Miracle-dz/UniFAR-FTUs huggingface dataset:
Socially Responsible AI Algorithms: Issues, Purposes, and Challenges

In the current era, people and society have grown increasingly reliant on artificial intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of humanity flourishes. It also comes with substantial risks for oppression and calamity. Discussions about whether we should (re)trust AI have repeatedly emerged in recent years and in many quarters, including industry, academia, healthcare, services, and so on. Technologists and AI researchers have a responsibility to develop trustworthy AI systems. They have responded with great effort to design more responsible AI algorithms. However, existing technical solutions are narrow in scope and have been primarily directed towards algorithms for scoring or classification tasks, with an emphasis on fairness and unwanted bias. To build long-lasting trust between AI and human beings, we argue that the key is to think beyond algorithmic fairness and connect major aspects of AI that potentially cause AI’s indifferent behavior. In this survey, we provide a systematic framework of Socially Responsible AI Algorithms that aims to examine the subjects of AI indifference and the need for socially responsible AI algorithms, define the objectives, and introduce the means by which we may achieve these objectives. We further discuss how to leverage this framework to improve societal well-being through protection, information, and prevention/mitigation. This article appears in the special track on AI & Society.
Statistical learning for accurate and interpretable battery lifetime prediction

Data-driven methods for battery lifetime prediction are attracting increasing attention for applications in which the degradation mechanisms are poorly understood and suitable training sets are available. However, while advanced machine learning and deep learning methods promise high performance with minimal data preprocessing, simpler linear models with engineered features often achieve comparable performance, especially for small training sets, while also providing physical and statistical interpretability. In this work, we use a previously published dataset to develop simple, accurate, and interpretable data-driven models for battery lifetime prediction. We first present the “capacity matrix” concept as a compact representation of battery electrochemical cycling data, along with a series of feature representations. We then create a number of univariate and multivariate models, many of which achieve comparable performance to the highest-performing models previously published for this dataset. These
Graphical Elastic Net and Target Matrices: Fast Algorithms and Software for Sparse Precision Matrix Estimation

We consider estimation of undirected Gaussian graphical models and inverse covariances in high-dimensional scenarios by penalizing the corresponding precision matrix. While single $L_1$ (Graphical Lasso) and $L_2$ (Graphical Ridge) penalties for the precision matrix have already been studied, we propose the combination of both, yielding an Elastic Net type penalty. We enable additional flexibility by allowing to include diagonal target matrices for the precision matrix. We generalize existing algorithms for the Graphical Lasso and provide corresponding software with an efficient implementation to facilitate usage for practitioners. Our software borrows computationally favorable parts from a number of existing packages for the Graphical Lasso, leading to an overall fast(er) implementation and at the same time yielding also much more methodological flexibility.
On the radius of Gaussian free field excursion clusters

We consider the Gaussian free field φ on Z, for d ≥ 3, and give sharp bounds on the probability that the radius of a finite cluster in the excursion set {φ ≥ h} exceeds a large value N for any height h 6= h∗, where h∗ refers to the corresponding percolation critical parameter. In dimension 3, we prove that this probability is sub-exponential in N and decays as exp{− 6 (h − h∗) N logN } as N → ∞ to principal exponential order. When d ≥ 4, we prove that these tails decay exponentially in N . Our results extend to other quantities of interest, such as truncated two-point functions and the two-arms probability for annuli crossings at scale N .
Parametrization of quark and gluon generalized parton distributions in a dynamical framework

We present a parametrization of the chiral even generalized parton distributions, $H$, $E$, $\widetilde{H}$, $\widetilde{E}$, for the quark, antiquark and gluon, in the perturbative QCD-parton framework. Parametric analytic forms are given as a function of two equivalent sets of variables $x,\xi,t$ (symmetric frame) and $X,\zeta,t$ (asymmetric frame), at an initial scale, $Q_o^2$. In the $X>\zeta$ region a convenient and flexible form is obtained as the product of a Regge term $\propto X^{-\alpha + \alpha' t}$, describing the low $X$ behavior, times a spectator model-based functional form depending on various mass parameters; the behavior at $X<\zeta$, is determined using the generalized parton distributions symmetry and polynomiality properties. The parameters are constrained using data on the flavor separated nucleon electromagnetic elastic form factors, the axial and pseudoscalar nucleon form factors, and the parton distribution functions from both the deep inelastic unpolarized and polarized nucleon structure functions. For the gluon distributions we use, in particular, constraints provided by recent lattice QCD moments calculations. The parametrization's kinematical range of validity is: $0.0001 \leq X \leq 0.85$, $0.01 \leq \zeta \leq 0.85$, $0 \leq -t \leq 1$ GeV$^2$, $2 \leq Q^2 \leq 100$ GeV$^2$. With the simultaneous description of the quark, anti-quark and gluon sectors, this parametrization represents a first tool enabling a global QCD analysis of deeply virtual exclusive experiments.
Do We Really Need Deep Learning Models for Time Series Forecasting?

Time series forecasting is a crucial task in machine learning, as it has a wide range of applications including but not limited to forecasting electricity consumption, traffic, and air quality. Traditional forecasting models rely on rolling averages, vector auto-regression and auto-regressive integrated moving averages. On the other hand, deep learning and matrix factorization models have been recently proposed to tackle the same problem with more competitive performance. However, one major drawback of such models is that they tend to be overly complex in comparison to traditional techniques. In this paper, we report the results of prominent deep learning models with respect to a well-known machine learning baseline, a Gradient Boosting Regression Tree (GBRT) model. Similar to the deep neural network (DNN) models, we transform the time series forecasting task into a window-based regression problem. Furthermore, we feature-engineered the input and output structure of the GBRT model, such that, for each training window, the target values are concatenated with external features, and then flattened to form one input instance for a multi-output GBRT model. We conducted a comparative study on nine datasets for eight state-of-the-art deep-learning models that were presented at top-level conferences in the last years. The results demonstrate that the window-based input transformation boosts the performance of a simple GBRT model to levels that outperform all state-of-the-art DNN models evaluated in this paper.
Lithiated amidines: syntheses and structural characterisations

The reaction of a toluene solution of PhNC(Ph)NHPh (N,N′-diphenylbenzamidine) with n-butyllithium gave the toluene-solvated amidinolithium compound {Li[PhNC(Ph)NPh]} n 1. Similarly the same reaction performed in the presence of the Lewis-base donors (Me 2 N) 3 PO (hmpa), Me 2 N(CH 2 ) 2 NMe 2 (tmen) or [Me 2 N(CH 2 ) 2 ] 2 NMe (pmdien) yielded the amidinolithium complexes {Li[PhNC(Ph)NPh·hmpa]} 2 2, {Li[PhNC(Ph)NPh]·tmen} 3 and {Li[PhNC(Ph)NPh]·pmdien} 4 respectively. In addition the reaction of a toluene solution of the related amidine PhNC(Me)NHPh (N,N′-diphenylacetamidine) with LiBu n in the presence of hmpa afforded {Li[PhNC(Me)NPh]·hmpa} 2 5. The solid-state structures of 2–5, which have been resolved by single-crystal X-ray diffraction methods, show both similarities and differences. The complexes 3 and 4, which contain the di- and tri-dentate ligands tmen and pmdien respectively, are monomeric, whilst use of the unidentate Lewis base hmpa results in dimers 2 and 5. However, the way in which dimerisation is achieved differs. The co-ordination geometry about the lithium cation is clearly influenced by the choice of donor and as such shows how a change in the denticity of the donor ligand utilised can have a significant effect on the solid-state structure of the system.
First Observed Interaction of the Circumstellar Envelope of an S-star with the Environment of Sgr A*

Several publications highlight the importance of the observations of bow shocks to learn more about the surrounding interstellar medium and radiation field. We revisit the most prominent dusty and gaseous bow shock source, X7, close to the supermassive black hole, Sgr A*, using multiwavelength analysis. For the purpose of this study, we use Spectrograph for Integral Field Observations in the Near Infrared (SINFONI) (H+K-band) and NACO L′- and M′-band) data sets between 2002 and 2018 with additional COMIC/ADONIS+RASOIR (L′-band) 7 7 COME-ON-PLUS Infrared Camera/Adaptive Optics Near Infrared System + Renouveau de l’Analyseur de Surface d’Onde InfraRouge data of 1999. By analyzing the line maps of SINFONI, we identify a velocity of ∼200 km s−1 from the tip to the tail. Furthermore, a combination of the multiwavelength data of NACO and SINFONI in the H-, K-, L′-, and M′-bands results in a two-component blackbody fit that implies that X7 is a dust-enshrouded stellar object. The observed ongoing elongation and orientation of X7 in the Brγ line maps and the NACO L′-band continuum indicate a wind arising at the position of Sgr A* or at the IRS16 complex. Observations after 2010 show that the dust and the gas shell seems to be decoupled in the projection from its stellar source S50. The data also implies that the tail of X7 thermally heats up due to the presence of S50. The gas emission at the tip is excited because of the related forward scattering (Mie scattering), which will continue to influence the shape of X7 in the near future. In addition, we find excited [Fe iii] lines, which together with the recently analyzed dusty sources and the Brγ-bar underline the uniqueness of this source. COME-ON-PLUS Infrared Camera/Adaptive Optics Near Infrared System + Renouveau de l’Analyseur de Surface d’Onde InfraRouge
Spatio-temporal variability of surface water quality parameters in a South African estuarine lake system

Long-term (20+ years) water quality datasets for estuaries are rare, especially for smaller systems. Monitoring of salinity, temperature, pH, dissolved oxygen and turbidity has been undertaken since 1991 in the intensively utilised, modified, and managed temporarily open/closed Touw Estuary and its associated three interconnected estuarine lakes, Eilandvlei, Langvlei and Rondevlei, of the Wilderness Lakes System, South Africa, a national park and Ramsar site. Spatial variability of salinity, pH and turbidity was pronounced in the Touw Estuary but largely absent in the lakes. Significant differences in median salinity, pH, dissolved oxygen and turbidity occurred between lakes, with reverse salinity and pH gradients frequently occurring. Seasonal variability in temperature and dissolved oxygen occurred in all waterbodies. Significant long-term declines in salinity have occurred in the more inland lakes, with decreases in turbidity and pH also occurring in some waterbodies. Water chemistry of the Wilderness Lakes is changing from that of an estuarine to a lacustrine system. Both biological and physical features were driving water quality changes, including reductions in river inflow, reduced marine connectivity, constriction of flow between waterbodies, and declines in submerged plant biomass. Management actions are proposed relating specifically to addressing the apparent causes for water quality changes.
SITELLE Hα Imaging Spectroscopy of z ∼ 0.25 Clusters: Emission-line Galaxy Detection and Ionized Gas Offset in Abell 2390 and Abell 2465

Environmental effects are crucial to the understanding of the evolution of galaxies in dense environments, such as galaxy clusters. Using the large field of view of SITELLE, the unique imaging Fourier transform spectrograph at the Canada–France–Hawaii Telescope, we are able to obtain 2D spectral information for a large and complete sample of cluster galaxies out to the infall region. We describe a pipeline developed to identify emission-line galaxies (ELGs) from the datacube using cross-correlation techniques. We present results based on the spatial offsets between the emission-line regions and stellar continua in ELGs from two z ∼ 0.25 galaxy clusters, Abell 2390 and A2465. We find a preference for the offsets to be pointed away from the cluster center. Combining the two clusters, there is a 3σ excess for high-velocity galaxies within the virial radius to have the offsets pointed away from the cluster center. Assuming the offset is a proxy for the velocity vector of a galaxy, as expected from ram pressure stripping, this excess indicates that ram pressure stripping occurs most effectively during the first passage of an infalling galaxy, leading to the quenching of its star formation. We also find that, outside the virial region, the continuum-normalized Hα line flux for infalling galaxies with large offsets is on average lower than those with small or no measurable offset, further supporting ram pressure as a dominant quenching mechanism during the initial infall stages.
One-shot Policy Elicitation via Semantic Reward Manipulation

Synchronizing expectations and knowledge about the state of the world is an essential capability for effective collaboration. For robots to effectively collaborate with humans and other autonomous agents, it is critical that they be able to generate intelligible explanations to reconcile differences between their understanding of the world and that of their collaborators. In this work we present Single-shot Policy Explanation for Augmenting Rewards (SPEAR), a novel sequential optimization algorithm that uses semantic explanations derived from combinations of planning predicates to augment agents' reward functions, driving their policies to exhibit more optimal behavior. We provide an experimental validation of our algorithm's policy manipulation capabilities in two practically grounded applications and conclude with a performance analysis of SPEAR on domains of increasingly complex state space and predicate counts. We demonstrate that our method makes substantial improvements over the state-of-the-art in terms of runtime and addressable problem size, enabling an agent to leverage its own expertise to communicate actionable information to improve another's performance.
Synergistic multi-spectral CT reconstruction with directional total variation

This work considers synergistic multi-spectral CT reconstruction where information from all available energy channels is combined to improve the reconstruction of each individual channel. We propose to fuse these available data (represented by a single sinogram) to obtain a polyenergetic image which keeps structural information shared by the energy channels with increased signal-to-noise ratio. This new image is used as prior information during a channel-by-channel minimization process through the directional total variation. We analyse the use of directional total variation within variational regularization and iterative regularization. Our numerical results on simulated and experimental data show improvements in terms of image quality and in computational speed. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 2’.
Optimal Action-based or User Prediction-based Haptic Guidance: Can You Do Even Better?

The recently advanced robotics technology enables robots to assist users in their daily lives. Haptic guidance (HG) improves users’ task performance through physical interaction between robots and users. It can be classified into optimal action-based HG (OAHG), which assists users with an optimal action, and user prediction-based HG (UPHG), which assists users with their next predicted action. This study aims to understand the difference between OAHG and UPHG and propose a combined HG (CombHG) that achieves optimal performance by complementing each HG type, which has important implications for HG design. We propose implementation methods for each HG type using deep learning-based approaches. A user study (n=20) in a haptic task environment indicated that UPHG induces better subjective evaluations, such as naturalness and comfort, than OAHG. In addition, the CombHG that we proposed further decreases the disagreement between the user intention and HG, without reducing the objective and subjective scores.
The subtlety of Ly α photons: changing the expected range of the 21-cm signal

We present the evolution of the 21-cm signal from cosmic dawn and the epoch of reionization (EoR) in an upgraded model including three subtle effects of Ly α radiation: Ly α heating, CMB heating (mediated by Ly α photons), and multiple scattering of Ly α photons. Taking these effects into account we explore a wide range of astrophysical models and quantify the impact of these processes on the global 21-cm signal and its power spectrum. We find that Ly α heating raises the gas temperature by up to $\mathcal {O}(100)$ degrees (CMB heating adds a few more degrees) in models with weak X-ray heating, thus suppressing the predicted 21-cm signals. Varying the astrophysical parameters broadly in an attempt to cover the full plausible range of models, we find that in the upgraded model the absorption trough of the global signal reaches a lowest possible floor of −165 mK at redshifts z ≈ 15 − 19. This is in contrast with the predictions for a pure adiabatically cooling Universe, for which the deepest possible absorption is a monotonically decreasing function of cosmic time and is −178 mK at z = 19 and −216 mK at z = 15, dropping to even lower values at lower redshifts. With Ly α and CMB heating included we also observe a strong suppression in the low-redshift power spectra, with the maximum possible power (evaluated over the ensemble of models) attenuated by a factor of 6.6 at z = 9 and k = 0.1 Mpc−1. Finally, we find that at high redshifts corresponding to cosmic dawn, the multiple scattering of Ly α photons leads to an amplification of the power spectrum by a factor of ∼2 − 3 in all models.
Weakly-Supervised Multi-Face 3D Reconstruction

3D face reconstruction plays a very important role in many real-world multimedia applications, including digital entertainment, social media, affection analysis, and person identification. The de-facto pipeline for estimating the parametric face model from an image requires to firstly detect the facial regions with landmarks, and then crop each face to feed the deep learning-based regressor. Comparing to the conventional methods performing forward inference for each detected instance independently, we suggest an effective end-to-end framework for multi-face 3D reconstruction, which is able to predict the model parameters of multiple instances simultaneously using single network inference. Our proposed approach not only greatly reduces the computational redundancy in feature extraction but also makes the deployment procedure much easier using the single network model. More importantly, we employ the same global camera model for the reconstructed faces in each image, which makes it possible to recover the relative head positions and orientations in the 3D scene. We have conducted extensive experiments to evaluate our proposed approach on the sparse and dense face alignment tasks. The experimental results indicate that our proposed approach is very promising on face alignment tasks without fully-supervision and pre-processing like detection and crop. Our implementation is publicly available at \url{https://github.com/kalyo-zjl/WM3DR}.
Interspeech 2021 Deep Noise Suppression Challenge

The Deep Noise Suppression (DNS) challenge is designed to foster innovation in the area of noise suppression to achieve superior perceptual speech quality. We recently organized a DNS challenge special session at INTERSPEECH and ICASSP 2020. We open-sourced training and test datasets for the wideband scenario. We also open-sourced a subjective evaluation framework based on ITU-T standard P.808, which was also used to evaluate participants of the challenge. Many researchers from academia and industry made significant contributions to push the field forward, yet even the best noise suppressor was far from achieving superior speech quality in challenging scenarios. In this version of the challenge organized at INTERSPEECH 2021, we are expanding both our training and test datasets to accommodate full band scenarios. The two tracks in this challenge will focus on real-time denoising for (i) wide band, and(ii) full band scenarios. We are also making available a reliable non-intrusive objective speech quality metric called DNSMOS for the participants to use during their development phase.
Environment Transfer for Distributed Systems

Collecting sufficient amount of data that can represent various acoustic environmental attributes is a critical problem for distributed acoustic machine learning. Several audio data augmentation techniques have been introduced to address this problem but they tend to remain in simple manipulation of existing data and are insufficient to cover the variability of the environments. We propose a method to extend a technique that has been used for transferring acoustic style textures between audio data. The method transfers audio signatures between environments for distributed acoustic data augmentation. This paper devises metrics to evaluate the generated acoustic data, based on classification accuracy and content preservation. A series of experiments were conducted using UrbanSound8K dataset and the results show that the proposed method generates better audio data with transferred environmental features while preserving content features.
Scalable Feature Matching Across Large Data Collections

Abstract This article is concerned with matching feature vectors in a one-to-one fashion across large collections of datasets. Formulating this task as a multidimensional assignment problem with decomposable costs (MDADC), we develop fast algorithms with time complexity roughly linear in the number n of datasets and space complexity a small fraction of the data size. These remarkable properties hinge on using the squared Euclidean distance as dissimilarity function, which can reduce matching problems between pairs of datasets to n problems and enable calculating assignment costs on the fly. To our knowledge, no other method applicable to the MDADC possesses these linear scaling and low-storage properties necessary to large-scale applications. In numerical experiments, the novel algorithms outperform competing methods and show excellent computational and optimization performances. An application of feature matching to a large neuroimaging database is presented. The algorithms of this article are implemented in the R package matchFeat available at github.com/ddegras/matchFeat. Supplementary materials for this article are available online.