Miracle-dz/UniFAR-FTUs huggingface dataset:
Boost Sum-Product Performance for Multiuser Detection in mMTC at Millimeter Wave

We consider the multiuser detection (MUD) problem, i.e., how to separate and decode colliding data streams, in the uplink of massive Machine Type Communications (mMTC) at millimeter wave (mmWave). Operating on factor-graphs by passing messages, the sum-product algorithm and its variants are widely applied in many other scenarios. However, in this paper, we find that their performance in mMTC at mmWave could be dramatically degraded due to the ill-conditioned MUD channel gain matrix and the existence of enormous short cycles in their corresponding factor-graphs, which are caused by the limited scattering of mmWave and the sharing of a same codebook for error correction among densely located user equipments. Assuming LDPC codes are used for error correction, we further propose a novel sum-product based approach to dealing with the MUD problem in mMTC at mmWave. It first leverages the propagation characteristics of mmWave to optimize the factor-graph for MUD by removing short cycles based on node-split and node-contraction, and then takes a dynamic-programming based method to approximate the messages passing on the resulted factor-graph, which can achieve a higher decoding accuracy. Extensive simulation results show that our approach outperforms the state-of-the-art sum-product based approaches significantly.
Loop Currents and Anomalous Hall Effect from Time-Reversal Symmetry-Breaking Superconductivity on the Honeycomb Lattice

We study a tight-binding model on the honeycomb lattice of chiral $d$-wave superconductivity that breaks time-reversal symmetry. Due to its nontrivial sublattice structure, we show that it is possible to construct a gauge-invariant time-reversal-odd bilinear of the pairing potential. The existence of this bilinear reflects the sublattice polarization of the pairing state. We show that it generates persistent loop current correlations around each lattice site and opens a topological mass gap at the Dirac points, resembling Haldane's model of the anomalous quantum Hall effect. In addition to the usual chiral $d$-wave edge states, there also exist electron-like edge resonances due to the topological mass gap. We show that the presence of loop-current correlations directly leads to a nonzero intrinsic ac Hall conductivity, which produces the polar Kerr effect without an external magnetic field. Similar results also hold for the nearest-neighbor chiral $p$-wave pairing. We briefly discuss the relevance of our results to superconductivity in twisted bilayer graphene.
Challenging High-Level ab initio Rovibrational Spectroscopy: The Nitrous Oxide Molecule

Abstract The equilibrium structure and rovibrational energies of nitrous oxide (N2O) in its electronic ground state (X1Σ+) are derived from a high-level ab initio potential energy function (PEF). This PEF is based on a composite approach with the basic contribution given by explicitly correlated coupled-cluster (CC) calculations. Smaller contributions include corrections due to inner-shell correlation, scalar-relativistic effects and higher-order correlation up to iterative pentuple excitations (CCSDTQP in CC nomenclature). The high importance of higher-order correlation in order to reach the desired accuracy led to the use of an extrapolation scheme to approximately account for the effect of hextuple and some pentuple excitations. A reasoning for the soundness of the method is given in this work. The results of the rovibrational calculations are compared to those of two multi-reference (MR) based composite PEFs, where the basic contribution is given by MR configuration interaction and MR average coupled-pair functional calculations. A highly accurate electric dipole moment function is also computed by the three composite methods in excellent agreement with the experimental values available. Subtle irregularities in the intensity pattern are reproduced in great detail and several kinds of resonances are analyzed without the need to empirically adjust our best ab initio PEF. The equilibrium bond lengths were determined by a mixed experimental/theoretical approach yielding Re(NN) = 1.12695(10) Å and Re(NO) = 1.18539(5) Å.
Testing different CO 2 response algorithms against a face crop rotation experiment and application for climate change impact assessment at different sites in Germany

In regional studies the effect of elevated CO 2 level on crop biomass and yield had not been considered in most cases, although several approaches were described in literature. Different algorithms describing CO 2 response on crop growth and crop water use efficiency have been integrated in the soil-crop model HERMES. The approaches are different in complexity and parameter requirement. Their suitability to explain crop growth responses and soil water dynamics observed in a six-year agricultural crop rotation (winter barley, sugar beet, winter wheat) under elevated atmospheric CO 2 level in a FACE experiment was tested. All algorithms were able to describe an observed increase in above-ground dry matter for all crops in the rotation. Increasing water use efficiency with rising CO 2 was also reflected. A combination of a semi-empirical Michaelis-Menten approach describing a direct impact of CO 2 on photosynthesis and a Penman-Monteith approach with a simple stomata conduction model for evapotranspiration yielded the best simulation result expressed by model performance indicators. Scenario simulations with and without CO 2 effect were performed for different sites in Germany for the present situation and the SRES-AIB scenario using statistically downscaled climate change scenarios from the WETTREG model. Results show that without consideration of the CO 2 effect mostly negative impacts on crop yields were simulated. Considering the CO 2 effect compensated the negative trend in most cases and turned yield effects to a positive impact.
Protocol and Data Description: The Free-Living Activity Study for Health

Physical behavior can be assessed using a range of competing methods. The Free-Living Activity Study for Health (FLASH) is an ongoing study that facilitates the comparison of such methods. The purpose of this report is to describe the FLASH, with a particular emphasis on a subsample of participants who have consented to have their deidentified data released in a shared repository. Participants in the FLASH wear seven physical activity monitors for a 24-hr period and then complete a detailed recall using the Activities Completed Over Time in 24-hr online assessment tool. The participants can optionally agree to be video recorded for 30–60 min, which allows for direct observation as a criterion indicator of their behavior during that period. As of version 0.1. 0, the repository includes data from 38 participants, and the sample size will grow as data are collected, processed, and released in future versions. The repository makes it possible to combine sensor data (e.g., from ActiGraph and SenseWear) with minute-by-minute contextual data (from the Activities Completed Over Time in 24-hr recall system), which enables the FLASH to generate benchmark data for a wide range of future research. The repository itself provides an example of how a powerful open-source tool (GitHub) can be used to share data and code in a way that encourages communication and collaboration among a variety of scientists (e.g., algorithm developers and end users). The FLASH data set will provide long-term benefits to researchers interested in advancing the science of physical behavior monitoring.
A Comprehensive Overhaul of Feature Distillation

We investigate the design aspects of feature distillation methods achieving network compression and propose a novel feature distillation method in which the distillation loss is designed to make a synergy among various aspects: teacher transform, student transform, distillation feature position and distance function. Our proposed distillation loss includes a feature transform with a newly designed margin ReLU, a new distillation feature position, and a partial L2 distance function to skip redundant information giving adverse effects to the compression of student. In ImageNet, our proposed method achieves 21.65% of top-1 error with ResNet50, which outperforms the performance of the teacher network, ResNet152. Our proposed method is evaluated on various tasks such as image classification, object detection and semantic segmentation and achieves a significant performance improvement in all tasks. The code is available at project page.
Cross-Entropy Adversarial View Adaptation for Person Re-Identification

Person re-identification (re-ID) is a task of matching pedestrians under disjoint camera views. To recognize paired snapshots, it has to cope with large cross-view variations caused by the camera view shift. The supervised deep neural networks are effective in producing a set of non-linear projections that can transform cross-view images into a common feature space. However, they typically impose a symmetric architecture, leaving the network ill-conditioned on its optimization. In this paper, we learn view-invariant subspace for person re-ID, and its corresponding similarity metric using an adversarial view adaptation approach. The main contribution is to learn coupled asymmetric mappings regarding view characteristics which are adversarially trained to address the view discrepancy by optimizing the cross-entropy view confusion objective. To determine the similarity value, the network is empowered with a similarity discriminator to promote features that are highly discriminant in distinguishing positive and negative pairs. The other contribution includes an adaptive weighing on the most difficult samples to address the imbalance of within-/between-identity pairs. Our approach achieves notably improved performance in comparison with the state-of-the-arts on benchmark datasets.
Fractal geometry of Airy_2 processes coupled via the Airy sheet

In last passage percolation models lying in the Kardar-Parisi-Zhang universality class, maximizing paths that travel over distances of order $n$ accrue energy that fluctuates on scale $n^{1/3}$; and these paths deviate from the linear interpolation of their endpoints on scale $n^{2/3}$. These maximizing paths and their energies may be viewed via a coordinate system that respects these scalings. What emerges by doing so is a system indexed by $x,y \in \mathbb{R}$ and $s,t \in \mathbb{R}$ with $s < t$ of unit order quantities $W_n\big( x,s ; y,t \big)$ specifying the scaled energy of the maximizing path that moves in scaled coordinates between $(x,s)$ and $(y,t)$. The space-time Airy sheet is, after a parabolic adjustment, the putative distributional limit $W_\infty$ of this system as $n \to \infty$. The Airy sheet has recently been constructed in [15] as such a limit of Brownian last passage percolation. In this article, we initiate the study of fractal geometry in the Airy sheet. We prove that the scaled energy difference profile given by $\mathbb{R} \to \mathbb{R}: z \to W_\infty \big( 1,0 ; z,1 \big) - W_\infty \big( -1,0 ; z,1 \big)$ is a non-decreasing process that is constant in a random neighbourhood of almost every $z \in \mathbb{R}$; and that the exceptional set of $z \in \mathbb{R}$ that violate this condition almost surely has Hausdorff dimension one-half. Points of violation correspond to special behaviour for scaled maximizing paths, and we prove the result by investigating this behaviour, making use of two inputs from recent studies of scaled Brownian LPP; namely, Brownian regularity of profiles, and estimates on the rarity of pairs of disjoint scaled maximizing paths that begin and end close to each other.
Activity Driven Weakly Supervised Object Detection

Weakly supervised object detection aims at reducing the amount of supervision required to train detection models. Such models are traditionally learned from images/videos labelled only with the object class and not the object bounding box. In our work, we try to leverage not only the object class labels but also the action labels associated with the data. We show that the action depicted in the image/video can provide strong cues about the location of the associated object. We learn a spatial prior for the object dependent on the action (e.g. "ball" is closer to "leg of the person" in "kicking ball"), and incorporate this prior to simultaneously train a joint object detection and action classification model. We conducted experiments on both video datasets and image datasets to evaluate the performance of our weakly supervised object detection model. Our approach outperformed the current state-of-the-art (SOTA) method by more than 6% in mAP on the Charades video dataset.
Critical and analytical thinking skill in ecology learning: A correlational study

Critical and analytical thinking skills are included in higher-order thinking skills which become the focus of educational goals in recent era. This study aimed to determine the relationship between critical and analytical thinking skills in ecology learning at senior high school in East Jakarta. This research was conducted from March to June 2020. The research method used was descriptive correlational with associative research. This study involved 180 students. As many as 52 students were selected as research participants using cluster random sampling. The data were collected using a critical and analysis thinking rubric which was integrated to essay questions. The data analysis technique used was simple linear regression. The results indicates that there is significant correlation between students’ critical and analytical thinking skill (r = 0.812, r2 = 0.659, N = 52). In addition, students’ analytical thinking skill could be predicted using the regression equation y = 5.41 + 0.91x which means that it gives a positive effect to students’ critical thinking skills. The contribution of critical thinking skills with analytical thinking skills was 65.9%.
Characterization of Exoplanet Atmospheres with the Optical Coronagraph on WFIRST

Wide-Field Infrared Survey Telescope (WFIRST)-CGI is a NASA technology demonstration mission that is charged with demonstrating key technologies for future exo-Earth imaging missions in space. In the process, it will obtain images and low-resolution spectra of a handful to a dozen extrasolar planets and possibly protoplanetary disks. Its unprecedented contrast levels in the optical will provide astronomers’ with their first direct look at mature, Jupiter-sized planets at moderate separations. This paper addresses the question: what science can be done with such data? An analytic noise model, which is informed by the ongoing engineering developments, is used to compute maximum achievable signal-to-noise ratios and scientifically viable integration times for hypothetical star–planet systems, as well as to investigate the constraining power of various combinations of WFIRST-CGI photometric and spectral observations. This work introduces two simple models for planetary geometric albedos, which are inspired largely by the solar system’s gas giants. The first planet model is a hybrid Jupiter–Neptune model, which separately treats the short and long wavelengths where chromophores and methane dominate absorption, respectively. The second planet model fixes cloud and haze properties in CoolTLusty to match Jupiter’s albedo spectrum, it then perturbs only the metallicity. MCMC retrievals performed on simulated observations are used to assess the precision with which planet model parameters can be measured subject to different exposure times and observing cases. Planet radius is recovered within ±15% for all observing cases with both the hybrid model and the CoolTLusty metallicity grid. Fit results for both models’ parameterizations of geometric albedo spectra demonstrate that a rough indication of the metallicity or methane content should be possible for some WFIRST-CGI targets. We conclude that real observations will likely be able to differentiate between extreme cases using these models, but will lack the precision necessary to uncover subtle trends.
Determination of the Primordial Helium Abundance Based on NGC 346, an H ii Region of the Small Magellanic Cloud

To place meaningful constraints on Big Bang Nucleosynthesis models, the primordial helium abundance determination is crucial. Low-metallicity H ii regions have been used to estimate it because their statistical uncertainties are relatively small. We present a new determination of the primordial helium abundance, based on long-slit spectra of the H ii region NGC 346 in the small Magellanic cloud. We obtained spectra using three 409″ × 0.51″ slits divided into 97 subsets. They cover the range λλ3600–7400 of the electromagnetic spectrum. We used PyNeb and standard reduction procedures to determine the physical conditions and chemical composition. We found that for NGC 346 X = 0.7465, Y = 0.2505, and Z = 0.0030. By assuming ΔY/ΔO = 3.3 ± 0.7 we found that the primordial helium abundance is YP = 0.2451 ± 0.0026 (1σ). Our YP value is in agreement with the value of neutrino families, Nν, and with the neutron half-life time, τn, obtained in the laboratory.
He 3He 4 solutions and other low-density Fermi liquids in a magnetic field

A theory is proposed for a Fermi liquid of low density, such as a solution of ~e~ in superfluid He4, in an arbitrary magnetic field. The characteristics of the solution are determined by an expansion in the concentration x ' I 3 and are speci fed, in a field that is not too strong, by a single parameter, the s scattering length. At almost total polarization of the spin system, the main contribution is already made by p-scattering. Values are obtained for the thermodynamic and hydrodynamic quantities and for the propagation velocity of the low-frequency oscillations (transverse spin waves, high-frequency first sound, and coupled spin-sound modes that exist in weak magnetic fields). The most noticeable are the magnetokinetic effects connected with the appreciable growth of the fennion mean free path and of the kinetic coefficients.
Modeling Vocabulary for Big Code Machine Learning

When building machine learning models that operate on source code, several decisions have to be made to model source-code vocabulary. These decisions can have a large impact: some can lead to not being able to train models at all, others significantly affect performance, particularly for Neural Language Models. Yet, these decisions are not often fully described. This paper lists important modeling choices for source code vocabulary, and explores their impact on the resulting vocabulary on a large-scale corpus of 14,436 projects. We show that a subset of decisions have decisive characteristics, allowing to train accurate Neural Language Models quickly on a large corpus of 10,106 projects.
Unsteady Chemically Reacting Casson Fluid Flow in an Irregular Channel with Convective Boundary

Abstract A mathematical model has been performed for momentum, temperature, and mass concentration of a time-dependent Casson fluid flow between a long vertical wavy wall and a parallel wavy wall subject to convective boundary conditions. Perturbation technique is used to convert the coupled partial differential equations for velocity, temperature, and mass concentration to systems of ordinary differential equations. Analytical results for these differential equations are computed. The effects of various physical parameters such as thermal conductivity, thermal Grashof number, solutal Grashof number, heat absorption parameter, and Biot number are analysed graphically.
Self-Evaluation in One-Shot Learning from Demonstration of Contact-Intensive Tasks

Humans naturally "program" a fellow collaborator to perform a task by demonstrating the task few times. It is intuitive, therefore, for a human to program a collaborative robot by demonstration and many paradigms use a single demonstration of the task. This is a form of one-shot learning in which a single training example, plus some context of the task, is used to infer a model of the task for subsequent execution and later refinement. This paper presents a one-shot learning from demonstration framework to learn contact-intensive tasks using only visual perception of the demonstrated task. The robot learns a policy for performing the tasks in terms of a priori skills and further uses self-evaluation based on visual and tactile perception of the skill performance to learn the force correspondences for the skills. The self-evaluation is performed based on goal states detected in the demonstration with the help of task context and the skill parameters are tuned using reinforcement learning. This approach enables the robot to learn force correspondences which cannot be inferred from a visual demonstration of the task. The effectiveness of this approach is evaluated using a vegetable peeling task.
General relativistic boson stars

There is accumulating evidence that (fundamental) scalar fields may exist in nature. The gravitational collapse of such a boson cloud would lead to a boson star (BS) as a new type of a compact object. As with white dwarfs and neutron stars, a limiting mass exists similarly, below which a BS is stable against complete gravitational collapse to a black hole. According to the form of the self-interaction of the basic constituents and spacetime symmetry, we can distinguish mini-, axidilaton, soliton, charged, oscillating and rotating BSs. Their compactness prevents a Newtonian approximation; however, modifications of general relativity, as in the case of Jordan? Brans? Dicke theory as a low-energy limit of strings, would provide them with gravitational memory. In general, a BS is a compact, completely regular configuration with structured layers due to the anisotropy of scalar matter, an exponentially decreasing 'halo', a critical mass inversely proportional to the constituent mass, an effective radius and a large particle number. Due to the Heisenberg principle, a completely stable branch exists, and as a coherent state, it allows for rotating solutions with quantized angular momentum. In this review, we concentrate on the fascinating possibilities of detecting the various subtypes of (excited) BSs: possible signals include gravitational redshift and (micro-)lensing, emission of gravitational waves, or, in the case of a giant BS, its dark matter contribution to the rotation curves of galactic halos.
Defined Contribution Pension Plans: Who Has Seen the Risk?

The trend towards eliminating defined benefit (DB) pension plans in favour of defined contribution (DC) plans implies that increasing numbers of pension plan participants will bear the risk that final realized portfolio values may be insufficient to fund desired retirement cash flows. We compare the outcomes of various asset allocation strategies for a typical DC plan investor. The strategies considered include constant proportion, linear glide path, and optimal dynamic (multi-period) time consistent quadratic shortfall approaches. The last of these is based on a double exponential jump diffusion model. We determine the parameters of the model using monthly US data over a 90-year sample period. We carry out tests in a synthetic market which is based on the same jump diffusion model and also using bootstrap resampling of historical data. The probability that portfolio values at retirement will be insufficient to provide adequate retirement incomes is relatively high, unless DC investors adopt optimal allocation strategies and raise typical contribution rates. This suggests there is a looming crisis in DC plans, which requires educating DC plan holders in terms of realistic expectations, required contributions, and optimal asset allocation strategies.