Miracle-dz/UniFAR-FTUs huggingface dataset:
The computational complexity of some explainable clustering problems

We study the computational complexity of some explainable clustering problems in the framework proposed by [Dasgupta et al., ICML 2020], where explainability is achieved via axis-aligned decision trees. We consider the $k$-means, $k$-medians, $k$-centers and the spacing cost functions. We prove that the first three are hard to optimize while the latter can be optimized in polynomial time.
Twist-2 light-quark distribution functions in a singly heavy baryon in the large Nc limit

A singly heavy baryon can be considered as a state consisting of the Nc − 1 light valence quarks that create the pion mean field in the large Nc limit. In the limit of the infinitely heavy quark mass, a heavy quark inside a singly heavy baryon is regarded as a mere static color source. It is required only to make the singly heavy baryon a color singlet. Thus, the Nc − 1 valence quarks govern quark dynamics inside the singly heavy baryon. Within this pion mean field framework, we investigate the twist-2 unpolarized and longitudinally polarized light-quark distribution functions inside charmed and bottom baryons at a low renormalization point. We observe that the light quarks inside a heavy baryon carry less momentum than those inside a nucleon. This feature is more prominent as the heavy quark mass increases. We discuss the baryon sum rule, momentum sum rule, and the Bjorken spin sum rule for singly heavy baryons. We also discuss the inequality conditions for the quark distribution functions. In addition, we present the results for the light-quark quasi-distribution functions.
Wave localization in number-theoretic landscapes

We investigate the localization of waves in aperiodic structures that manifest the characteristic multiscale complexity of certain arithmetic functions with a central role in number theory. In particular, we study the eigenspectra and wave localization properties of tight-binding Schr¨odinger equation models with on-site potentials distributed according to the Liouville function λ ( n ), the M¨obius function µ ( n ), and the Legendre sequence of quadratic residues modulo a prime (QRs). We employ Multifractal Detrended Fluctuation Analysis (MDFA) and establish the multifractal scaling properties of the energy spectra in these systems. Moreover, by systematically analyzing the spatial eigenmodes and their level spacing distributions, we show the absence of level repulsion with broadband localization across the entire energy spectra. Our study introduces deterministic aperiodic systems whose eigenmodes are all strongly localized in realistic ﬁnite one-dimensional systems and provides opportunities for novel quantum and classical devices of particular importance to cold-atom experiments in engineered speckle potentials and enhanced light-matter interactions.
Simultaneous Beam and User Selection for the Beamspace mmWave/THz Massive MIMO Downlink

Beamspace millimeter-wave (mmWave) and terahertz (THz) massive MIMO constitute attractive schemes for next-generation communications, given their abundant bandwidth and high throughput. However, their user and beam selection problem has not been efficiently addressed yet. Inspired by this challenge, we develop low-complexity solutions explicitly. In contrast to the zero forcing in the prior art, we introduce the dirty paper coding (DPC) into the joint user and beam selection problem. We unveil the compelling properties of the DPC sum rate in beamspace massive MIMO, showing its monotonic evolution against the number of users and beams selected. We then exploit its beneficial properties for substantially simplifying the joint user and beam selection problem. Furthermore, we develop a set of algorithms striking unique trade-offs for solving the simplified problem, facilitating simultaneous user and beam selection based on partial beamspace channels for the first time. Additionally, we derive the sum rate bound of the algorithms and analyze their complexity. Our simulation results validate the effectiveness of the proposed design and analysis, confirming their superiority over prior solutions.
Prediction of User Request and Complaint in Spoken Customer-Agent Conversations

We present the corpus called HealthCall. This was recorded in real-life conditions in the call center of Malakoff Humanis. It includes two separate audio channels, the first one for the customer and the second one for the agent. Each conversation was anonymized respecting the General Data Protection Regulation. This corpus includes a transcription of the spoken conversations and was divided into two sets: Train and Devel sets. Two important customer relationship management tasks were assessed on the HealthCall corpus: Automatic prediction of type of user requests and complaints detection. For this purpose, we have investigated 14 feature sets: 6 linguistic feature sets, 6 audio feature sets and 2 vocal interaction feature sets. We have used Bidirectional Encoder Representation from Transformers models for the linguistic features, openSMILE and Wav2Vec 2.0 for the audio features. The vocal interaction feature sets were designed and developed from Turn Takings. The results show that the linguistic features always give the best results (91.2% for the Request task and 70.3% for the Complaint task). The Wav2Vec 2.0 features seem more suitable for these two tasks than the ComPaRe16 features. Vocal interaction features outperformed ComPaRe16 features on Complaint task with a 57% rate achieved with only six features.
Synthetic inertia versus fast frequency response: a definition

This study discusses synthetic inertia from the perspective of a transmission system operator and compares it to fast frequency response based on frequency deviation. A clear distinction of the meanings between these concepts is discussed, the basis of which is a description of their characteristics. A contribution and the purpose is the clarification of these concepts in addition to share the perspectives of a transmission system operator. The frequency response of a power system based on the Nordic system is examined for future scenarios with large amounts of wind power. Conclusions are drawn regarding the benefit of synthetic inertia compared with fast frequency response based on frequency deviation.
The UV-SCOPE mission: ultraviolet spectroscopic characterization of planets and their environments

UV-SCOPE is a mission concept to determine the causes of atmospheric mass loss in exoplanets, investigate the mechanisms driving aerosol formation in hot Jupiters, and study the influence of the stellar environment on atmospheric evolution and habitability. As part of these investigations, the mission will generate a broad-purpose legacy database of time-domain ultraviolet (UV) spectra for nearly 200 stars and planets. The observatory consists of a 60 cm, f/10 telescope paired to a long-slit spectrograph, yielding simultaneous, almost continuous coverage between 1203 Å and 4000 Å, with resolutions ranging from 6000 to 240. The efficient instrument provides throughputs < 4% (far-UV; FUV) and < 15% (near-UV; NUV), comparable to HST/COS and much better than HST/STIS, over the same spectral range. A key design feature is the LiF prism, which serves as a dispersive element and provides high throughput even after accounting for radiation degradation. The use of two delta-doped Electron-Multiplying CCD detectors with UV-optimized, single-layer anti-reflection coatings provides high quantum efficiency and low detector noise. From the Earth-Sun second Lagrangian point, UV-SCOPE will continuously observe planetary transits and stellar variability in the full FUV-to-NUV range, with negligible astrophysical background. All these features make UV-SCOPE the ideal instrument to study exoplanetary atmospheres and the impact of host stars on their planets. UV-SCOPE was proposed to NASA as a Medium Explorer (MidEx) mission for the 2021 Announcement of Opportunity. If approved, the observatory will be developed over a 5-year period. Its primary science mission takes 34 months to complete. The spacecraft carries enough fuel for 6 years of operations.
Moduli spaces of stable vector bundles on Enriques surfaces

Abstract. We show that the image of the moduli space of stable bundles on an Enriques surface by the pull back map is a Lagrangian subvariety in the moduli space of stable bundles, which is a symplectic variety, on the covering K3 surface. We also describe singularities and some other features of it.
From Time Series to Networks in R with the ts2net Package

Network science established itself as a prominent tool for modeling time series and complex systems. This modeling process consists of transforming a set or a single time series into a network. Nodes may represent complete time series, segments, or single values, while links define associations or similarities between the represented parts. R is one of the main programming languages used in data science, statistics, and machine learning, with many packages available. However, no single package provides the necessary methods to transform time series into networks. This paper presents ts2net, an R package for modeling one or multiple time series into networks. The package provides the time series distance functions that can be easily computed in parallel and in supercomputers to process larger data sets and methods to transform distance matrices into networks. Ts2net also provides methods to transform a single time series into a network, such as recurrence networks, visibility graphs, and transition networks. Together with other packages, ts2net permits using network science and graph mining tools to extract information from time series.
Density Probabilities and Quantum Critical Phenomena of a Bose-Fermi Mixture in 1d Double Well Potential

The time evolution of probability density, the ground-state fidelity and the entanglement of a Bose-Fermi mixture in a 1D double well potential, are studied through the two mode approximation. We found that the behaviour of the quantum return probability shows three distinct regions. The first region is characterized by a complete miscibility, and correlated tunneling of bosons and fermion. The second region is characterized by correlated sequential tunneling and in the last region we found an increase in the tunneling frequency of the two species. We found through the Von Neumann entropy, that the boson-fermion coupling allows a maximum entanglement of quantum correlations of bosons and fermions in the same value. Finally we calculated the fidelity in the $\lambda_{FF}-\lambda_{BF}$ and $\lambda_{BB}-\lambda_{BF}$ planes and we found that the drop of the two fidelities becomes deeper and deeper as the boson-fermion interaction decreases.
Matrix Completion With Cross-Concentrated Sampling: Bridging Uniform Sampling and CUR Sampling

While uniform sampling has been widely studied in the matrix completion literature, CUR sampling approximates a low-rank matrix via row and column samples. Unfortunately, both sampling models lack flexibility for various circumstances in real-world applications. In this work, we propose a novel and easy-to-implement sampling strategy, coined Cross-Concentrated Sampling (CCS). By bridging uniform sampling and CUR sampling, CCS provides extra flexibility that can potentially save sampling costs in applications. In addition, we also provide a sufficient condition for CCS-based matrix completion. Moreover, we propose a highly efficient non-convex algorithm, termed Iterative CUR Completion (ICURC), for the proposed CCS model. Numerical experiments verify the empirical advantages of CCS and ICURC against uniform sampling and its baseline algorithms, on both synthetic and real-world datasets.
Design and feedback control of self-folding untethered miniature origami robots

Small robots that can be manufactured rapidly and inexpensively have numerous scientific applications. Current manufacturing processes for robots are made specific to the robot, with little application beyond the shape and size of the target robot. The idea of magnetically-controlled, self-folding origami robots has been proposed to enable universal robot construction on this scale. This thesis continues the investigation into miniature magnetic origami robots by exploring specifically the design and control of such robots. We detail our heatactivated self-folding process and its thermal constraints. We further explore design principles for robots using this self-folding strategy, culminating in the design and performance of an untethered, walking, swimming robot. We develop a control method for the robots utilizing a novel electromagnetic coil system that does not enclose the workspace of the robot. We model the magnetic field of the robot and the generated field from the electromagnetic coil system, and we explore their influences on the frequency and speed response of the robots due to induced motion. We develop a manual and autonomous controller to drive the robot through oscillitory walking motion. We develop a novel method of localization using Hall effect sensors to enable position feedback control as well as vision-based localization and path planning. Finally, we examine magnetically-controlled assembly using special exoskeleton robots as well as medical applications within the gastrointestinal system. Thesis Supervisor: Daniela Rus Title: Professor Thesis Supervisor: Sangbae Kim Title: Professor
TransNet: Category-Level Transparent Object Pose Estimation

Transparent objects present multiple distinct challenges to visual perception systems. First, their lack of distinguishing visual features makes transparent objects harder to detect and localize than opaque objects. Even humans find certain transparent surfaces with little specular reflection or refraction, e.g. glass doors, difficult to perceive. A second challenge is that common depth sensors typically used for opaque object perception cannot obtain accurate depth measurements on transparent objects due to their unique reflective properties. Stemming from these challenges, we observe that transparent object instances within the same category (e.g. cups) look more similar to each other than to ordinary opaque objects of that same category. Given this observation, the present paper sets out to explore the possibility of category-level transparent object pose estimation rather than instance-level pose estimation. We propose TransNet, a two-stage pipeline that learns to estimate category-level transparent object pose using localized depth completion and surface normal estimation. TransNet is evaluated in terms of pose estimation accuracy on a recent, large-scale transparent object dataset and compared to a state-of-the-art category-level pose estimation approach. Results from this comparison demonstrate that TransNet achieves improved pose estimation accuracy on transparent objects and key findings from the included ablation studies suggest future directions for performance improvements.
How a Small Amount of Data Sharing Benefits Distributed Optimization and Learning

Distributed optimization algorithms have been widely used in machine learning. While those algorithms have the merits in parallel processing and protecting data security, they often suffer from slow convergence. This paper focuses on how a small amount of data sharing could benefit distributed optimization and learning. Specifically, we examine higher-order optimization algorithms including distributed multi-block alternating direction method of multipliers (ADMM) and preconditioned conjugate gradient method (PCG). The contribution of this paper is three-folded. First, in theory, we answer when and why distributed optimization algorithms are slow by identifying the worst data structure. Surprisingly, while PCG algorithm converges slowly under heterogeneous data structure, for distributed ADMM, data homogeneity leads to the worst performance. This result challenges the common belief that data heterogeneity hurts convergence, highlighting the need for a universal approach on altering data structure for different algorithms. Second, in practice, we propose a meta-algorithm of data sharing, with its tailored applications in multi-block ADMM and PCG methods. By only sharing a small amount of prefixed data (e.g. 1%), our algorithms provide good quality estimators in different machine learning tasks within much fewer iterations, while purely distributed optimization algorithms may take hundreds more times of iterations to converge. Finally, in philosophy, we argue that even minimal collaboration can have huge synergy, which is a concept that extends beyond the realm of optimization analysis. We hope that the discovery resulting from this paper would encourage even a small amount of data sharing among different regions to combat difficult global learning problems.
Learning in Audio-visual Context: A Review, Analysis, and New Perspective

Sight and hearing are two senses that play a vital role in human communication and scene understanding. To mimic human perception ability, audio-visual learning, aimed at developing computational approaches to learn from both audio and visual modalities, has been a flourishing field in recent years. A comprehensive survey that can systematically organize and analyze studies of the audio-visual field is expected. Starting from the analysis of audio-visual cognition foundations, we introduce several key findings that have inspired our computational studies. Then, we systematically review the recent audio-visual learning studies and divide them into three categories: audio-visual boosting, cross-modal perception and audio-visual collaboration. Through our analysis, we discover that, the consistency of audio-visual data across semantic, spatial and temporal support the above studies. To revisit the current development of the audio-visual learning field from a more macro view, we further propose a new perspective on audio-visual scene understanding, then discuss and analyze the feasible future direction of the audio-visual learning area. Overall, this survey reviews and outlooks the current audio-visual learning field from different aspects. We hope it can provide researchers with a better understanding of this area. A website including constantly-updated survey is released: \url{https://gewu-lab.github. io/audio-visual-learning/}.
Importance of higher harmonics and $v_4$ puzzle in quark-gluon plasma tomography

QGP tomography aims to constrain the parameters characterizing the properties and evolution of Quark-Gluon Plasma (QGP) formed in heavy-ion collisions, by exploiting low and high- p ⊥ theory and data. Higher-order harmonics v n ( n > 2) are an important—but seldom explored—part of this approach. However, to take full advantage of them, several issues have to be addressed: i) consistency of diﬀerent methods for calculating v n , ii) importance of event-by-event ﬂuctuations to high- p ⊥ R AA and v 2 predictions, iii) sensitivity of higher harmonics to the initial state of ﬂuid-dynamical evolution. We obtain that i) several methods for calculating harmonics are compatible with each other, ii) event-by-event calculations are important in mid-central collisions, and iii) various initializations of the evolution of the medium lead to quantitatively and qualitatively diﬀerent predictions, likely to be distinguished by future measurements. We also ﬁnd that the present high- p ⊥ v 4 data cannot be reproduced using initial states for ﬂuid-dynamical evolution given by state-of-the-art models. We call this discrepancy high- p ⊥ v 4 puzzle at the LHC.
Scalable mRMR feature selection to handle high dimensional datasets: Vertical partitioning based Iterative MapReduce framework

While building machine learning models, Feature selection (FS) stands out as an essential preprocessing step used to handle the uncertainty and vagueness in the data. Recently, the minimum Redundancy and Maximum Relevance (mRMR) approach has proven to be effective in obtaining the irredundant feature subset. Owing to the generation of voluminous datasets, it is essential to design scalable solutions using distributed/parallel paradigms. MapReduce solutions are proven to be one of the best approaches to designing fault-tolerant and scalable solutions. This work analyses the existing MapReduce approaches for mRMR feature selection and identifies the limitations thereof. In the current study, we proposed VMR_mRMR, an efficient vertical partitioning-based approach using a memorization approach, thereby overcoming the extant approaches’ limitations. The experiment analysis says that VMR_mRMR significantly outperformed extant approaches and achieved a better computational gain (C.G). In addition, we also conducted a comparative analysis with the horizontal partitioning approach HMR_mRMR [1] to assess the strengths and limitations of the proposed approach.
PLMCL: Partial-Label Momentum Curriculum Learning for Multi-Label Image Classification

Multi-label image classification aims to predict all possible labels in an image. It is usually formulated as a partial-label learning problem, given the fact that it could be expensive in practice to annotate all labels in every training image. Existing works on partial-label learning focus on the case where each training image is annotated with only a subset of its labels. A special case is to annotate only one positive label in each training image. To further relieve the annotation burden and enhance the performance of the classifier, this paper proposes a new partial-label setting in which only a subset of the training images are labeled, each with only one positive label, while the rest of the training images remain unlabeled. To handle this new setting, we propose an end-to-end deep network, PLMCL (Partial Label Momentum Curriculum Learning), that can learn to produce confident pseudo labels for both partially-labeled and unlabeled training images. The novel momentum-based law updates soft pseudo labels on each training image with the consideration of the updating velocity of pseudo labels, which help avoid trapping to low-confidence local minimum, especially at the early stage of training in lack of both observed labels and confidence on pseudo labels. In addition, we present a confidence-aware scheduler to adaptively perform easy-to-hard learning for different labels. Extensive experiments demonstrate that our proposed PLMCL outperforms many state-of-the-art multi-label classification methods under various partial-label settings on three different datasets.