecution (MVX) approach for JavaScript programs. SINATRA works in pure JavaScript, does not require any browser support, thus works on closed-source browsers, and requires trivial changes to each target page, that can be automated. First, SINATRA captures all the non-determinism available to a JavaScript program (e.g., event handlers executed, expired timers, invocations of Math.random). Our evaluation shows that SINATRA requires 5MB to store such events, and the memory grows at a modest rate of 23.1KB/s as the user keeps interacting with each page. When an update becomes available, SINATRA transfer the state by re-executing the same set of non-deterministic events on the new browser. During this time, which can be as long as 13 seconds, SINATRA uses MVX to allow the user to keep interacting with the old browser. Finally, SINATRA changes the roles in 353ms, and the user starts interacting with the new browser, effectively performing a browser update with zero downtime and no loss of state.
An Approximate Analytical Solution to Knudsen Layers

Abstract. We apply moment methods to obtaining an approximate analytical solution to Knudsen layers. Based on the hyperbolic regularized moment system for the Boltzmann equation with the Shakhov collision model, we derive a linearized hyperbolic moment system to model the scenario with the Knudsen layer vicinity to a solid wall with Maxwell boundary condition. We find that the reduced system is in an even-odd parity form that the reduced system proves to be well-posed under all accommodation coefficients. We show that the system may capture the temperature jump coefficient and the thermal Knudsen layer well with only a few moments. With the increasing number of moments used, qualitative convergence of the approximate solution is observed.
Curriculum Design for Teaching via Demonstrations: Theory and Applications

We consider the problem of teaching via demonstrations in sequential decision-making settings. In particular, we study how to design a personalized curriculum over demonstrations to speed up the learner's convergence. We provide a unified curriculum strategy for two popular learner models: Maximum Causal Entropy Inverse Reinforcement Learning (MaxEnt-IRL) and Cross-Entropy Behavioral Cloning (CrossEnt-BC). Our unified strategy induces a ranking over demonstrations based on a notion of difficulty scores computed w.r. t. the teacher's optimal policy and the learner's current policy. Compared to the state of the art, our strategy doesn't require access to the learner's internal dynamics and still enjoys similar convergence guarantees under mild technical conditions. Furthermore, we adapt our curriculum strategy to the setting where no teacher agent is present using task-specific difficulty scores. Experiments on a synthetic car driving environment and navigation-based environments demonstrate the effectiveness of our curriculum strategy.
Instantaneous Grammatical Error Correction with Shallow Aggressive Decoding

In this paper, we propose Shallow Aggressive Decoding (SAD) to improve the online inference efficiency of the Transformer for instantaneous Grammatical Error Correction (GEC). SAD optimizes the online inference efficiency for GEC by two innovations: 1) it aggressively decodes as many tokens as possible in parallel instead of always decoding only one token in each step to improve computational parallelism; 2) it uses a shallow decoder instead of the conventional Transformer architecture with balanced encoder-decoder depth to reduce the computational cost during inference. Experiments in both English and Chinese GEC benchmarks show that aggressive decoding could yield identical predictions to greedy decoding but with significant speedup for online inference. Its combination with the shallow decoder could offer an even higher online inference speedup over the powerful Transformer baseline without quality loss. Not only does our approach allow a single model to achieve the state-of-the-art results in English GEC benchmarks: 66.4 F0.5 in the CoNLL-14 and 72.9 F0.5 in the BEA-19 test set with an almost 10x online inference speedup over the Transformer-big model, but also it is easily adapted to other languages. Our code is available at https://github.com/AutoTemp/Shallow-Aggressive-Decoding.
Generative Models as a Data Source for Multiview Representation Learning

Generative models are now capable of producing highly realistic images that look nearly indistinguishable from the data on which they are trained. This raises the question: if we have good enough generative models, do we still need datasets? We investigate this question in the setting of learning general-purpose visual representations from a black-box generative model rather than directly from data. Given an off-the-shelf image generator without any access to its training data, we train representations from the samples output by this generator. We compare several representation learning methods that can be applied to this setting, using the latent space of the generator to generate multiple"views"of the same semantic content. We show that for contrastive methods, this multiview data can naturally be used to identify positive pairs (nearby in latent space) and negative pairs (far apart in latent space). We find that the resulting representations rival or even outperform those learned directly from real data, but that good performance requires care in the sampling strategy applied and the training method. Generative models can be viewed as a compressed and organized copy of a dataset, and we envision a future where more and more"model zoos"proliferate while datasets become increasingly unwieldy, missing, or private. This paper suggests several techniques for dealing with visual representation learning in such a future. Code is available on our project page https://ali-design.github. io/GenRep/.
Quantitative near‐surface remote sensing of wastewater quality in oxidation ponds and reservoirs: a case study of the Naan system

This study develops a remote sensing technique for real‐time monitoring of wastewater effluents stored before irrigation. Reflectance and attenuation coefficients for downwelling irradiance spectra (400 to 950 nm spectral resolution of 2 nm) were acquired simultaneously with turbidity, chlorophyll, organic matter, and total suspended matter concentrations in the Naan wastewater system, Israel. High‐spectral‐resolution spectrometric data were used to select the most suitable spectral bands for remote sensing of “optically active” constituent concentrations. Algorithms for remote estimation of wastewater quality expressed as chlorophyll‐a, bacteriochlorophyll‐a, and total suspended matter concentrations were developed. Reflectance height at 720 nm and an area above the base line from 670 to 950 nm was used in algorithms for chlorophyll‐a assessment (r2 > 0.83). For bacteriochlorophyll‐a assessment, an area under the base line through 780 to 900 nm was used, allowing its quantitative estimation (r2 > 0.94). Reflectance of approximately 570 and from 400 to 950 nm correlated closely with total suspended matter concentration (r2 > 0.82) and was used to retrieve it from reflectance spectra. This is the first reported study on the quantitative remote assessment of wastewater quality. The results suggest that four spectral bands be used for real‐time monitoring of wastewater ponds quality.
A mean‐field game approach to equilibrium pricing in solar renewable energy certificate markets

Solar renewable energy certificate (SREC) markets are a market‐based system that incentivizes solar energy generation. A regulatory body overseeing load serving entities imposes a lower bound on the amount of energy each regulated firm must generate via solar means, providing them with a tradeable certificate for each MWh generated. Firms seek to navigate the market optimally by modulating their SREC generation and trading rates. As such, the SREC market can be viewed as a stochastic game, where agents interact through the SREC price. We study this stochastic game by solving the mean‐field game (MFG) limit with subpopulations of heterogeneous agents. Market participants optimize costs accounting for trading frictions, cost of generation, nonlinear noncompliance costs, and generation uncertainty. Moreover, we endogenize SREC price through market clearing. We characterize firms' optimal controls as the solution of McKean–Vlasov (MV) forward‐backward stochastic differential equations (FBSDEs) and determine the equilibrium SREC price. We establish the existence and uniqueness of a solution to this MV‐FBSDE, and prove that the MFG strategies form an ε$\epsilon$ ‐Nash equilibrium for the finite player game. Finally, we develop a numerical scheme for solving the MV‐FBSDEs and conduct a simulation study.