Miracle-dz/UniFAR-FTUs huggingface dataset:
Literature Review: Monopsony, Employer Consolidation, and Health Care Labor Markets

Employer wage-setting, or monopsony, power has received renewed attention in recent years among researchers and policymakers. This literature review summarizes relevant parameters and techniques for measuring the extent of labor market monopsony and discusses estimates of the earnings effects of monopsony, with particular attention paid to employer concentration (or consolidation) and health care labor markets. While I have done my best to include all of the most relevant recent studies related to employer market power and concentration, the rapid growth of this literature likely means that some papers have been inadvertently omitted. The key parameter for measuring the degree of monopsony power available to employers is the elasticity of labor supply to the firm. I briefly discuss the underlying theory behind this elasticity in Section 2 and show why it represents what Bronfenbrenner (1956) called “potential monopsony power. ” Intuitively, this parameter measures how sensitive employment at a given employer is to changes in the wage that it pays. If the employer reduced wages by a small amount and all of its workers left to work elsewhere, then the elasticity of labor supply to the firm is infinite and the employer operates in a perfectly competitive labor market. If, on the other hand, at least some of the workers stayed then this elasticity is finite, and the employer is said to have some degree of wage-setting or market power. Measuring this elasticity using the most robust methodology requires linked employeremployee data which is typically not publicly available. As a result, renewed interest in antitrust policy as a means to increase competition in the labor market, together with barriers to directly measuring employer power, has led researchers in recent years to (re)turn to labor market
What Doesn't Kill You Makes You Robust(er): How to Adversarially Train against Data Poisoning

Data poisoning is a threat model in which a malicious actor tampers with training data to manipulate outcomes at inference time. A variety of defenses against this threat model have been proposed, but each suffers from at least one of the following flaws: they are easily overcome by adaptive attacks, they severely reduce testing performance, or they cannot generalize to diverse data poisoning threat models. Adversarial training, and its variants, are currently considered the only empirically strong defense against (inference-time) adversarial attacks. In this work, we extend the adversarial training framework to defend against (training-time) data poisoning, including targeted and backdoor attacks. Our method desensitizes networks to the effects of such attacks by creating poisons during training and injecting them into training batches. We show that this defense withstands adaptive attacks, generalizes to diverse threat models, and incurs a better performance trade-off than previous defenses such as DP-SGD or (evasion) adversarial training.
Fixed point sets and the fundamental group I: semi-free actions on G-CW-complexes

<jats:p>Smith theory says that the fixed point set of a semi-free action of a group <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S030821052300063X_inline2. png" /> <jats:tex-math>$G$</jats:tex-math> </jats:alternatives> </jats:inline-formula> on a contractible space is <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S030821052300063X_inline3. png" /> <jats:tex-math>${\mathbb {Z}}_p$</jats:tex-math> </jats:alternatives> </jats:inline-formula>-acyclic for any prime factor <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S030821052300063X_inline4. png" /> <jats:tex-math>$p$</jats:tex-math> </jats:alternatives> </jats:inline-formula> of the order of <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S030821052300063X_inline5. png" /> <jats:tex-math>$G$</jats:tex-math> </jats:alternatives> </jats:inline-formula>. Jones proved the converse of Smith theory for the case <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S030821052300063X_inline6. png" /> <jats:tex-math>$G$</jats:tex-math> </jats:alternatives> </jats:inline-formula> is a cyclic group acting semi-freely on contractible, finite CW-complexes. We extend the theory to semi-free group actions on finite CW-complexes of given homotopy types, in various settings. In particular, the converse of Smith theory holds if and only if a certain <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S030821052300063X_inline7. png" /> <jats:tex-math>$K$</jats:tex-math> </jats:alternatives> </jats:inline-formula>-theoretical obstruction vanishes. We also give some examples that show the geometrical effects of different types of <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S030821052300063X_inline8. png" /> <jats:tex-math>$K$</jats:tex-math> </jats:alternatives> </jats:inline-formula>-theoretical obstructions. </jats:p>
Enhanced Multi-Objective A* Using Balanced Binary Search Trees

This work addresses a Multi-Objective Shortest Path Problem (MO-SPP) on a graph where the goal is to find a set of Pareto-optimal solutions from a start node to a destination in the graph. A family of approaches based on MOA* have been developed to solve MO-SPP in the literature. Typically, these approaches maintain a "frontier" set at each node during the search process to keep track of the non-dominated, partial paths to reach that node. This search process becomes computationally expensive when the number of objectives increases as the number of Pareto-optimal solutions becomes large. In this work, we introduce a new method to efficiently maintain these frontiers for multiple objectives by incrementally constructing balanced binary search trees within the MOA* search framework. We first show that our approach correctly finds the Pareto-optimal front, and then provide extensive simulation results for problems with three, four and five objectives to show that our method runs faster than existing techniques by up to an order of magnitude.
A Review on Methods and Applications in Multimodal Deep Learning

Deep Learning has implemented a wide range of applications and has become increasingly popular in recent years. The goal of multimodal deep learning (MMDL) is to create models that can process and link information using various modalities. Despite the extensive development made for unimodal learning, it still cannot cover all the aspects of human learning. Multimodal learning helps to understand and analyze better when various senses are engaged in the processing of information. This article focuses on multiple types of modalities, i.e., image, video, text, audio, body gestures, facial expressions, physiological signals, flow, RGB, pose, depth, mesh, and point cloud. Detailed analysis of the baseline approaches and an in-depth study of recent advancements during the past five years (2017 to 2021) in multimodal deep learning applications has been provided. A fine-grained taxonomy of various multimodal deep learning methods is proposed, elaborating on different applications in more depth. Last, main issues are highlighted separately for each domain, along with their possible future research directions.
Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast

Deep learning has been a prevalence in computational chemistry and widely implemented in molecular property predictions. Recently, self-supervised learning (SSL), especially contrastive learning (CL), has gathered growing attention for the potential to learn molecular representations that generalize to the gigantic chemical space. Unlike supervised learning, SSL can directly leverage large unlabeled data, which greatly reduces the effort to acquire molecular property labels through costly and time-consuming simulations or experiments. However, most molecular SSL methods borrow the insights from the machine learning community but neglect the unique cheminformatics (e.g., molecular fingerprints) and multilevel graphical structures (e.g., functional groups) of molecules. In this work, we propose iMolCLR, improvement of Molecular Contrastive Learning of Representations with graph neural networks (GNNs) in two aspects: (1) mitigating faulty negative contrastive instances via considering cheminformatics similarities between molecule pairs and (2) fragment-level contrasting between intramolecule and intermolecule substructures decomposed from molecules. Experiments have shown that the proposed strategies significantly improve the performance of GNN models on various challenging molecular property predictions. In comparison to the previous CL framework, iMolCLR demonstrates an averaged 1.2% improvement of ROC-AUC on eight classification benchmarks and an averaged 10.1% decrease of the error on six regression benchmarks. On most benchmarks, the generic GNN pretrained by iMolCLR rivals or even surpasses supervised learning models with sophisticated architectures and engineered features. Further investigations demonstrate that representations learned through iMolCLR intrinsically embed scaffolds and functional groups that can reason molecule similarities.
Coulomb and Riesz gases: The known and the unknown

We review what is known, unknown, and expected about the mathematical properties of Coulomb and Riesz gases. Those describe infinite configurations of points in [Formula: see text] interacting with the Riesz potential ±| x|− s (respectively, −log | x| for s = 0). Our presentation follows the standard point of view of statistical mechanics, but we also mention how these systems arise in other important situations (e.g., in random matrix theory). The main question addressed in this Review is how to properly define the associated infinite point process and characterize it using some (renormalized) equilibrium equation. This is largely open in the long range case s < d. For the convenience of the reader, we give the detail of what is known in the short range case s > d. Finally, we discuss phase transitions and mention what is expected on physical grounds.
Multiwavelength Analysis and the C iv λ1549 Å Emission Line Behavior From 2008 to 2020 of FSRQ B2 1633+382

The flat-spectrum radio quasar B2 1633+382 (4C 38.41) has been monitored for several years and has presented correlated variability in multiple wavelengths. In this article, we are performing different analyses for multiple frequencies, from gamma rays to radio, as well as the C iv λ1549 Å emission line and the λ1350 Å continuum. Using the nonthermal dominance parameter, we separated the C iv and the continuum light curves for when the dominant source of continuum is the accretion disk or the jet. We found a correlation at a delay consistent with zero between the line and the continuum dominated by disk emission indicating a very small broad-line region (BLR). From the resulting delay between the 15 GHz and gamma rays, we estimated the distance of the gamma-ray emission region from the jet apex to be ∼37 pc. The C iv flux decreases when the continuum and gamma rays increase at some of the high-activity periods. The C iv profile presents a larger variable component in its blue wing. The relation between the luminosities of C iv and the continuum does not completely follow the relation for a quasar sample. Our results lead us to propose an outflow of BLR material in the jet flow direction, a gamma-ray production through magnetic reconnection for the flaring event of mid-2011, and that there is not enough BLR material close to the radio core to be easily ionized by the nonthermal continuum.
VCVTS: Multi-Speaker Video-to-Speech Synthesis Via Cross-Modal Knowledge Transfer from Voice Conversion

Though significant progress has been made for speaker-dependent Video-to-Speech (VTS) synthesis, little attention is devoted to multi-speaker VTS that can map silent video to speech, while allowing flexible control of speaker identity, all in a single system. This paper proposes a novel multi-speaker VTS system based on cross-modal knowledge transfer from voice conversion (VC), where vector quantization with contrastive predictive coding (VQCPC) is used for the content encoder of VC to derive discrete phoneme-like acoustic units, which are transferred to a Lip-to-Index (Lip2Ind) network to infer the index sequence of acoustic units. The Lip2Ind network can then substitute the content encoder of VC to form a multi-speaker VTS system to convert silent video to acoustic units for reconstructing accurate spoken content. The VTS system also inherits the advantages of VC by using a speaker encoder to produce speaker representations to effectively control the speaker identity of generated speech. Extensive evaluations verify the effectiveness of proposed approach, which can be applied in both constrained vocabulary and open vocabulary conditions, achieving state-of-the-art performance in generating high-quality speech with high naturalness, intelligibility and speaker similarity. Our demo page is released here1.
Machine learning models and facial regions videos for estimating heart rate: a review on Patents, Datasets and Literature

Estimating heart rate is important for monitoring users in various situations. Estimates based on facial videos are increasingly being researched because they allow the monitoring of cardiac information in a non-invasive way and because the devices are simpler, as they require only cameras that capture the user’s face. From these videos of the user’s face, machine learning can estimate heart rate. This study investigates the benefits and challenges of using machine learning models to estimate heart rate from facial videos through patents, datasets, and article review. We have searched the Derwent Innovation, IEEE Xplore, Scopus, and Web of Science knowledge bases and identified seven patent filings, eleven datasets, and twenty articles on heart rate, photoplethysmography, or electrocardiogram data. In terms of patents, we note the advantages of inventions related to heart rate estimation, as described by the authors. In terms of datasets, we have discovered that most of them are for academic purposes and with different signs and annotations that allow coverage for subjects other than heartbeat estimation. In terms of articles, we have discovered techniques, such as extracting regions of interest for heart rate reading and using video magnification for small motion extraction, and models, such as EVM-CNN and VGG-16, that extract the observed individual’s heart rate, the best regions of interest for signal extraction, and ways to process them.
Deep-Learning Architectures for Multi-Pitch Estimation: Towards Reliable Evaluation

Extracting pitch information from music recordings is a challenging but important problem in music signal processing. Frame-wise transcription or multi-pitch estimation aims for detecting the simultaneous activity of pitches in polyphonic music recordings and has recently seen major improvements thanks to deep-learning techniques, with a variety of proposed network architectures. In this paper, we realize different architectures based on CNNs, the U-net structure, and self-attention components. We propose several modifications to these architectures including self-attention modules for skip connections, recurrent layers to replace the self-attention, and a multi-task strategy with simultaneous prediction of the degree of polyphony. We compare variants of these architectures in different sizes for multi-pitch estimation, focusing on Western classical music beyond the piano-solo scenario using the MusicNet and Schubert Winterreise datasets. Our experiments indicate that most architectures yield competitive results and that larger model variants seem to be beneficial. However, we find that these results substantially depend on randomization effects and the particular choice of the training-test split, which questions the claim of superiority for particular architectures given only small improvements. We therefore investigate the influence of dataset splits in the presence of several movements of a work cycle (cross-version evaluation) and propose a best-practice splitting strategy for MusicNet, which weakens the influence of individual test tracks and suppresses overfitting to specific works and recording conditions. A final evaluation on a mixed dataset suggests that improvements on one specific dataset do not necessarily generalize to other scenarios, thus emphasizing the need for further high-quality multi-pitch datasets in order to reliably measure progress in music transcription tasks.
Transfer and Marginalize: Explaining Away Label Noise with Privileged Information

Supervised learning datasets often have privileged information, in the form of features which are available at training time but are not available at test time e.g. the ID of the annotator that provided the label. We argue that privileged information is useful for explaining away label noise, thereby reducing the harmful impact of noisy labels. We develop a simple and efficient method for supervised learning with neural networks: it transfers via weight sharing the knowledge learned with privileged information and approximately marginalizes over privileged information at test time. Our method, TRAM (TRansfer and Marginalize), has minimal training time overhead and has the same test-time cost as not using privileged information. TRAM performs strongly on CIFAR-10H, ImageNet and Civil Comments benchmarks.
Convective mesoscale turbulence at very low Prandtl numbers

Abstract Horizontally extended turbulent convection, termed mesoscale convection in natural systems, remains a challenge to investigate in both experiments and simulations. This is particularly so for very low molecular Prandtl numbers, such as occur in stellar convection and in the Earth's outer core. The present study reports three-dimensional direct numerical simulations of turbulent Rayleigh–Bénard convection in square boxes of side length $L$ and height $H$ with the aspect ratio $\varGamma =L/H$ of 25, for Prandtl numbers that span almost 4 orders of magnitude, $10^{-3}\le Pr \le 7$, and Rayleigh numbers $10^5 \le Ra \le 10^7$, obtained by massively parallel computations on grids of up to $5.36\times 10^{11}$ points. The low end of this $Pr$-range cannot be accessed in controlled laboratory measurements. We report the essential properties of the flow and their trends with the Rayleigh and Prandtl numbers, in particular, the global transport of momentum and heat – the latter decomposed into convective and diffusive contributions – across the convection layer, mean vertical profiles of the temperature and temperature fluctuations and the kinetic energy and thermal dissipation rates. We also explore the degree to which the turbulence in the bulk of the convection layer resembles classical homogeneous and isotropic turbulence in terms of spectra, increment moments and dissipative anomaly, and find close similarities. Finally, we show that a characteristic scale of the order of the mesoscale seems to saturate to a wavelength of $\lambda \gtrsim 3H$ for $Pr\lesssim 0.005$. We briefly discuss possible implications of these results for the development of subgrid-scale parameterization of turbulent convection.
High-accuracy high-mass-ratio simulations for binary neutron stars and their comparison to existing waveform models

The subsequent observing runs of the advanced gravitational-wave detector network will likely provide us with various gravitational-wave observations of binary neutron star systems. For an accurate interpretation of these detections, we need reliable gravitational-wave models. To test and to point out how existing models could be improved, we perform a set of high-resolution numerical-relativity simulations for four different physical setups with mass ratios $q$ = $1.25$, $1.50$, $1.75$, $2.00$, and total gravitational mass $M = 2. 7M_\odot$ . Each configuration is simulated with five different resolutions to allow a proper error assessment. Overall, we find approximately 2nd order converging results for the dominant $(2,2)$, but also subdominant $(2,1)$, $(3,3)$, $(4,4)$ modes, while, generally, the convergence order reduces slightly for an increasing mass ratio. Our simulations allow us to validate waveform models, where we find generally good agreement between state-of-the-art models and our data, and to prove that scaling relations for higher modes currently employed for binary black hole waveform modeling also apply for the tidal contribution. Finally, we also test if the current NRTidal model to describe tidal effects is a valid description for high-mass ratio systems. We hope that our simulation results can be used to further improve and test waveform models in preparation for the next observing runs.
The Rotation of Magnetic Flux Ropes Formed during Solar Eruption

Eruptions of solar filaments often show rotational motion about their rising direction, but the mechanism governing such rotation, and how the rotation is related to the initial morphology of the preeruptive filament (and cospatial sigmoid), filament chirality, and magnetic helicity, remains elusive. The conventional view of rotation as a result of a magnetic flux rope (MFR) undergoing ideal kink instability still has difficulty explaining these relationships. Here we propose an alternative explanation for the rotation during eruptions by analyzing a magnetohydrodynamic simulation in which magnetic reconnection initiates an eruption from a sheared arcade configuration, and an MFR is formed during eruption via reconnection. The simulation reproduces a reverse-S-shaped MFR with dextral chirality, and the axis of this MFR rotates counterclockwise while rising, which compares favorably with a typical filament eruption observed from dual viewing angles. By calculating the twist and writhe numbers of the modeled MFR during its eruption, we found that, accompanied by the rotation, the nonlocal writhe of the MFR’s axis decreases while the twist of its surrounding field lines increases, and this is distinct from kink instability, which converts magnetic twist into the writhe of the MFR axis.
Modeling the GRB 170202A Fireball from Continuous Observations with the Zadko and the Virgin Island Robotic Telescopes

We present coordinated observations of GRB 170202A carried out by the Zadko and the Virgin Island Robotic Telescopes. The observations started 59 s after the event trigger, and provided nearly continuous coverage for two days, due to the unique locations of these telescopes. We clearly detected an early rise in optical emission, followed by late optical flares. By complementing these data with archival observations, we show that GRB 170202A is well described by the standard fireball model if multiple reverse shocks are taken into account. Its fireball is evidenced as expanding within a constant-density interstellar medium, with most burst parameters being consistent with the usual ranges found in the literature. The electron and magnetic energy parameters (ϵ e , ϵ B ) are orders of magnitude smaller than the commonly assumed values. We argue that the global fit of the fireball model achieved by our study should be possible for any burst, pending the availability of a sufficiently comprehensive data set. This conclusion emphasizes the crucial importance of coordinated observation campaigns of gamma-ray bursts, such as the one central to this work, to answer outstanding questions about the underlying physics driving these phenomena.