Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech intelligibility of simulated hearing loss sounds and its prediction using the Gammachirp Envelope Similarity Index (GESI)

In the present study, speech intelligibility (SI) experiments were performed using simulated hearing loss (HL) sounds in laboratory and remote environments to clarify the effects of peripheral dysfunction. Noisy speech sounds were processed to simulate the average HL of 70- and 80-year-olds using Wadai Hearing Impairment Simulator (WHIS). These sounds were presented to normal hearing (NH) listeners whose cognitive function could be assumed to be normal. The results showed that the divergence was larger in the remote experiments than in the laboratory ones. However, the remote results could be equalized to the laboratory ones, mostly through data screening using the results of tone pip tests prepared on the experimental web page. In addition, a newly proposed objective intelligibility measure (OIM) called the Gammachirp Envelope Similarity Index (GESI) explained the psychometric functions in the laboratory and remote experiments fairly well. GESI has the potential to explain the SI of HI listeners by properly setting HL parameters.
Inter-team coordination in large-scale agile software development

Agile approaches for software development have increased in popularity since the formulation of the Agile Manifesto in 2001. The methods were initially developed for small teams to be able to respond quickly to change and to be flexible in the development process. Many have also seen the benefits of close customer collaboration and having self-empowered teams. In the latest years, agile practices have also caught interest in larger organisations, to whether it can be applied to large-scale software development projects as well. However, the theoretical grounding for agile in large-scale is still limited. As projects increase in size, there will be more teams and team members, which lead to increased complexity that urges the need for more coordination than in smaller agile projects. Inter-team coordination has thereby been identified as an important research topic within large-scale agile software development. In this master thesis, inter-team coordination has been studied through an exploratory case study, on a very-large scale agile software development project, which was conducted in Norway. The data from this case were analysed with the Van de Ven model of coordination. The primary results of this master thesis support many principles of the Van de Ven model of coordination. There are influential factors to coordination that demand different coordination mechanisms, and it was experienced in the analysed case, that these mechanisms and arenas for coordination changed over time. From more standardised, plan-based and formal coordination in the beginning, to be more informal and horizontal as the project progressed. The informal communication was seen essential to ease the inter-team coordination, by being able to make clarifications at lower levels. This was first possible when relations and trust, across the teams, were built. Another interesting finding was that large-scale projects need, to some extent, more standardisation and structure than smaller agile projects. However, the difficulty is to find the right balance, so the agility is not lost.
Power laws and symmetries in a minimal model of financial market economy

A financial market is a system resulting from the complex interaction between participants in a closed economy. We propose a minimal microscopic model of the financial market economy based on the real economy's symmetry constraint and minimality requirement. We solve the proposed model analytically in the mean-field regime, which shows that various kinds of universal power-law-like behaviors in the financial market may depend on one another, just like the critical exponents in physics. We then discuss the parameters in the proposed model, and we show that each parameter in our model can be related to measurable quantities in the real market, which enables us to discuss the cause of a few kinds of social and economic phenomena.
A Relative Church-Turing-Deutsch Thesis from Special Relativity and Undecidability

Beginning with Turing’s seminal work in 1950, artiﬁcial intelligence pro-poses that consciousness can be simulated by a Turing machine. This implies a potential theory of everything where the universe is a simulation on a computer, which begs the question of whether we can prove we exist in a simulation. In this work, we construct a relative model of computation where a computable local machine is simulated by a global , classical Turing machine. We show that the problem of the local machine computing simulation properties of its global simulator is undecidable in the same sense as the Halting problem. Then, we show that computing the time, space, or error accumulated by the global simulator are simulation properties and therefore are undecidable. These simulation properties give rise to special relativistic eﬀects in the relative model which we use to construct a relative Church-Turing-Deutsch thesis where a global, classical Turing machine computes quantum mechanics for a local machine with the same constant-time local computational complexity as experienced in our universe.
A Multi-Purpose Realistic Haze Benchmark With Quantifiable Haze Levels and Ground Truth

Imagery collected from outdoor visual environments is often degraded due to the presence of dense smoke or haze. A key challenge for research in scene understanding in these degraded visual environments (DVE) is the lack of representative benchmark datasets. These datasets are required to evaluate state-of-the-art object recognition and other computer vision algorithms in degraded settings. In this paper, we address some of these limitations by introducing the first realistic haze image benchmark, from both aerial and ground view, with paired haze-free images, and in-situ haze density measurements. This dataset was produced in a controlled environment with professional smoke generating machines that covered the entire scene, and consists of images captured from the perspective of both an unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV). We also evaluate a set of representative state-of-the-art dehazing approaches as well as object detectors on the dataset. The full dataset presented in this paper, including the ground truth object classification bounding boxes and haze density measurements, is provided for the community to evaluate their algorithms at: https://a2i2-archangel.vision. A subset of this dataset has been used for the “Object Detection in Haze” Track of CVPR UG2 2022 challenge at https://cvpr2022.ug2challenge. org/track1.html.
Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Images

Developing an AI-assisted gland segmentation method from histology images is critical for automatic cancer diagnosis and progno-sis; however, the high cost of pixel-level annotations hinders its appli-cations to broader diseases. Existing weakly-supervised semantic segmentation methods in computer vision achieve degenerative results for gland segmentation, since the characteristics and problems of glandular datasets are diﬀerent from general object datasets. We observe that, unlike natural images, the key problem with histology images is the confusion of classes owning to morphological homogeneity and low color contrast among diﬀerent tissues. To this end, we propose a novel method Online Easy Example Mining (OEEM) that encourages the network to focus on credible supervision signals rather than noisy signals, there-fore mitigating the inﬂuence of inevitable false predictions in pseudo-masks. According to the characteristics of glandular datasets, we design a strong framework for gland segmentation. Our results exceed many fully-supervised methods and weakly-supervised methods for gland segmentation over 4.4% and 6.04% at mIoU, respectively. Code is available at https://github.com/xmed-lab/OEEM.
Density Estimation with Autoregressive Bayesian Predictives

Bayesian methods are a popular choice for statistical inference in small-data regimes due to the regularization eﬀect induced by the prior, which serves to counteract overﬁtting. In the context of density estimation, the standard Bayesian approach is to target the posterior predictive. In general, direct estimation of the posterior predictive is intractable and so methods typically resort to approximating the posterior distribution as an intermediate step. The recent development of recursive predictive copula updates, however, has made it possible to perform tractable predictive density estimation without the need for posterior approximation. Although these estimators are computationally appealing, they tend to struggle on non-smooth data distributions. This is largely due to the comparatively restrictive form of the likelihood models from which the proposed copula updates were derived. To address this shortcoming, we consider a Bayesian nonparametric model with an autoregressive likelihood decomposition and Gaussian process prior, which yields a data-dependent bandwidth parameter in the copula update. Further, we formulate a novel parameterization of the bandwidth using an autoregressive neural network that maps the data into a latent space, and is thus able to capture more complex dependencies in the data. Our extensions increase the modelling capacity of existing recursive Bayesian density estimators, achieving state-of-the-art results on tabular data sets.
Supernova Dust Evolution Probed by Deep-sea 60Fe Time History

There is a wealth of data on live, undecayed 60Fe (t 1/2 = 2.6 Myr) in deep-sea deposits, the lunar regolith, cosmic rays, and Antarctic snow, which is interpreted as originating from the recent explosions of at least two near-Earth supernovae. We use the 60Fe profiles in deep-sea sediments to estimate the timescale of supernova debris deposition beginning ∼3 Myr ago. The available data admits a variety of different profile functions, but in all cases the best-fit 60Fe pulse durations are >1.6 Myr when all the data is combined. This timescale far exceeds the ≲0.1 Myr pulse that would be expected if 60Fe was entrained in the supernova blast wave plasma. We interpret the long signal duration as evidence that 60Fe arrives in the form of supernova dust, whose dynamics are separate from but coupled to the evolution of the blast plasma. In this framework, the >1.6 Myr is that for dust stopping due to drag forces. This scenario is consistent with the simulations in Fry et al. (2020), where the dust is magnetically trapped in supernova remnants and thereby confined around regions of the remnant dominated by supernova ejects, where magnetic fields are low. This picture fits naturally with models of cosmic-ray injection of refractory elements as sputtered supernova dust grains and implies that the recent 60Fe detections in cosmic rays complement the fragments of grains that survived to arrive on the Earth and Moon. Finally, we present possible tests for this scenario.
Near-Optimal Randomized Exploration for Tabular Markov Decision Processes

We study algorithms using randomized value functions for exploration in reinforcement learning. This type of algorithms enjoys appealing empirical performance. We show that when we use 1) a single random seed in each episode, and 2) a Bernstein-type magnitude of noise, we obtain a worst-case $\widetilde{O}\left(H\sqrt{SAT}\right)$ regret bound for episodic time-inhomogeneous Markov Decision Process where $S$ is the size of state space, $A$ is the size of action space, $H$ is the planning horizon and $T$ is the number of interactions. This bound polynomially improves all existing bounds for algorithms based on randomized value functions, and for the first time, matches the $\Omega\left(H\sqrt{SAT}\right)$ lower bound up to logarithmic factors. Our result highlights that randomized exploration can be near-optimal, which was previously achieved only by optimistic algorithms. To achieve the desired result, we develop 1) a new clipping operation to ensure both the probability of being optimistic and the probability of being pessimistic are lower bounded by a constant, and 2) a new recursive formula for the absolute value of estimation errors to analyze the regret.
Provably Sample-Efficient Model-Free Algorithm for MDPs with Peak Constraints

In the optimization of dynamic systems, the variables typically have constraints. Such problems can be modeled as a Constrained Markov Decision Process (CMDP). This paper considers the peak Constrained Markov Decision Process (PCMDP), where the agent chooses the policy to maximize total reward in the finite horizon as well as satisfy constraints at each epoch with probability 1. We propose a model-free algorithm that converts PCMDP problem to an unconstrained problem and a Q-learning based approach is applied. We define the concept of probably approximately correct (PAC) to the proposed PCMDP problem. The proposed algorithm is proved to achieve an $(\epsilon,p)$-PAC policy when the episode $K\geq\Omega(\frac{I^2H^6SA\ell}{\epsilon^2})$, where $S$ and $A$ are the number of states and actions, respectively. $H$ is the number of epochs per episode. $I$ is the number of constraint functions, and $\ell=\log(\frac{SAT}{p})$. We note that this is the first result on PAC kind of analysis for PCMDP with peak constraints, where the transition dynamics are not known apriori. We demonstrate the proposed algorithm on an energy harvesting problem and a single machine scheduling problem, where it performs close to the theoretical upper bound of the studied optimization problem.
Energy Flows: Towards Determinant-Free Training of Normalizing Flows

Normalizing ﬂows are a popular approach for constructing probabilistic and generative models. However, maximum likelihood training of ﬂows is challenging due to the need to calculate computationally expensive determinants of Jacobians. This paper takes steps towards addressing this challenge by introducing an approach for determinant-free training of ﬂows inspired by two-sample testing. Central to our framework is the energy objective, a multidimensional extension of proper scoring rules that admits efﬁcient estimators based on random projections and that outperforms a range of alternative two-sample objectives that can be derived in our framework. Crucially, the energy objective and its alternatives do not require calculating determinants and therefore support general ﬂow architectures that are not well-suited to maximum likelihood training (e.g., densely connected networks). We empirically demonstrate that energy ﬂows achieve competitive generative modeling performance while maintaining fast generation and posterior inference.
Provably Efficient Offline Reinforcement Learning with Trajectory-Wise Reward

The remarkable success of reinforcement learning (RL) heavily relies on observing the reward of every visited state-action pair. In many real world applications, however, an agent can observe only a score that represents the quality of the whole trajectory, which is referred to as the {\em trajectory-wise reward}. In such a situation, it is difficult for standard RL methods to well utilize trajectory-wise reward, and large bias and variance errors can be incurred in policy evaluation. In this work, we propose a novel offline RL algorithm, called Pessimistic vAlue iteRaTion with rEward Decomposition (PARTED), which decomposes the trajectory return into per-step proxy rewards via least-squares-based reward redistribution, and then performs pessimistic value iteration based on the learned proxy reward. To ensure the value functions constructed by PARTED are always pessimistic with respect to the optimal ones, we design a new penalty term to offset the uncertainty of the proxy reward. For general episodic MDPs with large state space, we show that PARTED with overparameterized neural network function approximation achieves an $\tilde{\mathcal{O}}(D_{\text{eff}}H^2/\sqrt{N})$ suboptimality, where $H$ is the length of episode, $N$ is the total number of samples, and $D_{\text{eff}}$ is the effective dimension of the neural tangent kernel matrix. To further illustrate the result, we show that PARTED achieves an $\tilde{\mathcal{O}}(dH^3/\sqrt{N})$ suboptimality with linear MDPs, where $d$ is the feature dimension, which matches with that with neural network function approximation, when $D_{\text{eff}}=dH$. To the best of our knowledge, PARTED is the first offline RL algorithm that is provably efficient in general MDP with trajectory-wise reward.
Attainable forms of Assouad spectra

Let $d\in\mathbb{N}$ and let $\varphi\colon(0,1)\to[0,d]$. We prove that there exists a set $F\subset\mathbb{R}^d$ such that $\operatorname{dim}_A^\theta F=\varphi(\theta)$ for all $\theta\in(0,1)$ if and only if for every $0<\lambda<\theta<1$, \[0\leq (1-\lambda)\varphi(\lambda)-(1-\theta)\varphi(\theta)\leq (\theta-\lambda)\varphi\Bigl(\frac{\lambda}{\theta}\Bigr). \] In particular, the following behaviours which have not previously been witnessed in any examples are possible: the Assouad spectrum can be non-monotonic on every open set, and can fail to be H\"older in a neighbourhood of 1.
On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice

We study two of the most popular performance metrics in medical image segmentation, Accuracy and Dice, when the target labels are noisy. For both metrics, several statements related to characterization and volume properties of the set of optimal segmentations are proved, and associated experiments are provided. Our main insights are: (i) the volume of the solutions to both metrics may deviate significantly from the expected volume of the target, (ii) the volume of a solution to Accuracy is always less than or equal to the volume of a solution to Dice and (iii) the optimal solutions to both of these metrics coincide when the set of feasible segmentations is constrained to the set of segmentations with the volume equal to the expected volume of the target.
SBERT studies Meaning Representations: Decomposing Sentence Embeddings into Explainable AMR Meaning Features

Metrics for graph-based meaning representations (e.g., Abstract Meaning Representation, AMR) can help us uncover key semantic aspects in which two sentences are similar to each other. However, such metrics tend to be slow, rely on parsers, and do not reach state-of-the-art performance when rating sentence similarity. On the other hand, models based on large-pretrained language models, such as S(entence)BERT, show high correlation to human similarity ratings, but lack interpretability. In this paper, we aim at the best of these two worlds, by creating similarity metrics that are highly effective, while also providing an interpretable rationale for their rating. Our approach works in two steps: We ﬁrst select AMR graph metrics that measure meaning similarity of sentences with respect to key semantic facets, such as, i.a. , semantic roles, negation, or quantiﬁcation. Second, we employ these metrics to induce Semantically Structured Sentence BERT embeddings (S 3 BERT), which are composed of different meaning aspects captured in different sub-spaces. In our experimental studies, we show that our approach offers a valuable balance between performance and interpretability.
On Learning the Dynamical Response of Nonlinear Control Systems with Deep Operator Networks

We propose a Deep Operator Network (DeepONet) framework to learn the dynamic response of continuous-time nonlinear control systems from data. To this end, we ﬁrst construct and train a DeepONet that approximates the control system’s local solution operator. Then, we design a numerical scheme that recursively uses the trained DeepONet to simulate the control system’s long/medium-term dynamic response for given control inputs and initial conditions. We accompany the proposed scheme with an estimate for the error bound of the associated cumulative error. Furthermore, we design a data-driven Runge-Kutta (RK) explicit scheme that uses the DeepONet forward pass and automatic diﬀerentiation to better approximate the system’s response when the numerical scheme’s step size is suﬃciently small. Numerical experiments on the predator-prey, pendulum, and cart pole systems conﬁrm that our DeepONet framework learns to approximate the dynamic response of nonlinear control systems eﬀectively.
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation

For 3D medical image (e.g. CT and MRI) segmentation, the difficulty of segmenting each slice in a clinical case varies greatly. Previous research on volumetric medical image segmentation in a slice-by-slice manner conventionally use the identical 2D deep neural network to segment all the slices of the same case, ignoring the data heterogeneity among image slices. In this paper, we focus on multi-modal 3D MRI brain tumor segmentation and propose a dynamic architecture network named Med-DANet based on adaptive model selection to achieve effective accuracy and efficiency trade-off. For each slice of the input 3D MRI volume, our proposed method learns a slice-specific decision by the Decision Network to dynamically select a suitable model from the predefined Model Bank for the subsequent 2D segmentation task. Extensive experimental results on both BraTS 2019 and 2020 datasets show that our proposed method achieves comparable or better results than previous state-of-the-art methods for 3D MRI brain tumor segmentation with much less model complexity. Compared with the state-of-the-art 3D method TransBTS, the proposed framework improves the model efficiency by up to 3.5x without sacrificing the accuracy. Our code will be publicly available soon.
Exercícios para tratamento de lombalgia inespecífica

JUSTIFICATIVA Y OBJETIVOS: La lumbalgia es un sindrome frecuente y provoca incapacidad y perdida en el trabajo. A pesar de que existen diversas causas, el tipo de lumbalgia mas frecuente es la inespecifica. Los ejercicios son a menudo usados para el tratamiento de la lumbalgia pero existen controversias en cuanto a su eficacia. El objetivo de este trabajo, fue hacer una revision sobre los ejercicios para el tratamiento de la lumbalgia inespecifica. CONTENIDO: Fueron descritas la definicion y la clasificacion de las lumbalgias. La incidencia y los mecanismos de la lumbalgia inespecifica, y los ejercicios empleados para su tratamiento tambien se relataron. CONCLUSIONES: Los tipos de ejercicios terapeuticos para la lumbalgia cronica o aguda que son mas eficaces todavia son bastante controversiales, sin embargo, los ejercicios terapeuticos son tal vez, el tratamiento conservador mas usado en todo el mundo.