Miracle-dz/UniFAR-FTUs huggingface dataset:
Nearly Minimal Over-Parametrization of Shallow Neural Networks

A recent line of work has shown that an overparametrized neural network can perfectly fit the training data, an otherwise often intractable nonconvex optimization problem. For (fully-connected) shallow networks, in the best case scenario, the existing theory requires quadratic over-parametrization as a function of the number of training samples. This paper establishes that linear overparametrization is sufficient to fit the training data, using a simple variant of the (stochastic) gradient descent. Crucially, unlike several related works, the training considered in this paper is not limited to the lazy regime in the sense cautioned against in [1, 2]. Beyond shallow networks, the framework developed in this work for over-parametrization is applicable to a variety of learning problems.
History state formalism for scalar particles

We present a covariant quantum formalism for scalar particles based on an enlarged Hilbert space. The particular physical theory can be introduced through a timeless Wheeler DeWitt-like equation, whose projection onto four-dimensional coordinates leads to the Klein Gordon equation. The standard quantum mechanical product in the enlarged space, which is invariant and positive definite, implies the usual Klein Gordon product when applied to its eigenstates. Moreover, the standard three-dimensional invariant measure emerges naturally from the flat measure in four dimensions when mass eigenstates are considered, allowing a rigorous identification between definite mass history states and the standard Wigner representation. Connections with the free propagator of scalar field theory and localized states are subsequently derived. The formalism also allows the superposition of different theories and remains valid in the presence of a fixed external field, revealing special orthogonality relations. Other details such as extended identities for the current density, the quantization of parameterized theories and the nonrelativistic limit, with its connection to the Page and Wootters formalism, are discussed. A related consistent second quantization formulation is also introduced.
FisheyeDistanceNet: Self-Supervised Scale-Aware Distance Estimation using Monocular Fisheye Camera for Autonomous Driving

Fisheye cameras are commonly used in applications like autonomous driving and surveillance to provide a large field of view (> 180o). However, they come at the cost of strong non-linear distortions which require more complex algorithms. In this paper, we explore Euclidean distance estimation on fisheye cameras for automotive scenes. Obtaining accurate and dense depth supervision is difficult in practice, but self-supervised learning approaches show promising results and could potentially overcome the problem. We present a novel self-supervised scale-aware framework for learning Euclidean distance and ego-motion from raw monocular fisheye videos without applying rectification. While it is possible to perform piece-wise linear approximation of fisheye projection surface and apply standard rectilinear models, it has its own set of issues like re-sampling distortion and discontinuities in transition regions. To encourage further research in this area, we will release our dataset as part of the WoodScape project [1]. We further evaluated the proposed algorithm on the KITTI dataset and obtained state-of-the-art results comparable to other self-supervised monocular methods. Qualitative results on an unseen fisheye video demonstrate impressive performance1.
Wavelet Domain Style Transfer for an Effective Perception-Distortion Tradeoff in Single Image Super-Resolution

In single image super-resolution (SISR), given a low-resolution (LR) image, one wishes to ﬁnd a high-resolution (HR) version of it which is both accurate and photorealistic. Recently, it has been shown that there exists a fundamental tradeoff between low distortion and high perceptual quality, and the generative adversarial network (GAN) is demonstrated to approach the perception-distortion (PD) bound effectively. In this paper, we propose a novel method based on wavelet domain style transfer (WDST), which achieves a better PD tradeoff than the GAN based methods. Speciﬁcally, we propose to use 2D stationary wavelet transform (SWT) to decompose one image into low-frequency and high-frequency sub-bands. For the low-frequency sub-band, we improve its objective quality through an enhancement network. For the high-frequency sub-band, we propose to use WDST to effectively improve its perceptual quality. By feat of the perfect reconstruction property of wavelets, these sub-bands can be re-combined to obtain an image which has simultaneously high objective and perceptual quality. The numerical results on various datasets show that our method achieves the best trade-off between the distortion and perceptual quality among the existing state-of-the-art SISR methods.
Rotational evolution of solar-type protostars during the star-disk interaction phase

Context. The early pre-main sequence phase during which solar-mass stars are still likely surrounded by an accretion disk represents a puzzling stage of their rotational evolution. While solar-mass stars are accreting and contracting, they do not seem to spin up substantially. Aims. It is usually assumed that the magnetospheric star-disk interaction tends to maintain the stellar rotation period constant (“disk-locking”), but this hypothesis has never been thoroughly verified. Our aim is to investigate the impact of the star-disk interaction mechanism on the stellar spin evolution during the accreting pre-main sequence phases. Methods. We devised a model for the torques acting on the stellar envelope based on studies of stellar winds, and we developed a new prescription for the star-disk coupling founded on numerical simulations of star-disk interaction and magnetospheric ejections. We then used this torque model to follow the long-term evolution of the stellar rotation. Results. Strong dipolar magnetic field components up to a few kG are required to extract enough angular momentum so as to keep the surface rotation rate of solar-type stars approximately constant for a few Myr. Furthermore an efficient enough spin-down torque can be provided by either one of the following: a stellar wind with a mass outflow rate corresponding to ≈10% of the accretion rate, or a lighter stellar wind combined with a disk that is truncated around the corotation radius entering a propeller regime. Conclusions. Magnetospheric ejections and accretion powered stellar winds play an important role in the spin evolution of solar-type stars. However, kG dipolar magnetic fields are neither uncommon or ubiquitous. Besides, it is unclear how massive stellar winds can be powered while numerical models of the propeller regime display a strong variability that has no observational confirmation. Better observational statistics and more realistic models could contribute to help lessen our calculations’ requirements.
Magnon Bose–Einstein condensation and superconductivity in a frustrated Kondo lattice

Significance Magnetically frustrated Kondo lattices are correlated metals not governed by symmetry-breaking quantum criticality, offering a new perspective on the puzzling phenomenology of strange metal behavior and unconventional superconductivity. While this concept is likely to be realized in certain heavy-fermion compounds, the corresponding models are notoriously difficult to solve, hindering robust theoretical predictions. Here we provide a comprehensive solution for a 1D Kondo ladder with a frustrated valence bond solid ground state in the presence of a magnetic field. We prove the stability of the field-induced magnon Bose–Einstein condensation and demonstrate the emergence of unconventional superconductivity. We also present strong evidence that our results carry over to two and three dimensions. These findings anchor future studies of frustrated heavy fermion systems. Motivated by recent experiments on magnetically frustrated heavy fermion metals, we theoretically study the phase diagram of the Kondo lattice model with a nonmagnetic valence bond solid ground state on a ladder. A similar physical setting may be naturally occurring in YbAl3C3, CeAgBi2, and TmB4 compounds. In the insulating limit, the application of a magnetic field drives a quantum phase transition to an easy-plane antiferromagnet, which is described by a Bose–Einstein condensation of magnons. Using a combination of field theoretical techniques and density matrix renormalization group calculations we demonstrate that in one dimension this transition is stable in the presence of a metallic Fermi sea, and its universality class in the local magnetic response is unaffected by the itinerant gapless fermions. Moreover, we find that fluctuations about the valence bond solid ground state can mediate an attractive interaction that drives unconventional superconducting correlations. We discuss the extensions of our findings to higher dimensions and argue that depending on the filling of conduction electrons, the magnon Bose–Einstein condensation transition can remain stable in a metal also in dimensions two and three.
Novel Applications of Factored Neural Machine Translation

In this work, we explore the usefulness of target factors in neural machine translation (NMT) beyond their original purpose of predicting word lemmas and their inflections, as proposed by Garcia-Martinez et al., 2016. For this, we introduce three novel applications of the factored output architecture: In the first one, we use a factor to explicitly predict the word case separately from the target word itself. This allows for information to be shared between different casing variants of a word. In a second task, we use a factor to predict when two consecutive subwords have to be joined, eliminating the need for target subword joining markers. The third task is the prediction of special tokens of the operation sequence NMT model (OSNMT) of Stahlberg et al., 2018. Automatic evaluation on English-to-German and English-to-Turkish tasks showed that integration of such auxiliary prediction tasks into NMT is at least as good as the standard NMT approach. For the OSNMT, we observed a significant improvement in BLEU over the baseline OSNMT implementation due to a reduced output sequence length that resulted from the introduction of the target factors.
The effects of ambient blue light on anger levels : Applications in the design of unmanned aircraft GCS

Light exercises broad psychophysiological effects besides vision including hormone secretion, body temperature, sleep, alertness, cognition, and emotion regulation. This study proposes that applying an integrative interaction design to the ground control station (GCS) that uses colour-coded lights to deliver peripheral information to GCS operators using blue light as a chromatic cue could promote relaxation and reduce physiological reactions associated with negative emotions with implications for emotional health and cognitive performance. Therefore, the aim of this study was to test the effects of blue light in reducing negative emotions in an enclosed environment that resembles a GCS cabin for operating unmanned aircraft. Thirty healthy participants were invited to carry out a 12-min anger-inducing task in a car driving simulator. Three equal experimental groups were formed: the first group was primed to the relaxation effects of blue light (BL1), the second group was exposed to blue light without priming (BL2) and the third group was not exposed to any ambient blue light (control group). Light was presented at three stages: prior to the task for 5 mins and during anger induction due to 2 simulated traffic jams (TJ1, TJ2). Psychological state of anger was measured using questionnaires and psychophysiological measures including: Blood Pressure (BP), Cardiac Output (CO), Heart Rate (HR), and facial Electromiography (fEMG). There was a decrease in subjective feelings of anger in the BL1 condition relative to the control condition. Systolic BP was also significantly reduced in the BL1 condition compared to the control condition. In addition, corrugator muscle activity and stroke volume (a component in CO measurement) were lower in the BL2 group compared to the control condition. The findings have implications for the design of the photometry of GCS lighting and in particular the use of blue light in confined darkened GCSs.
Growth of Massive Black Holes at High-z via Accretion Predominantly Driven by Magnetic Outflows

Luminous quasars powered by accreting supermassive black holes (SMBHs) have been found in the early universe at , which set a strong constraint on both the seed black hole (BH) mass and the rapid growth of the SMBHs. In this work, we explore how the SMBHs grow through Eddington-limited accretion driven predominantly by magnetic outflows. Most angular momentum and the released gravitational energy in the disk can be removed by magnetic outflows, therefore the mass-accretion rate of the BH can be high even if the disk is radiating at sub-Eddington luminosity. It is found that the SMBH with several billion solar masses discovered at may be grown through chaotic accretion predominantly driven by magnetic outflows from a stellar mass BH, when the disks are radiating at moderate luminosity (∼0.5 Eddington luminosity) with mild outflows. We find that most SMBHs are spinning at moderate values of spin parameter a*, which implies only a small fraction of quasars may have radio jets.
Bias-Resilient Neural Network

Presence of bias and confounding effects is inarguably one of the most critical challenges in machine learning applications that has alluded to pivotal debates in the recent years. Such challenges range from spurious associations of confounding variables in medical studies to the bias of race in gender or face recognition systems. One solution is to enhance datasets and organize them such that they do not reflect biases, which is a cumbersome and intensive task. The alternative is to make use of available data and build models considering these biases. Traditional statistical methods apply straightforward techniques such as residualization or stratification to precomputed features to account for confounding variables. However, these techniques are generally not suitable for end-to-end deep learning methods. In this paper, we propose a method based on the adversarial training strategy to learn discriminative features unbiased and invariant to the confounder(s). This is enabled by incorporating a new adversarial loss function that encourages a vanished correlation between the bias and learned features. We apply our method to synthetic data, medical images, and a gender classification (Gender Shades Pilot Parliaments Benchmark) dataset. Our results show that the learned features by our method not only result in superior prediction performance but also are uncorrelated with the bias or confounder variables. The code is available at http://github.com/QingyuZhao/BR-Net/.
Next integrated result modelling for stopping the text field recognition process in a video using a result model with per-character alternatives

In the field of document analysis and recognition using mobile devices for capturing, and the field of object recognition in a video stream, an important problem is determining the time when the capturing process should be stopped. Efficient stopping influences not only the total time spent for performing recognition and data entry, but the expected accuracy of the result as well. This paper is directed on extending the stopping method based on next integrated recognition result modelling, in order for it to be used within a string result recognition model with per-character alternatives. The stopping method and notes on its extension are described, and experimental evaluation is performed on an open dataset MIDV-500. The method was compares with previously published methods based on input observations clustering. The obtained results indicate that the stopping method based on the next integrated result modelling allows to achieve higher accuracy, even when compared with the best achievable configuration of the competing methods.
Diffractive dissociation in oniu-nucleus scattering from a partonic picture

We present a partonic picture for diffractive onium-nucleus scattering in the large-number-of-color limit from which the distribution of rapidity gaps in a certain kinematic region can be deduced. This picture allows us to draw a parallel between diffractive dissociation and the genealogy of partonic evolution, the latter being essentially similar to a branching-diffusion process in which the branching is the parton splitting, and the diffusion occurs in the transverse momenta of the partons. In particular, we show that the rapidity gap distribution corresponds to the distribution of the splitting rapidity of the last common ancestor of the partons whose transverse momenta are smaller than the nuclear saturation scale, when the scattering process is viewed in the restframe of the nucleus. Numerical calculations are also implemented to support the analytical predictions.
Deterministic Completion of Rectangular Matrices Using Ramanujan Bigraphs - II: Explicit Constructions and Phase Transitions

Matrix completion is a part of compressed sensing, and refers to determining an unknown low-rank matrix from a relatively small number of samples of the elements of the matrix. The problem has applications in recommendation engines, sensor localization, quantum tomography etc. In a companion paper (Part-1), the first and second author showed that it is possible to guarantee exact completion of an unknown low rank matrix, if the sample set corresponds to the edge set of a Ramanujan bigraph. In this paper, we present for the first time an infinite family of unbalanced Ramanujan bigraphs with explicitly constructed biadjacency matrices. In addition, we also show how to construct the adjacency matrices for the currently available families of Ramanujan graphs. In an attempt to determine how close the sufficient condition presented in Part-1 is to being necessary, we carried out numerical simulations of nuclear norm minimization on randomly generated low-rank matrices. The results revealed several noteworthy points, the most interesting of which is the existence of a phase transition. For square matrices, the maximum rank $\bar{r}$ for which nuclear norm minimization correctly completes all low-rank matrices is approximately $\bar{r} \approx d/3$, where $d$ is the degree of the Ramanujan graph. This upper limit appears to be independent of the specific family of Ramanujan graphs. The percentage of low-rank matrices that are recovered changes from 100% to 0% if the rank is increased by just two beyond $\bar{r}$. Again, this phenomenon appears to be independent of the specific family of Ramanujan graphs.
The utilization and supplementation to stubble lands for South African Mutton Merino ewes

The utilization and supplementation to stubble lands for South African Mutton Merino ewes. Laetitia Brundyn Dr. A.V. Ferreira Dr. T.S. Brand Animal Sciences Agricultural and Forestry Sciences Stellenbosch M.Sc. Agric. An experiment was conducted to determine the effect of the frequency of supplementary feeding on the production of South African Mutton Merino (SAMM) ewes grazing wheat stubble. One hundred and sixty ewes were randomly divided into two groups that consisted of four groups each. Eight camps were grazed for 138 days during which lambing occurred. Four groups of 25 ewes each grazed a 17 ha camp at a stocking density of 5.8 ewes/ha and four groups of 15 ewes each grazed a 12 ha camp at a stocking density of 5.0 ewes/ha. A combination energy/protein supplement was made available to the ewes as a lick. Two of the groups received no supplementary feed (control), two groups received 200 g/ewe/day, two groups received 400 g/ewe every second day and two groups received 600 g/ewe every third day. The smallest decrease in weight during the feeding period was observed in the ewe group that received supplementation every day as well as the ewe group that received supplementation every second day, but no significant differences were observed between these two groups. The smallest decrease in weight over the total feeding period was observed in the three ewe groups that received supplementation (P < 0.1), while the ewes that received none (control groups) lost the most weight during the same period. All supplemented groups performed significantly better than the unsupplemented control group in terms of the liveweight change over the feeding as well as the total period. No significant differences occurred in the lambing percentage, weaning percentage, birthweight, 42-day weight and survival rate of the lambs due to the different feeding regimes. It was concluded that supplementation to ewes every third day or every second day is an economically feasible option and will reduce labour and transport costs. Stellenbosch University http://scholar.sun.ac.za
de socavación local bajo condiciones hidráulicas impermanentes ( Effective flow work for estimation of pier scour under flood waves )

Bridges are important for society, especially for the mobility, economy and development of humanity. The main objectives for the design of bridges are linked to life expectancy, stability, and safety during natural extreme events such as earthquakes and floods. The global climate change increases the magnitude and frequency of extreme precipitations and floods events confirming the necessity for a better bridge-pier design ad-hoc to unsteady hydraulic conditions. Generally during a flood event, the maximum scour depth at peak-flow may be smaller than the equilibrium scour depth, which is commonly estimated using peak-flow conditions for design of bridge piers. If the local scour depth is sub-estimated the hydraulic structure can fail and on the other hand, if this scour depth is overestimated, that construction has no economic justification. The objective of this work was to study the impact of the shape, duration and peak discharge of flood hydrograph on the maximum scour depth under clear-water scour conditions and over natural uniform sediment. Scour experiments were conducted at the Laboratory of Hydraulic Engineering, University of Concepción, with constant discharges until equilibrium and with hydrographs having different duration, shapes and peak discharge. Finally, the pier scour caused by flood waves is analyzed introducing the idea of an effective work by the flow on the sediment bed around the pier. Following dimensional considerations, the author shows different possible formulations for the dimensionless flow work W* and the corresponding dimensionless parameters that govern scouring. In each case, the dimensionless flow work is shown to be a generalization of the flow intensity concept c u u , commonly used in existent scour formulas. A novel experimental installation able to reproduce any hydrograph with high precision in the laboratory flume is described and four series of experiments are presented. The first series consists of experiments with constant discharge until advanced stages of scour. The second and third series, of scour experiments with flood waves of different shapes and durations, respectively. The fourth series consists of scour experiments caused by hydrographs with multiple peaks. Results confirm that the dimensionless flow work W * represent a reliable concept for the prediction of scour caused by any hydrograph.