Miracle-dz/UniFAR-FTUs huggingface dataset:
Adapting Text-based Dialogue State Tracker for Spoken Dialogues

Although there have been remarkable advances in dialogue systems through the dialogue systems technology competition (DSTC), it remains one of the key challenges to building a robust task-oriented dialogue system with a speech interface. Most of the progress has been made for text-based dialogue systems since there are abundant datasets with written cor- pora while those with spoken dialogues are very scarce. However, as can be seen from voice assistant systems such as Siri and Alexa, it is of practical importance to transfer the success to spoken dialogues. In this paper, we describe our engineering effort in building a highly successful model that participated in the speech-aware dialogue systems technology challenge track in DSTC11. Our model consists of three major modules: (1) automatic speech recognition error correction to bridge the gap between the spoken and the text utterances, (2) text-based dialogue system (D3ST) for estimating the slots and values using slot descriptions, and (3) post-processing for recovering the error of the estimated slot value. Our experiments show that it is important to use an explicit automatic speech recognition error correction module, post-processing, and data augmentation to adapt a text-based dialogue state tracker for spoken dialogue corpora.
Optimal Economic Gas Turbine Dispatch with Deep Reinforcement Learning

Dispatching strategies for gas turbines (GTs) are changing in modern electricity grids. A growing incorporation of intermittent renewable energy requires GTs to operate more but shorter cycles and more frequently on partial loads. Deep reinforcement learning (DRL) has recently emerged as a tool that can cope with this development and dispatch GTs economically. The key advantages of DRL are a model-free optimization and the ability to handle uncertainties, such as those introduced by varying loads or renewable energy production. In this study, three popular DRL algorithms are implemented for an economic GT dispatch problem on a case study in Alberta, Canada. We highlight the benefits of DRL by incorporating an existing thermodynamic software provided by Siemens Energy into the environment model and by simulating uncertainty via varying electricity prices, loads, and ambient conditions. Among the tested algorithms and baseline methods, Deep Q-Networks (DQN) obtained the highest rewards while Proximal Policy Optimization (PPO) was the most sample efficient. We further propose and implement a method to assign GT operation and maintenance cost dynamically based on operating hours and cycles. Compared to existing methods, our approach better approximates the true cost of modern GT dispatch and hence leads to more realistic policies.
Conformal Meta-learners for Predictive Inference of Individual Treatment Effects

We investigate the problem of machine learning-based (ML) predictive inference on individual treatment effects (ITEs). Previous work has focused primarily on developing ML-based meta-learners that can provide point estimates of the conditional average treatment effect (CATE); these are model-agnostic approaches for combining intermediate nuisance estimates to produce estimates of CATE. In this paper, we develop conformal meta-learners, a general framework for issuing predictive intervals for ITEs by applying the standard conformal prediction (CP) procedure on top of CATE meta-learners. We focus on a broad class of meta-learners based on two-stage pseudo-outcome regression and develop a stochastic ordering framework to study their validity. We show that inference with conformal meta-learners is marginally valid if their (pseudo outcome) conformity scores stochastically dominate oracle conformity scores evaluated on the unobserved ITEs. Additionally, we prove that commonly used CATE meta-learners, such as the doubly-robust learner, satisfy a model- and distribution-free stochastic (or convex) dominance condition, making their conformal inferences valid for practically-relevant levels of target coverage. Whereas existing procedures conduct inference on nuisance parameters (i.e., potential outcomes) via weighted CP, conformal meta-learners enable direct inference on the target parameter (ITE). Numerical experiments show that conformal meta-learners provide valid intervals with competitive efficiency while retaining the favorable point estimation properties of CATE meta-learners.
Surveillance des sols dans l'environnement par télédétection et systèmes d'information géographiques = Monitoring soils in the environment with remote sensing and GIS

The experience of a large project ongoing in Tunisia is used to discuss the different aspects of desertification monitoring with the help of remote sensing. Emphasis is put on performing appropriate field measurements by surface sampling for satellite data calibration. Change detection is linked to simple surface parameters such as brightness, green vegetation cover and soil color. Soi1 surface optical properties are dominant in the signal received by the satellites and show strong potential for long term degradation detection. Long t h e series of Landsat MSS data can be used to detect these surface changes over the last 20 years. As a result of the real-size test done in Tunisia a global methodology for a satellite-based arid land monitoring strategy is proposed for further discussion and development.
RecRec: Algorithmic Recourse for Recommender Systems

Recommender systems play an essential role in the choices people make in domains such as entertainment, shopping, food, news, employment, and education. The machine learning models underlying these recommender systems are often enormously large and black-box in nature for users, content providers, and system developers alike. It is often crucial for all stakeholders to understand the model's rationale behind making certain predictions and recommendations. This is especially true for the content providers whose livelihoods depend on the recommender system. Drawing motivation from the practitioners' need, in this work, we propose a recourse framework for recommender systems, targeted towards the content providers. Algorithmic recourse in the recommendation setting is a set of actions that, if executed, would modify the recommendations (or ranking) of an item in the desired manner. A recourse suggests actions of the form: ''if a feature changes X to Y, then the ranking of that item for a set of users will change to X. '' Furthermore, we demonstrate that RecRec is highly effective in generating valid, sparse, and actionable recourses through an empirical evaluation of recommender systems trained on three real-world datasets. To the best of our knowledge, this work is the first to conceptualize and empirically test a generalized framework for generating recourses for recommender systems.
Equivalence Principle of the $P$-value and Mutual Information

In this paper, we propose a novel equivalence between probability theory and information theory. For a single random variable, Shannon's self-information, $I=-\log {p}$, is an alternative expression of a probability $p$. However, for two random variables, no information equivalent to the $p$-value has been identified. Here, we prove theorems that demonstrate that mutual information (MI) is equivalent to the $p$-value irrespective of prior information about the distribution of the variables. If the maximum entropy principle can be applied, our equivalence theorems allow us to readily compute the $p$-value from multidimensional MI. By contrast, in a contingency table of any size with known marginal frequencies, our theorem states that MI asymptotically coincides with the logarithm of the $p$-value of Fisher's exact test, divided by the sample size. Accordingly, the theorems enable us to perform a meta-analysis to accurately estimate MI with a low $p$-value, thereby calculating informational interdependence that is robust against sample size variation. Thus, our theorems demonstrate the equivalence of the $p$-value and MI at every dimension, use the merits of both, and provide fundamental information for integrating probability theory and information theory.
SMOClust: Synthetic Minority Oversampling based on Stream Clustering for Evolving Data Streams

Many real-world data stream applications not only suffer from concept drift but also class imbalance. Yet, very few existing studies investigated this joint challenge. Data difficulty factors, which have been shown to be key challenges in class imbalanced data streams, are not taken into account by existing approaches when learning class imbalanced data streams. In this work, we propose a drift adaptable oversampling strategy to synthesise minority class examples based on stream clustering. The motivation is that stream clustering methods continuously update themselves to reflect the characteristics of the current underlying concept, including data difficulty factors. This nature can potentially be used to compress past information without caching data in the memory explicitly. Based on the compressed information, synthetic examples can be created within the region that recently generated new minority class examples. Experiments with artificial and real-world data streams show that the proposed approach can handle concept drift involving different minority class decomposition better than existing approaches, especially when the data stream is severely class imbalanced and presenting high proportions of safe and borderline minority class examples.
An Advanced Tree Algorithm with Interference Cancellation in Uplink and Downlink

In this paper, we propose Advanced Tree-algorithm with Interference Cancellation (ATIC), a variant of binary tree-algorithm with successive interference cancellation (SICTA) introduced by Yu and Giannakis. ATIC assumes that Interference Cancellation (IC) can be performed both by the access point (AP), as in SICTA, but also by the users. Specifically, after every collision slot, the AP broadcasts the observed collision as feedback. Users who participated in the collision then attempt to perform IC by subtracting their transmissions from the collision signal. This way, the users can resolve collisions of degree 2 and, using a simple distributed arbitration algorithm based on user IDs, ensure that the next slot will contain just a single transmission. We show that ATIC reaches the asymptotic throughput of 0.924 as the number of initially collided users tends to infinity and reduces the number of collisions and packet delay. We also compare ATIC with other tree algorithms and indicate the extra feedback resources it requires.
Shape-Margin Knowledge Augmented Network for Thyroid Nodule Segmentation and Diagnosis

Thyroid nodule segmentation is a crucial step in the diagnostic procedure of physicians and computer-aided diagnosis systems. Mostly, current studies treat segmentation and diagnosis as independent tasks without considering the correlation between these tasks. The sequence steps of these independent tasks in computer-aided diagnosis systems may lead to the accumulation of errors. Therefore, it is worth combining them as a whole through exploring the relationship between thyroid nodule segmentation and diagnosis. According to the thyroid imaging reporting and data system (TI-RADS), the assessment of shape and margin characteristics is the prerequisite for the discrimination of benign and malignant thyroid nodules. These characteristics can be observed in the thyroid nodule segmentation masks. Inspired by the diagnostic procedure of TI-RADS, this paper proposes a shape-margin knowledge augmented network (SkaNet) for simultaneously thyroid nodule segmentation and diagnosis. Due to the similarity in visual features between segmentation and diagnosis, SkaNet shares visual features in the feature extraction stage and then utilizes a dual-branch architecture to perform thyroid nodule segmentation and diagnosis tasks simultaneously. To enhance effective discriminative features, an exponential mixture module is devised, which incorporates convolutional feature maps and self-attention maps by exponential weighting. Then, SkaNet is jointly optimized by a knowledge augmented multi-task loss function with a constraint penalty term. It embeds shape and margin characteristics through numerical computation and models the relationship between the thyroid nodule diagnosis results and segmentation masks.
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition

In real-world scenarios like traffic and energy, massive time-series data with missing values and noises are widely observed, even sampled irregularly. While many imputation methods have been proposed, most of them work with a local horizon, which means models are trained by splitting the long sequence into batches of fit-sized patches. This local horizon can make models ignore global trends or periodic patterns. More importantly, almost all methods assume the observations are sampled at regular time stamps, and fail to handle complex irregular sampled time series arising from different applications. Thirdly, most existing methods are learned in an offline manner. Thus, it is not suitable for many applications with fast-arriving streaming data. To overcome these limitations, we propose BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition. We treat the multivariate time series as the weighted combination of groups of low-rank temporal factors with different patterns. We apply a group of Gaussian Processes (GPs) with different kernels as functional priors to fit the factors. For computational efficiency, we further convert the GPs into a state-space prior by constructing an equivalent stochastic differential equation (SDE), and developing a scalable algorithm for online inference. The proposed method can not only handle imputation over arbitrary time stamps, but also offer uncertainty quantification and interpretability for the downstream application. We evaluate our method on both synthetic and real-world datasets.
Bose condensation of upper-branch exciton-polaritons in a transferrable microcavity

Exciton-polaritons are composite bosonic quasiparticles arising from the strong coupling of excitonic transitions and optical modes. Exciton-polaritons have triggered wide exploration in the past decades not only due to their rich quantum phenomena such as superfluidity, superconductivity and quantized vortices but also due to their potential applications for unconventional coherent light sources and all-optical control elements. Here, we report the observation of Bose-Einstein condensation of the upper polariton branch in a transferrable WS$_2$ monolayer microcavity. Near the condensation threshold, we observe a nonlinear increase in upper polariton intensity. This sharp increase in intensity is accompanied by a decrease of the linewidth and an increase of the upper polariton temporal coherence, all of which are hallmarks of Bose-Einstein condensation. By simulating the quantum Boltzmann equation, we show that the upper polariton condensation only occurs for a particular range of particle density. We can attribute the creation of Bose condensation of the upper polariton to the following requirements: 1) the upper polariton is more excitonic than the lower one; 2) there is relatively more pumping in the upper branch; and 3) the conversion time from the upper to the lower polariton branch is long compared to the lifetime of the upper polaritons.
GPS-aided Visual Wheel Odometry

This paper introduces a novel GPS-aided visual-wheel odometry (GPS-VWO) for ground robots. The state estimation algorithm tightly fuses visual, wheeled encoder and GPS measurements in the way of Multi-State Constraint Kalman Filter (MSCKF). To avoid accumulating calibration errors over time, the proposed algorithm calculates the extrinsic rotation parameter between the GPS global coordinate frame and the VWO reference frame online as part of the estimation process. The convergence of this extrinsic parameter is guaranteed by the observability analysis and verified by using real-world visual and wheel encoder measurements as well as simulated GPS measurements. Moreover, a novel theoretical finding is presented that the variance of unobservable state could converge to zero for specific Kalman filter system. We evaluate the proposed system extensively in large-scale urban driving scenarios. The results demonstrate that better accuracy than GPS is achieved through the fusion of GPS and VWO. The comparison between extrinsic parameter calibration and non-calibration shows significant improvement in localization accuracy thanks to the online calibration.
Optimal ratcheting of dividend payout under Brownian motion surplus

This paper is concerned with a long standing optimal dividend payout problem in insurance subject to the so-called ratcheting constraint, that is, the dividend payout rate shall be non-decreasing over time. The surplus process is modeled by a drifted Brownian motion process and the aim is to find the optimal dividend ratcheting strategy to maximize the expectation of the total discounted dividend payouts until the ruin time. Due to the path-dependent constraint, the standard control theory cannot be directly applied to tackle the problem. The related Hamilton-Jacobi-Bellman (HJB) equation is a new type of variational inequality. In the literature, it is only shown to have a viscosity solution, which is not strong enough to guarantee the existence of an optimal dividend ratcheting strategy. This paper proposes a novel partial differential equation method to study the HJB equation. We not only prove the the existence and uniqueness of the solution in some stronger functional space, but also prove the monotonicity, boundedness, and $C^{\infty}$-smoothness of the dividend ratcheting free boundary. Based on these results, we eventually derive an optimal dividend ratcheting strategy, and thus solve the open problem completely. Economically, we find that if the surplus volatility is above an explicit threshold, then one should pay dividends at the maximum rate, regardless the surplus level. Otherwise, by contrast, the optimal dividend ratcheting strategy relays on the surplus level and one should only ratchet up the dividend payout rate when the surplus level touches the dividend ratcheting free boundary.
The Relative Gaussian Mechanism and its Application to Private Gradient Descent

The Gaussian Mechanism (GM), which consists in adding Gaussian noise to a vector-valued query before releasing it, is a standard privacy protection mechanism. In particular, given that the query respects some L2 sensitivity property (the L2 distance between outputs on any two neighboring inputs is bounded), GM guarantees R\'enyi Differential Privacy (RDP). Unfortunately, precisely bounding the L2 sensitivity can be hard, thus leading to loose privacy bounds. In this work, we consider a Relative L2 sensitivity assumption, in which the bound on the distance between two query outputs may also depend on their norm. Leveraging this assumption, we introduce the Relative Gaussian Mechanism (RGM), in which the variance of the noise depends on the norm of the output. We prove tight bounds on the RDP parameters under relative L2 sensitivity, and characterize the privacy loss incurred by using output-dependent noise. In particular, we show that RGM naturally adapts to a latent variable that would control the norm of the output. Finally, we instantiate our framework to show tight guarantees for Private Gradient Descent, a problem that naturally fits our relative L2 sensitivity assumption.
A Massive Quiescent Galaxy in a Group Environment at z = 4.53

We report on the spectroscopic confirmation of a massive quiescent galaxy at z spec = 4.53 in the COSMOS field. The object was first identified as a galaxy with suppressed star formation at z phot ∼ 4.65 from the COSMOS2020 catalog. The follow-up spectroscopy with Keck/MOSFIRE in the K band reveals faint [O ii] emission and the Balmer break, indicative of evolved stellar populations. We fit the spectral energy distribution using photometry and a spectrum to infer physical properties. The obtained stellar mass is high (M * ∼ 1010.8 M ⊙) and the current star formation rate is more than 1 dex below that of main-sequence galaxies at z = 4.5. Its star formation history suggests that this galaxy experienced rapid quenching from z ∼ 5. The galaxy is among the youngest quiescent galaxies confirmed so far at z spec > 3 with z form ∼ 5.2 (200 Myr ago), which is the epoch when 50% of the total stellar mass was formed. A unique aspect of the galaxy is that it is in an extremely dense region; there are four massive star-forming galaxies at 4.4 < z phot < 4.7 located within 150 physical kpc from the galaxy. Interestingly, three of them have virial radii that strongly overlap with that of the central quiescent galaxy (∼70 kpc), suggesting that the overdensity region is likely the highest-redshift candidate of a dense group with a spectroscopically confirmed quiescent galaxy at the center. The group provides us with a unique opportunity to gain insights into the role of the group environment in quenching at z ∼ 5, which corresponds to the formation epoch of massive elliptical galaxies in the local Universe.
Hierarchical Time Series Forecasting with Bayesian Modeling

We encounter time series data in many domains such as finance, physics, business, and weather. One of the main tasks of time series analysis, one that helps to take informed decisions under uncertainty, is forecasting. Time series are often hierarchically structured, e.g., a company sales might be broken down into different regions, and each region into different stores. In some cases the number of series in the hierarchy is too big to fit in a single model to produce forecasts in relevant time, and a decentralized approach is beneficial. One way to do this is to train independent forecasting models for each series and for some summary statistics series implied by the hierarchy (e.g. the sum of all series) and to pass those models to a reconciliation algorithm to improve those forecasts by sharing information between the series. In this work we focus on the reconciliation step, and propose a method to do so from a Bayesian perspective - Bayesian forecast reconciliation. We also define the common case of linear Gaussian reconciliation, where the forecasts are Gaussian and the hierarchy has linear structure, and show that we can compute reconciliation in closed form. We evaluate these methods on synthetic and real data sets, and compare them to other work in this field.