Miracle-dz/UniFAR-FTUs huggingface dataset:
Selection Effects in Retail Chain Pricing

This paper examines the effect of retail chain price synchronization on the extent to which nominal shocks have real effects on the economy. I develop a menu cost model in which stores belong to retail chains. However, retail chains have imperfect information over store-level state variables when determining prices. This constraint affects how firms optimally choose the timing of their price changes also known as the selection effect. I find that selection effects are more than twice as large in the presence of retail chains compared to the standard menu cost model. This relationship suggests that the standard menu cost model overestimates the degree of monetary non-neutrality by ignoring synchronization in retail chain pricing.
Can Heart Sound Denoising be Beneficial in Phonocardiogram Classification Tasksƒ

The purpose of computer-aided diagnosis (CAD) systems is to improve the detection of diseases in a shorter time and with reduced subjectivity. A robust system frequently requires a noise-free input signal. For CADs which use heart sounds, this problem is critical as heart sounds are often low amplitude and affected by some unavoidable sources of noise such as movement artifacts and physiological sounds. Removing noises by using denoising algorithms can be beneficial in improving the diagnostics accuracy of CADs. In this study, four denoising algorithms were investigated. Each algorithm has been carefully adapted to fit the requirements of the phonocardiograph signal. The effect of the denoising algorithms was objectively compared based on the improvement it introduces in the classification performance of the heart sound dataset. According to the findings, using denoising methods directly before classification decreased the algorithm's classification performance because a murmur was also treated as noise and suppressed by the denoising process. However, when denoising using Wiener estimation-based spectral subtraction was used as a preprocessing step to improve the segmentation algorithm, it increased the system's classification performance with a sensitivity of 96.0%, a specificity of 74.0%, and an overall score of 85.0%. As a result, to improve performance, denoising can be added as a preprocessing step into heart sound classifiers that are based on heart sound segmentation.
Observation of oscillation symmetry in nuclei excited state masses and widths

A systematic study of hadron masses and widths shows regular oscillations which can be fitted by a simple cosine function. This oscillation symmetry is observed studing the differences between adjacent masses of each nucleon family plotted versus the corresponding mean masses. It is also observed in the widths of excited levels, when plotted versus the corresponding masses. We observe the same distribution of periods versus the atomic number A, between the nuclear mass data and the periods describing the atomic energy levels of several neutral atoms. The nuclear level widths data are analysed in a way similar to that done for the masses. The distributions of the mass data between some different body families are compared.
Depression Classification Using n-Gram Speech Errors from Manual and Automatic Stroop Color Test Transcripts

While the psychological Stroop color test has frequently been used to analyze response delays in temporal cognitive processing, minimal research has examined incorrect/correct verbal test response pattern differences exhibited in healthy control and clinically depressed populations. Further, the development of speech error features with an emphasis on sequential Stroop test responses has been unexplored for automatic depression classification. In this study which uses speech recorded via a smart device, an analysis of и-gram error sequence distributions shows that participants with clinical depression produce more Stroop color test errors, especially sequential errors, than the healthy controls. By utilizing и-gram error features derived from multisession manual transcripts, experimentation shows that trigram error features generate up to 95% depression classification accuracy, whereas an acoustic feature baseline achieve only upwards of 75%. Moreover, и-gram error features using ASR transcripts produced up to 90% depression classification accuracy.
Early neurodevelopmental problems and risk for avoidant/restrictive food intake disorder (ARFID) in the general child population: a Japanese birth cohort study

Background: An overrepresentation of neurodevelopmental disorders (NDDs) has been observed in individuals with avoidant/restrictive food intake disorder (ARFID). Previous studies on the association between ARFID and NDDs are limited to cross-sectional data from clinical samples of small size. This study aimed to extend previous research by using prospectively collected data in children from a general population sample. We examined the occurrence and predictive power of early neurodevelopmental problems in 4-7-year-old children with suspected ARFID. Methods: Data were collected via parent-report in 3,728 children born between 2011 and 2014 in Kochi prefecture, a sub-sample of the Japan Environment and Children's Study (JECS). Neurodevelopmental problems were assessed with several instruments at different time points between 0.5 and 3 years of age as part of the JECS. In an add-on study, ARFID was identified cross-sectionally (between 4 and 7 years of age) using a newly developed screening tool. Results: Circa 3% of children at high risk for NDDs in preschool age screened positive for ARFID between age 4 and 7 years, reflecting a three times increased risk of suspected ARFID. A fifth (20.8%) of children with suspected ARFID had likely NDDs, compared to 8.6% of children without suspected ARFID. Developmental delay trajectories of children with and without suspected ARFID started to divert after the age of 6 months. Only 2.2% of children with early feeding problems later screened positive for ARFID. The inclusion of neurodevelopmental problems improved the prediction of later ARFID. Conclusions: The results mirror the previously observed overrepresentation of NDDs in ARFID populations, although to a weaker extent. In non-clinical populations, early feeding problems are common and rarely develop into ARFID, however, our findings imply that they should be monitored closely in children with high neurodevelopmental risk in order to prevent ARFID.
A twenty-segment kinematics and kinetics model for analysing golf swing mechanics

The golf swing is a complex, multi-planar, three-dimensional (3D) motion sequence performed at very high speeds. These properties make biomechanical analysis of the golf swing difficult. Hence, the aim of this study was to develop a computer model of the golf swing capable of calculating a diverse range of 3D kinematics and kinetics values based on motion analysis data collected in the laboratory. Five golfers performed six swings in the field of view of eight Falcon High Speed Resolution cameras (240 Hz), which captured the movements of 56 markers placed on the golfers and their clubs, resulting in marker trajectories that were processed into linear xyz-coordinates using the Eva Motion Analysis system. To perform the kinematics and kinetics calculations, a 20-segment rigid body model of the human body was designed in the Mechanical Systems Pack, connecting the segments by a selection of linear and spherical constraints, resulting in a system of segments with 58 degrees of freedom, with the constraint equations of motion calculated by the Newton–Lagrangian iteration method. The model allowed for the derivation of segmental sequencing, separation angles, segmental planes of motion, segmental velocity contributions, joint torques and muscle powers. The preliminary data suggest that such an integrated kinematics and kinetics analysis is necessary to understand the mechanical complexity of golf swing. Even with the small sample size analysed in this study, some interesting trends were found, such as certain violations of the classical proximal-to-distal sequencing scheme, differing swing plane and club head trajectories in the backswing and downswing phases, minimal hip angular velocity contribution to the ball at impact, concentric and eccentric muscle powers in the downswing phase, and increased lumbar loading factors from the mid-downswing phase to ball impact.
Experimental Investigation of Blade Group Vibrations in an Axial Turbine Cascade

Keywords: aeroelasticity ; annular turbine cascade ; experimental cluster measurements ; single-blade ; grouped-blades ; cluster-blade ; aerodynamic stability ; traveling-wave mode ; influence coefficients ; superposition principle ; unsteady pressure ; time-resolved measurements ; aerodynamic damping ; GTT ; LTT ; flow measurements ; unsteady flow These Ecole polytechnique federale de Lausanne EPFL, n° 6194 (2014)Programme doctoral EnergieFaculte des sciences et techniques de l'ingenieurLaboratoire de thermique appliquee et de turbomachinesJury: Dr S. -R. Cherkaoui (president) ; Dr P. Ott (directeur) ; Prof. T. Arts, Dr H.J. Belz, Prof. D. Favrat (rapporteurs) Public defense: 2014-7-4 Reference doi:10.5075/epfl-thesis-6194Print copy in library catalog Record created on 2014-06-25, modified on 2017-05-10
Influence of free-stream turbulence on boundary layer transition in favorable pressure gradients

Results from an experimental study of large-scale, two-dimensional incompressible transitional boundary layer flows are presented. Tests were conducted on a heated flat wall with a zero pressure gradient for two levels of streamwise acceleration. Convective heat-transfer distributions, laminar, transitional, and fully turbulent boundary layer mean velocity and temperature profile data, and free-stream turbulence intensity distributions are presented. Boundary layer integral quantities and shape factors are also given. Transition onset Reynolds number data obtained for this program agreed well with the results of other experimental and theoretical studies for both zero pressure gradient and accelerating flows. Comparisons of the profile data and wall heat-transfer distribution data indicated that fully turbulent mean velocity profiles were achieved upstream of fully turbulent wall heat-transfer rates.
Spatiotemporal Scene-Graph Embedding for Autonomous Vehicle Collision Prediction

In autonomous vehicles (AVs), early warning systems rely on collision prediction to ensure occupant safety. However, state-of-the-art methods using deep convolutional networks either fail at modeling collisions or are too expensive/slow, making them less suitable for deployment on AV edge hardware. To address these limitations, we propose SG2VEC, a spatiotemporal scene-graph embedding methodology that uses the graph neural network (GNN) and long short-term memory (LSTM) layers to predict future collisions via visual scene perception. We demonstrate that SG2VEC predicts collisions 8.11% more accurately and 39.07% earlier than the state-of-the-art method on synthesized data sets, and 29.47% more accurately on a challenging real-world collision data set. We also show that SG2VEC is better than the state of the art at transferring knowledge from synthetic data sets to real-world driving data sets. Finally, we demonstrate that SG2VEC performs inference $9.3\times $ faster with an 88.0% smaller model, 32.4% less power, and 92.8% less energy than the state-of-the-art method on the industry-standard Nvidia DRIVE PX 2 platform, making it more suitable for implementation on the edge.
AlphaDDA: game artificial intelligence with dynamic difficulty adjustment using AlphaZero

An artiﬁcial intelligence (AI) player has obtained superhuman skill for games like Go, Chess, and Othello (Reversi). In other words, the AI player becomes too strong as an opponent of human players. Then, we will not enjoy playing board games with the AI player. In order to entertain human players, the AI player is required to balance its skill with the human player’s one automatically. To address this issue, I propose AlphaDDA, an AI player with dynamic difﬁculty adjustment based on AlphaZero. AlphaDDA consists of a deep neural network (DNN) and Monte Carlo tree search like AlphaZero. AlphaDDA estimates the value of the game state form only the board state using the DNN and changes its skill according to the value. AlphaDDA can adjust AlphaDDA’s skill using only the state of a game without prior knowledge about an opponent. In this study, AlphaDDA plays Connect4, 6x6 Othello, which is Othello using a 6x6 size board, and Othello with the other AI agents. The other AI agents are AlphaZero, Monte Carlo tree search, Minimax algorithm, and a random player. This study shows that AlphaDDA achieves to balance its skill with the other AI agents except for a random player. AlphaDDA’s DDA ability is derived from the accurate estimation of the value from the state of a game. We will be able to use the approach of AlphaDDA for any games in that the DNN can estimate the value from the state.
Towards Domain-Independent and Real-Time Gesture Recognition Using mmWave Signal

Human gesture recognition using millimeter-wave (mmWave) signals provides attractive applications including smart home and in-car interfaces. While existing works achieve promising performance under controlled settings, practical applications are still limited due to the need of intensive data collection, extra training efforts when adapting to new domains, and poor performance for real-time recognition. In this article, we propose DI-Gesture, a domain-independent and real-time mmWave gesture recognition system. Specifically, we first derive signal variations corresponding to human gestures with spatial-temporal processing. To enhance the robustness of the system and reduce data collecting efforts, we design a data augmentation framework for mmWave signals based on correlations between signal patterns and gesture variations. Furthermore, a spatial-temporal gesture segmentation algorithm is employed for real-time recognition. Extensive experimental results show DI-Gesture achieves an average accuracy of 97.92%, 99.18%, and 98.76% for new users, environments, and locations, respectively. We also evaluate DI-Gesture in challenging scenarios like real-time recognition and sensing at extreme angles, all of which demonstrates the superior robustness and effectiveness of our system.
On Recovering the Best Rank-r Approximation from Few Entries

In this note, we investigate how well we can reconstruct the best rank-$r$ approximation of a large matrix from a small number of its entries. We show that even if a data matrix is of full rank and cannot be approximated well by a low-rank matrix, its best low-rank approximations may still be reliably computed or estimated from a small number of its entries. This is especially relevant from a statistical viewpoint: the best low-rank approximations to a data matrix are often of more interest than itself because they capture the more stable and oftentimes more reproducible properties of an otherwise complicated data-generating model. In particular, we investigate two agnostic approaches: the first is based on spectral truncation; and the second is a projected gradient descent based optimization procedure. We argue that, while the first approach is intuitive and reasonably effective, the latter has far superior performance in general. We show that the error depends on how close the matrix is to being of low rank. Both theoretical and numerical evidence is presented to demonstrate the effectiveness of the proposed approaches.
A Novel Corpus of Discourse Structure in Humans and Computers

We present a novel corpus of 445 human- and computer-generated documents, comprising about 27,000 clauses, annotated for semantic clause types and coherence relations that allow for nuanced comparison of artificial and natural discourse modes. The corpus covers both formal and informal discourse, and contains documents generated using fine-tuned GPT-2 (Zellers et al., 2019) and GPT-3(Brown et al., 2020). We showcase the usefulness of this corpus for detailed discourse analysis of text generation by providing preliminary evidence that less numerous, shorter and more often incoherent clause relations are associated with lower perceived quality of computer-generated narratives and arguments.
A Hierarchy for Replica Quantum Advantage

We prove that given the ability to make entangled measurements on at most $k$ replicas of an $n$-qubit state $\rho$ simultaneously, there is a property of $\rho$ which requires at least order $2^n$ measurements to learn. However, the same property only requires one measurement to learn if we can make an entangled measurement over a number of replicas polynomial in $k, n$. Because the above holds for each positive integer $k$, we obtain a hierarchy of tasks necessitating progressively more replicas to be performed efficiently. We introduce a powerful proof technique to establish our results, and also use this to provide new bounds for testing the mixedness of a quantum state.
Towards Axiomatic, Hierarchical, and Symbolic Explanation for Deep Models

This aims to show that the inference logic of a deep can be faithfully as a symbolic a causal graph potentially bridges the gap between connectionism and To this end, the faithfulness of the causal graph is theoretically guaranteed, because we show that the causal graph can well mimic the model’s output on an exponential number of different masked samples. Besides, such a causal graph can be further simpliﬁed and rewritten as an And-Or graph (AOG), which explains the logical relationship between interactive concepts encoded by the deep model, without losing much explanation accuracy. The code will be released when the paper is accepted.
Local Normal Forms of Noncommutative Functions

This article describes local normal forms of functions in noncommuting variables, up to equivalence generated by isomorphism of noncommutative Jacobi algebras, extending singularity theory in the style of Arnold's commutative local normal forms into the noncommutative realm. This generalisation unveils many new phenomena, including an ADE classification when the Jacobi ring has dimension zero and, by suitably taking limits, a further ADE classification in dimension one. These are natural generalisations of the simple singularities and those with infinite multiplicity in Arnold's classification. We obtain normal forms away from some exceptional Type E cases. Remarkably these normal forms have no moduli, and the key new feature is that the noncommutative world affords larger families. This theory has a range of immediate consequences to the birational geometry of 3-folds. The normal forms of dimension zero are the analytic classification of smooth 3-fold flops, and one outcome of NC singularity theory is the first complete list of all Type D flopping germs, with variants covering Type E. The normal forms of dimension one have further ramifications to divisorial contractions to a curve. In addition, the general techniques also give strong evidence towards new contractibility criteria for rational curves.
On the Equivalence between Neural Network and Support Vector Machine

Recent research shows that the dynamics of an infinitely wide neural network (NN) trained by gradient descent can be characterized by Neural Tangent Kernel (NTK) \citep{jacot2018neural}. Under the squared loss, the infinite-width NN trained by gradient descent with an infinitely small learning rate is equivalent to kernel regression with NTK \citep{arora2019exact}. However, the equivalence is only known for ridge regression currently \citep{arora2019harnessing}, while the equivalence between NN and other kernel machines (KMs), e.g. support vector machine (SVM), remains unknown. Therefore, in this work, we propose to establish the equivalence between NN and SVM, and specifically, the infinitely wide NN trained by soft margin loss and the standard soft margin SVM with NTK trained by subgradient descent. Our main theoretical results include establishing the equivalences between NNs and a broad family of $\ell_2$ regularized KMs with finite-width bounds, which cannot be handled by prior work, and showing that every finite-width NN trained by such regularized loss functions is approximately a KM. Furthermore, we demonstrate our theory can enable three practical applications, including (i) \textit{non-vacuous} generalization bound of NN via the corresponding KM; (ii) \textit{non-trivial} robustness certificate for the infinite-width NN (while existing robustness verification methods would provide vacuous bounds); (iii) intrinsically more robust infinite-width NNs than those from previous kernel regression. Our code for the experiments is available at \url{https://github.com/leslie-CH/equiv-nn-svm}.
3D Kinematics of Stellar SiO Masers in the Galactic Center

We present 3D velocity measurements and acceleration limits for stars within a few parsec of the Galactic Center (GC) black hole, Sgr A*, based on observations of 43 and 86 GHz circumstellar maser emission. Observations were taken with the Very Large Array in 2013, 2014, and 2020 and with the Atacama Large Millimeter/submillimeter Array in 2015 and 2017. We detect 28 masers in total, of which four are new detections. Combining these data with extant maser astrometry, we calculate stellar proper motions and accelerations with uncertainties as low as ∼10 μas yr−1 and 0.5 μas yr−2, respectively, corresponding to approximately 0.5 km s−1 and 0.04 km s−1 yr−1 at a distance of 8 kpc. We measure radial velocities from maser spectra with ∼0.5 km s−1 uncertainties, though the precision and accuracy of such measurements for deducing the underlying stellar velocities are limited by the complex spectral profiles of some masers. We therefore measure radial acceleration limits with typical uncertainties of ∼0.1 km s−1 yr−1. We analyze the resulting 3D velocities and accelerations with respect to expected motions resulting from models of the mass distribution in the GC.
Current-induced superconducting anisotropy of Sr$\mathsf{_2}$RuO$\mathsf{_4}$

In the unconventional superconductor Sr2RuO4, unusual first-order superconducting transition has been observed in the low-temperature and high-field region, accompanied by a four-fold anisotropy of the in-plane upper critical magnetic field Hc2. The origin of such unusual Hc2 behavior should be closely linked to the debated superconducting symmetry of this oxide. Here, toward clarification of the unusual Hc2 behavior, we performed the resistivity measurements capable of switching in-plane current directions as well as precisely controlling the field directions. Our results reveal that resistive Hc2 under the in-plane current exhibits an additional twofold anisotropy. By systematically analyzing Hc2 data taken under various current directions, we succeeded in separating the two-fold Hc2 component into the one originating from applied current and the other originating from certain imperfection in the sample. The former component, attributable to vortex flow effect, is weakened at low temperatures where Hc2 is substantially suppressed. The latter component is enhanced in the first order transition region, possibly reflecting a change in the nature of the superconducting state under high magnetic field.
Circular motion of charged particles near a charged black hole

We study the circular motion of charged particles near a charged black hole. The general form of the Lyapunov exponent of charged particles is obtained by using the Jacobian matrix. The results show that the chaos bound can be saturated by the circular motion of charged particles on the horizon. By further expanding the Lyapunov exponent near the horizon and investigating Reissner-Nordström(RN) black holes with different M/Q, we find that in contrast to the static equilibrium, the circular motion of charged particles can have a larger Lyapunov exponent due to the existence of angular momentum. For the RN black holes which have the mass-charge ratio M/Q < 2 √ 3 , the chaos bound is violated by the null circular motion and the time-like circular motion with large angular momentum.