Miracle-dz/UniFAR-FTUs huggingface dataset:
Domino: Discovering Systematic Errors with Cross-Modal Embeddings

Machine learning models that achieve high overall accuracy often make systematic errors on important subsets (or slices) of data. Identifying underperforming slices is particularly challenging when working with high-dimensional inputs (e.g. images, audio), where important slices are often unlabeled. In order to address this issue, recent studies have proposed automated slice discovery methods (SDMs), which leverage learned model representations to mine input data for slices on which a model performs poorly. To be useful to a practitioner, these methods must identify slices that are both underperforming and coherent (i.e. united by a human-understandable concept). However, no quantitative evaluation framework currently exists for rigorously assessing SDMs with respect to these criteria. Additionally, prior qualitative evaluations have shown that SDMs often identify slices that are incoherent. In this work, we address these challenges by first designing a principled evaluation framework that enables a quantitative comparison of SDMs across 1,235 slice discovery settings in three input domains (natural images, medical images, and time-series data). Then, motivated by the recent development of powerful cross-modal representation learning approaches, we present Domino, an SDM that leverages cross-modal embeddings and a novel error-aware mixture model to discover and describe coherent slices. We find that Domino accurately identifies 36% of the 1,235 slices in our framework - a 12 percentage point improvement over prior methods. Further, Domino is the first SDM that can provide natural language descriptions of identified slices, correctly generating the exact name of the slice in 35% of settings.
Automatic Detection of Speech Sound Disorder in Child Speech Using Posterior-based Speaker Representations

This paper presents a macroscopic approach to automatic detection of speech sound disorder (SSD) in child speech. Typically, SSD is manifested by persistent articulation and phonological errors on speciﬁc phonemes in the language. The disorder can be detected by focally analyzing the phonemes or the words elicited by the child subject. In the present study, in-stead of attempting to detect individual phone- and word-level errors, we propose to extract a subject-level representation from a long utterance that is constructed by concatenating multiple test words. The speaker veriﬁcation approach, and posterior features generated by deep neural network models, are applied to derive various types of holistic representations. A linear classiﬁer is trained to differentiate disordered speech in normal one. On the task of detecting SSD in Cantonese-speaking children, experimental results show that the proposed approach achieves improved detection performance over previous method that requires fusing phone-level detection results. Using articulatory posterior features to derive i-vectors from multiple-word utterances achieves an unweighted average recall of 78.2% and a macro F1 score of 78.0%.
The Gaia-ESO Survey: Chemical tagging in the thin disk. Open clusters blindly recovered in the elemental abundance space

The chemical makeup of a star provides the fossil information of the environment where it formed. Under this premise, it should be possible to use chemical abundances to tag stars that formed within the same stellar association. This idea - known as chemical tagging - has not produced the expected results, especially within the thin disk where open stellar clusters have chemical patterns that are difficult to disentangle. The ultimate goal of this study is to probe the feasibility of chemical tagging within the thin disk population using high-quality data from a controlled sample of stars. We also aim at improving the existing techniques of chemical tagging and giving guidance on different strategies of clustering analysis in the elemental abundance space. Here we develop the first blind search of open clusters' members through clustering analysis in the elemental abundance space using the OPTICS algorithm applied to data from the Gaia-ESO survey. First, we evaluate different strategies of analysis, determining which ones are more performing. Second, we apply these methods to a data set including both field stars and open clusters attempting a blind recover of as many open clusters as possible. We show how specific strategies of data analysis can improve the final results. Specifically, we demonstrate that open clusters can be more efficaciously recovered with the Manhattan metric and on a space whose dimensions are carefully selected. Using these (and other) prescriptions we are able to recover open clusters hidden in our data set and find new members of these stellar associations. Our results indicate that there are chances of recovering open clusters' members via clustering analysis in the elemental abundance space. Presumably, the performances of chemical tagging will further increase with higher quality data and more sophisticated clustering algorithms.
Residual finiteness of extensions of arithmetic subgroups of SU(d,1) with cusps

Let $\Gamma$ be an arithmetic subgroup of $SU(d,1)$ with cusps, and let $X_\Gamma$ be the associated locally symmetric space. We prove that if the first inner cohomology group $H^1_! (X_\Gamma,\mathbb{C})$ is non-zero then the pre-image of $\Gamma$ in each connected cover of $SU(d,1)$ is residually finite. We also give an example of a such a group $\Gamma$ for which $H^1_! (X_\Gamma,\mathbb{C})$ is non-zero.
Gaussian Control Barrier Functions: Non-Parametric Paradigm to Safety

Inspired by the success of control barrier functions (CBFs) in addressing safety, and the rise of data-driven techniques for modeling functions, we propose a non-parametric approach for online synthesis of CBFs using Gaussian Processes (GPs). A dynamical system is defined to be safe if a subset of its states remains within the prescribed set, also called the safe set. CBFs achieve safety by designing a candidate function a priori. However, designing such a function can be challenging. Consider designing a CBF in a disaster recovery scenario where safe and navigable regions need to be determined. The decision boundary for safety here is unknown and cannot be designed a priori. Moreover, CBFs employ a parametric design approach and cannot handle arbitrary changes to the safe set in practice. In our approach, we work with safety samples to construct the CBF online by assuming a flexible GP prior on these samples, and term our formulation as a Gaussian CBF. GPs have favorable properties such as analytical tractability and robust uncertainty estimation. This allows realizing the posterior with high safety guarantees while also computing associated partial derivatives analytically for safe control. Moreover, Gaussian CBFs can change the safe set arbitrarily based on sampled data, thus allowing non-convex safe sets. We validated experimentally on a quadrotor by demonstrating safe control for 1) arbitrary safe sets, 2) collision avoidance with online safe set synthesis, 3) and juxtaposed Gaussian CBFs with CBFs in the presence of noisy states. The experiment video link is: https://youtu.be/HX6uokvCiGk.
Investment residence in the UK: past and future

In 2008, the UK introduced its (current) golden visa programme, officially known as the ‘Tier 1 (Investor)’ route, replacing the previous ‘Investor Immigrant’ category, firstly introduced in 1994. Despite the fact that golden visa programmes are not as controversial as golden passport programmes, which, at the moment, are operative in a handful of countries, the UK programme has been criticised mainly for its lack of transparency, for its inability to return any benefits to UK residents, and for creating the danger of transforming the UK into a safe haven for money launderers. These criticisms have, in fact, formed the main rationale behind the amendments effected to the programme in recent years, and (possibly) for the proposals for the abolition of this programme, which have been very recently laid on the table. The aim of this article is to trace the development of the UK’s (current) golden visa programme, from the moment of its inception in June 2008 to today. An assessment of the strengths and weaknesses of this programme is also offered, as well as a consideration of the possibilities for its development (or abolition) in the future.
Comparing the Manipulability of Approval Voting and Borda

The Gibbard-Satterthwaite theorem established that no non-trivial voting rule is strategy-proof, but that does not mean that all voting rules are equally susceptible to strategic manipulation. Over the past fifty years numerous approaches have been proposed to compare the manipulability of voting rules in terms of the probability of manipulation, the domains on which manipulation is possible, the complexity of finding such a manipulation, and others. In the closely related field of matching, Pathak and Snmez (2013) pioneered a notion of manipulability based on case-by-case comparison of manipulable profiles. The advantage of this approach is that it is independent of the underlying statistical culture or the computational power of the agents, and it has proven fruitful in the matching literature. In this paper, we extend the notion of Pathak and Snmez to voting, studying the families of k-approval and truncated Borda scoring rules. We find that, with one exception, the notion does not allow for a meaningful ordering of the manipulability of these rules.
CMGAN: Conformer-based Metric GAN for Speech Enhancement

Recently, convolution-augmented transformer (Conformer) has achieved promising performance in automatic speech recognition (ASR) and time-domain speech enhancement (SE), as it can capture both local and global dependencies in the speech signal. In this paper, we propose a conformer-based metric generative adversarial network (CMGAN) for SE in the time-frequency (TF) domain. In the generator, we utilize two-stage conformer blocks to aggregate all magnitude and complex spectrogram information by modeling both time and frequency dependencies. The estimation of magnitude and complex spectrogram is decoupled in the decoder stage and then jointly incorporated to reconstruct the enhanced speech. In addition, a metric discriminator is employed to further improve the quality of the enhanced estimated speech by optimizing the generator with respect to a corresponding evaluation score. Quantitative analysis on Voice Bank+DEMAND dataset indicates the capability of CMGAN in outperforming various previous models with a margin, i.e., PESQ of 3.41 and SSNR of 11.10 dB.
Influence of Communication Among Shared Developers on the Productivity of Open Source Software Projects

Many software developers rely on open source software for developing their applications and writing their source codes. Measuring an independent project's overall productivity is still an open problem for many technology companies. In this project, we address to bridge the gap of analyzing which are the most important features for prediction of a productivity based system. We have chosen to collect data from GitHub via their application programming interfaces (API) and analyze the data we gathered to understand the relation between the average time to close an issue and the features that we collected. Since most of the data we gathered were not Gaussian, we had to preprocess the data using outlier detection and applying transformations before statistical modeling. The best model we observed was polynomial regression with degree 5. Overall, we noticed that there are many aspects of software development that make developers increase their productivity.
Multilevel Inverters Harmonic Elimination with Non-Equal DC Voltage Sources Using Linear Fuzzy Programming

Abstract This paper presents a fast and effective optimal solution of harmonic elimination of multilevel inverters with unbalanced DC sources using fuzzy linear programming (FLP). A set of non-linear mathematical models containing a description of the general output waveform of cascaded H-bridge multilevel inverters with unbalanced DC sources, will be formulated. An objective function will be employed to obtain an optimal solution for a set of switching instances to eliminate undesired harmonics. The solution algorithm will also control the output voltage level at the desired fundamental component. FLP is used to obtain an accurate and fast solution to minimize the linearized objective function and identify the desired set of switching angles. The proposed fuzzy linear programming is tested both in simulation and experimentally, for different cases of multilevel inverters to verify the accuracy of the results.
Galaxy and mass assembly (GAMA): Self-organizing map application on nearby galaxies

Galaxy populations show bimodality in a variety of properties: stellar mass, colour, specific star-formation rate, size, and Sérsic index. These parameters are our feature space. We use an existing sample of 7556 galaxies from the Galaxy and Mass Assembly (GAMA) survey, represented using five features and the K-means clustering technique, showed that the bimodalities are the manifestation of a more complex population structure, represented by between 2 and 6 clusters. Here we use Self Organizing Maps (SOM), an unsupervised learning technique which can be used to visualize similarity in a higher dimensional space using a 2D representation, to map these five-dimensional clusters in the feature space on to two-dimensional projections. To further analyse these clusters, using the SOM information, we agree with previous results that the sub-populations found in the feature space can be reasonably mapped on to three or five clusters. We explore where the ‘green valley’ galaxies are mapped on to the SOM, indicating multiple interstitial populations within the green valley population. Finally, we use the projection of the SOM to verify whether morphological information provided by GalaxyZoo users, for example, if features are visible, can be mapped on to the SOM-generated map. Voting on whether galaxies are smooth, likely ellipticals, or ‘featured’ can reasonably be separated but smaller morphological features (bar, spiral arms) can not. SOMs promise to be a useful tool to map and identify instructive sub-populations in multidimensional galaxy survey feature space, provided they are large enough.
Randomized trials and their observational emulations: a framework for benchmarking and joint analysis

A randomized trial and an analysis of observational data designed to emulate the trial sample observations separately, but have the same eligibility criteria, collect information on some shared baseline covariates, and compare the eﬀects of the same treatments on the same outcomes. Treatment eﬀect estimates from the trial and its emulation can be compared to benchmark observational analysis methods. In a simpliﬁed setting with complete adherence to the assigned treatment strategy and no loss-to-follow-up, we show that benchmarking relies on an exchangeability condition between the populations underlying the trial and its emulation, to account for diﬀerences in the distribution of covariates between them. When this exchangeability condition holds, and the usual conditions needed for the estimates from the trial and its emulation to have a causal interpretation also hold, we derive restrictions on the law of the observed data. When the data are compatible with the restrictions, joint analysis of the trial and its emulation is possible. When the data are incompatible with the restrictions, a discrepancy between (1) estimates based on extending inferences from the trial to the population underlying the emulation and (2) the emulation itself may reﬂect either inability to benchmark (e.g., due to selective participation into the trial) or a failure of the emulation (e.g., due to unmeasured confounding), but we cannot use the data to determine which is the case. Our analysis reveals how benchmarking attempts combine causal assumptions, data analysis methods, and substantive knowledge to examine the validity of observational analysis methods.
Locally maximising orbits for the non-standard generating function of convex billiards and applications

Given an exact symplectic map T of a cylinder with a generating function H satisfying the so-called negative twist condition, H12>0 , we study the locally maximising orbits of T, that is, configurations which are local maxima of the action functional ∑nH(qn,qn+1) . We provide a necessary and sufficient condition for a configuration to be locally maximising. Using it, we consider a situation where T has two generating functions with respect to two different sets of symplectic coordinates. We suggest a simple geometric condition which guarantees that the set of locally maximising orbits with respect to both of these generating functions coincide. As the main application we show that the two generating functions for planar Birkhoff billiards satisfy this geometric condition. We apply it to get the following result: consider a centrally symmetric curve γ, for which the Birkhoff billiard map has a rotational invariant curve α of four-periodic orbits. We prove that a certain L 2-distance between γ and its ‘best approximating’ ellipse can be bounded from above in terms of the measure of the complement of the set filled by locally maximising orbits lying between α and the boundary of the phase cylinder. Moreover, this estimate is sharp, giving an effective version of a recent result on Birkhoff conjecture for centrally symmetric curves (Bialy and Mironov 2022 Ann. Math. 196 389–413). We also get a similar bound for arbitrary curves γ, that relates the measure of the complement of the set of locally maximising orbits with the L 2-distance between γ and its ‘best approximating’ circle.
A Distribution-Dependent Mumford–Shah Model for Unsupervised Hyperspectral Image Segmentation

Hyperspectral images provide a rich representation of the underlying spectrum for each pixel, allowing for a pixelwise classification/segmentation into different classes. As the acquisition of labeled training data is very time-consuming, unsupervised methods become crucial in hyperspectral image analysis. The spectral variability and noise in hyperspectral data make this task very challenging and define special requirements for such methods. Here, we present a novel unsupervised hyperspectral segmentation framework. It starts with a denoising and dimensionality reduction step by the well-established minimum noise fraction (MNF) transform. Then, the Mumford–Shah (MS) segmentation functional is applied to segment the data. We equipped the MS functional with a novel robust distribution-dependent indicator function designed to handle the characteristic challenges of hyperspectral data. To optimize our objective function with respect to the parameters for which no closed-form solution is available, we propose an efficient fixed point iteration scheme. Numerical experiments on four public benchmark datasets show that our method produces competitive results, which outperform three state-of-the-art methods substantially on three of these datasets.
Amplitudes meet Cosmology: A (Scalar) Primer

We review the most recent progress in our understanding of quantum mechanical observables in cosmology in the perturbative regime. It relies on an approach that considers them directly as functions of the data at the space-like boundary at future infinity prescinding from the explicit time evolution. It takes inspiration from the on-shell formulation of perturbative scattering amplitudes developed in the past 20 years: starting with the requirement of consistency with some fundamental principles such as causality, unitarity and locality, it provides different ways of phrasing and extracting predictions. In this review, we aim to provide a pedagogical treatment of the most recent insights about the analytic structure of the perturbative quantum mechanical observables in cosmology, its relation to fundamental principles as well as physical processes, and how such observables and their features emerge from novel well-defined mathematical objects with their own first principle definition. The review is divided in three parts: Part 0 discusses the definition of quantum mechanical observables in cosmology and some general principles; Part I reviews the boundary approach to the analysis and computation of the perturbative wavefunction of the universe; Part II provides an introduction to the combinatorial-geometrical description of cosmological processes in terms of cosmological polytopes.
Continuous Metric Learning For Transferable Speech Emotion Recognition and Embedding Across Low-resource Languages

Speech emotion recognition (SER) refers to the technique of inferring the emotional state of an individual from speech signals. SERs continue to garner interest due to their wide applicability. While the domain is mainly founded on signal processing, machine learning and deep learning methods, generalizing over languages continues to remain a challenge. To improve performance over languages, in this paper we propose a denoising autoencoder with semi-supervision using a continuous metric loss. The novelty of this work lies in our proposal for continuous metric learning, which is among the first proposals on the topic to the best of our knowledge. Furthermore, we contribute labels corresponding to the dimensional model, that were used to evaluate the quality of embedding (the labels will be made available by the time of the publication). We show that the proposed method consistently outperforms the baseline method in terms of the classification accuracy and correlation with respect to the dimensional variables.
PEAF: Learnable Power Efficient Analog Acoustic Features for Audio Recognition

At the end of Moore's law, new computing paradigms are required to prolong the battery life of wearable and IoT smart audio devices. Theoretical analysis and physical validation have shown that analog signal processing (ASP) can be more power-efficient than its digital counterpart in the realm of low-to-medium signal-to-noise ratio applications. In addition, ASP allows a direct interface with an analog microphone without a power-hungry analog-to-digital converter. Here, we present power-efficient analog acoustic features (PEAF) that are validated by fabricated CMOS chips for running audio recognition. Linear, non-linear, and learnable PEAF variants are evaluated on two speech processing tasks that are demanded in many battery-operated devices: wake word detection (WWD) and keyword spotting (KWS). Compared to digital acoustic features, higher power efficiency with competitive classification accuracy can be obtained. A novel theoretical framework based on information theory is established to analyze the information flow in each individual stage of the feature extraction pipeline. The analysis identifies the information bottleneck and helps improve the KWS accuracy by up to 7%. This work may pave the way to building more power-efficient smart audio devices with best-in-class inference performance.
A Review of Urban Mining in the Past, Present and Future

As the world’s population is growing exponentially, more and more resources are needed to meet the demand. The earth does not have an infinite amount of resources and natural reserves are on a trend towards depletion. In fact, about half of the world’s copper and other metal stocks have been mined. A solution that would help to slow the mining of virgin materials is urban mining. This is the concept of extracting valuable materials from existing infrastructure, landfills, and the dissipation of them into the environment. This would allow an increased demand for metals to be met without having to mine additional virgin materials. There are many different types and processes that can be used to collect materials, especially metals. These types include secondary mining, landfill mining, hibernation mining, dissipation mining, and in-use mining. Although there are challenges associated with urban mining, there are many benefits to continue to expand. Future research will help to improve these processes.