Miracle-dz/UniFAR-FTUs huggingface dataset:
Skeleton Based Sign Language Recognition Using Whole-body Keypoints

—Sign language is a visual language that is used by deaf or speech impaired people to communicate with each other. Sign language is always performed by fast transitions of hand gestures and body postures, requiring a great amount of knowledge and training to understand it. Sign language recognition becomes a useful yet challenging task in computer vision. Skeleton-based action recognition is becoming popular that it can be further ensembled with RGB-D based method to achieve state-of-the-art performance. However, skeleton-based recognition can hardly be applied to sign language recognition tasks, majorly because skeleton data contains no indication of hand gestures or facial expressions. Inspired by the recent development of whole-body pose estimation [1], we propose recognizing sign language based on the whole-body key points and features. The recognition results are further ensembled with other modalities of RGB and optical ﬂows to improve the accuracy further. In the challenge about isolated sign language recognition hosted by ChaLearn using a new large-scale multi-modal Turkish Sign Language dataset (AUTSL) [2]. Our method achieved leading accuracy in both the development phase and test phase. This manuscript is the fact sheet version of our method. Our workshop paper version will be released soon. Our code has been made available at https://github.com/jackyjsy/CVPR21Chal-SLR
Rapid parameter estimation of discrete decaying signals using autoencoder networks

In this work we demonstrate the use of neural networks for rapid extraction of signal parameters of discretely sampled signals. In particular, we use dense autoencoder networks to extract the parameters of interest from exponentially decaying signals and decaying oscillations. By using a three-stage training method and careful choice of the neural network size, we are able to retrieve the relevant signal parameters directly from the latent space of the autoencoder network at significantly improved rates compared to traditional algorithmic signal-analysis approaches. We show that the achievable precision and accuracy of this method of analysis is similar to conventional algorithm-based signal analysis methods, by demonstrating that the extracted signal parameters are approaching their fundamental parameter estimation limit as provided by the Cramér–Rao bound. Furthermore, we demonstrate that autoencoder networks are able to achieve signal analysis, and, hence, parameter extraction, at rates of 75 kHz, orders-of-magnitude faster than conventional techniques with similar precision. Finally, our exploration of the limitations of our approach in different computational systems suggests that analysis rates of > 200 kHz are feasible using neural networks in systems where the transfer time between the data-acquisition system and data-analysis modules can be kept below ∼3 µs.
Tomography of time-dependent quantum spin networks with machine learning

Interacting quantum Hamiltonians are fundamental to quantum computing. Data-based tomography of timeindependent quantum Hamiltonians has been achieved, but an open challenge is to ascertain the structures of time-dependent quantum Hamiltonians using time series measurements taken locally from a small subset of the spins. Physically, the dynamical evolution of a spin system under time-dependent driving or perturbation is described by the Heisenberg equation of motion. Motivated by this basic fact, we articulate a physics-enhanced machine-learning framework whose core is Heisenberg neural networks. In particular, we develop a deep learning algorithm according to some physics-motivated loss function based on the Heisenberg equation, which “forces” the neural network to follow the quantum evolution of the spin variables. We demonstrate that, from local measurements, not only can the local Hamiltonian be recovered, but the Hamiltonian reflecting the interacting structure of the whole system can also be faithfully reconstructed. We test our Heisenberg neural machine on spin systems of a variety of structures. In the extreme case in which measurements are taken from only one spin, the achieved tomography fidelity values can reach about 90%. The developed machine-learning framework is applicable to any time-dependent systems whose quantum dynamical evolution is governed by the Heisenberg equation of motion.
Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating

Efficient Market Hypothesis states that stock prices are a reflection of all the information present in the world and generating excess returns is not possible by merely analysing trade data which is already available to all public. Yet to further the research rejecting this idea, a rigorous literature review was conducted and a set of five technical indicators and 23 fundamental indicators was identified to establish the possibility of generating excess returns on the stock market. Leveraging these data points and various classification machine learning models, trading data of the 505 equities on the US S&P500 over the past 20 years was analysed to develop a classifier effective for our cause. From any given day, we were able to predict the direction of change in price by 1% up to 10 days in the future. The predictions had an overall accuracy of 83.62% with a precision of 85% for buy signals and a recall of 100% for sell signals. Moreover, we grouped equities by their sector and repeated the experiment to see if grouping similar assets together positively effected the results but concluded that it showed no significant improvements in the performance—rejecting the idea of sector-based analysis. Also, using feature ranking we could identify an even smaller set of 6 indicators while maintaining similar accuracies as that from the original 28 features and also uncovered the importance of buy, hold and sell analyst ratings as they came out to be the top contributors in the model. Finally, to evaluate the effectiveness of the classifier in real-life situations, it was backtested on FAANG (Facebook, Amazon, Apple, Netflix & Google) equities using a modest trading strategy where it generated high returns of above 60% over the term of the testing dataset. In conclusion, our proposed methodology with the combination of purposefully picked features shows an improvement over the previous studies, and our model predicts the direction of 1% price changes on the 10th day with high confidence and with enough buffer to even build a robotic trading system.
Strong Parallel Repetition for Unique Games on Small Set Expanders

Strong Parallel Repetition for Unique Games on Small Set Expanders The strong parallel repetition problem for unique games is to efficiently reduce the 1-delta vs. 1-C*delta gap problem of Boolean unique games (where C>1 is a sufficiently large constant) to the 1-epsilon vs. epsilon gap problem of unique games over large alphabet. Due to its importance to the Unique Games Conjecture, this problem garnered a great deal of interest from the research community. There are positive results for certain easy unique games (e.g., unique games on expanders), and an impossibility result for hard unique games. In this paper we show how to bypass the impossibility result by enlarging the alphabet sufficiently before repetition. We consider the case of unique games on small set expanders for two setups: (i) Strong small set expanders that yield easy unique games. (ii) Weaker small set expanders underlying possibly hard unique games as long as the game is mildly fortified. We show how to fortify unique games in both cases, i.e., how to transform the game so sufficiently large induced sub-games have bounded value. We then prove strong parallel repetition for the fortified games. Prior to this work fortification was known for projection games but seemed hopeless for unique games.
Space efficient quantum algorithms for mode, min-entropy and k-distinctness

We study the problem of determining if the mode of the output distribution of a quantum circuit given as a black-box is larger than a given threshold. We design a quantum algorithm for a promised version of this problem whose space complexity is logarithmic in the size of the domain of the distribution. Developing on top of that we further design an algorithm to estimate the largest probability among the outcomes of that circuit. This allows to revisit a few recently studied problems in the few-qubits scenario, namely k -distinctness and its gapped version, estimating the largest frequency in an array, and estimating the min-entropy of a distribution. In particular, our algorithm for k -distinctness on n -sized m -valued arrays requires O (log n +log m ) qubits compared to Ω( poly ( n )) qubits required by all the previous algorithms, and its query complexity is optimal for k = Ω( n ). We also study reductions between the above problems and derive better lower bounds for some of them. The time-complexities of our algorithms have a small overhead over their query complexities making them efficiently implementable on currently available quantum backends.
Training Dynamical Binary Neural Networks with Equilibrium Propagation

Equilibrium Propagation (EP) is an algorithm intrinsically adapted to the training of physical networks, thanks to the local updates of weights given by the internal dynamics of the system. However, the construction of such a hardware requires to make the algorithm compatible with existing neuromorphic CMOS technologies, which generally exploit digital communication between neurons and offer a limited amount of local memory. In this work, we demonstrate that EP can train dynamical networks with binary activations and weights. We first train systems with binary weights and full-precision activations, achieving an accuracy equivalent to that of full-precision models trained by standard EP on MNIST, and losing only 1.9% accuracy on CIFAR-10 with equal architecture. We then extend our method to the training of models with binary activations and weights on MNIST, achieving an accuracy within 1% of the full-precision reference for fully connected architectures and reaching the full-precision accuracy for convolutional architectures. Our extension of EP to binary networks opens new solutions for on-chip learning and provides a compact framework for training BNNs end-to-end with the same circuitry as for inference.
Ferromagnetic helical nodal line and Kane-Mele spin-orbit coupling in kagome metal 
Fe3Sn2

The two-dimensional kagome lattice hosts Dirac fermions at its Brillouin zone corners K and K’, analogous to the honeycomb lattice. In the density functional theory electronic structure of ferromagnetic kagome metal Fe3Sn2, without spin-orbit coupling we identify two energetically split helical nodal lines winding along z in the vicinity of K and K’ resulting from the trigonal stacking of the kagome layers. We find that hopping across A-A stacking introduces a layer splitting in energy while that across A-B stacking controls the momentum space amplitude of the helical nodal lines. The effect of spin-orbit coupling is found to resemble that of a Kane-Mele term, where the nodal lines can either be fully gapped to quasi-two-dimensional massive Dirac fermions, or remain gapless at discrete Weyl points depending on the ferromagnetic moment orientation. Aside from numerically establishing Fe3Sn2 as a model Dirac kagome metal, our results provide insights into materials design of topological phases from the lattice point of view, where paradigmatic low dimensional lattice models often find realizations in crystalline materials with three-dimensional stacking.
Human Brain Mapping with Multi-Thousand Channel PtNRGrids Resolves Novel Spatiotemporal Dynamics

Electrophysiological devices are critical for mapping eloquent and diseased brain regions and for therapeutic neuromodulation in clinical settings and are extensively utilized for research in brain-machine interfaces. However, the existing devices are often limited in either spatial resolution or cortical coverage, even including those with thousands of channels used in animal experiments. Here, we developed scalable manufacturing processes and dense connectorization to achieve reconfigurable thin-film, multi-thousand channel neurophysiological recording grids using platinum-nanorods (PtNRGrids). With PtNRGrids, we have achieved a multi-thousand channel array of small (30 μm) contacts with low impedance, providing unparalleled spatial and temporal resolution over a large cortical area. We demonstrate that PtNRGrids can resolve sub-millimeter functional organization of the
Investigation of UV Absorbers on Venus Using the 283 and 365 nm Phase Curves Obtained From Akatsuki

The so‐called unknown absorber in the clouds of Venus is an important absorber of solar energy, but its vertical distribution remains poorly quantified. We analyze the 283 and 365 nm phase curves of the disk‐integrated albedo measured by Akatsuki. Based on our models, we find that the unknown absorber can exist either well mixed over the entire upper cloud or within a thin layer. The necessary condition to explain the 365 nm phase curve is that the unknown absorber must absorb efficiently within the cloud scale height immediately below the cloud top. Using this constraint, we attempt to extract the SO2 abundance from the 283 nm phase curve. However, we cannot disentangle the absorption by SO2 and by the unknown absorber. Considering previous SO2 abundance measurements at midinfrared wavelengths, the required absorption coefficient of the unknown absorber at 283 nm must be more than twice that at 365 nm.
Halo-model Analysis of the Clustering of Photometric Luminous Red Galaxies at 0.10 ≤ z ≤1.05 from the Subaru Hyper Suprime-Cam Survey

We present the clustering analysis of photometric luminous red galaxies (LRGs) at a redshift range of 0.1 ≤ z ≤ 1.05 using 615,317 photometric LRGs selected from the Hyper Suprime-Cam Subaru Strategic Program, covering ∼124 deg2. Our sample covers a broad range of stellar masses and photometric redshifts and enables a halo occupation distribution analysis to study the redshift and stellar-mass dependence of dark halo properties of LRGs. We find a tight correlation between the characteristic dark halo mass to host central LRGs, Mmin , and the number density of LRGs, independently of redshifts, indicating that the formation of LRGs is associated with the global environment. The Mmin of LRGs depends only weakly on the stellar mass M ⋆ at M ⋆ ≲ 1010.75 h −2 M ⊙ at 0.3 < z < 1.05, in contrast to the case for all photometrically selected galaxies, for which Mmin shows significant dependence on M ⋆ even at low M ⋆. The weak stellar-mass dependence is indicative of the dark halo mass being the key parameter for the formation of LRGs, rather than the stellar mass. Our result suggests that the halo mass of ∼1012.5±0.2 h −1 M ⊙ is the critical mass for an efficient halo quenching due to the halo environment. We compare our result with the result of the hydrodynamical simulation to find that low-mass LRGs at z ∼ 1 will increase their stellar masses by an order of magnitude from z = 1 to 0 through mergers and satellite accretions, and that a large fraction of massive LRGs at z < 0.9 consist of LRGs that recently migrated from massive green valley galaxies or those that evolved from less massive LRGs through mergers and satellite accretions.
Learning without gradient descent encoded by the dynamics of a neurobiological model

The success of state-of-the-art machine learning is essentially all based on different variations of gradient descent algorithms that minimize some version of a cost or loss function. A fundamental limitation, however, is the need to train these systems in either supervised or unsupervised ways by exposing them to typically large numbers of training examples. Here, we introduce a fundamentally novel conceptual approach to machine learning that takes advantage of a neurobiologically derived model of dynamic signaling, constrained by the geometric structure of a network. We show that MNIST images can be uniquely encoded and classified by the dynamics of geometric networks with nearly state-of-the-art accuracy in an unsupervised way, and without the need for any training.
Deoxygenation of Exfoliated Graphite Oxide under Alkaline Conditions: A Green Route to Graphene Preparation

Graphene – a flat monolayer of carbon atoms tightly packed into a two-dimensional (2D) honeycomb lattice – is the basal building block in all graphitic materials. Since it was first reported in 2004, graphene has attracted great interest because of the unique electronic, thermal, and mechanical properties arising from its strictly 2D structure, and to its potential technical applications. However, producing graphene on a large scale using existing mechanical methods is still unfeasible. Searching for alternative chemical approaches is an urgent matter. However, the hydrophobic nature of graphene and its strong tendency to agglomerate in solvents present a great challenge to the development of fabrication methods, and severely restrict its promising applications. Although the mechanism involved remains unproven, the chemical reduction of readily available exfoliated graphite oxide (GO) with reducing agents such as hydrazine and dimethylhydrazine is a promising strategy in the large-scale production of graphene. Unfortunately, the reducing agents involved are very hazardous, and the graphene obtained presents irreversibly agglomerated features in solvents that do not contain polymer surfactants. Here, we report a new green route for the synthesis of processable graphene on a large scale. We observed that a stable graphene suspension could be quickly prepared by simply heating an exfoliated-GO suspension under strongly alkaline conditions at moderate temperatures (50–90 8C) (Figure 1a). Our initial purpose was to introduce functional groups to exfoliated GO by free-radical addition. Surprisingly, the addition of NaOH to the GO suspension – to improve the solubility of the alkyl free-radical initiator, which is carboxyl-terminated – was accompanied by a fast, unexpected color change (from yellow-brown to homogeneous black). Careful experiments revealed that exfoliated GO can undergo fast deoxygenation in strongly alkaline solutions, resulting in stable aqueous graphene suspensions
Early Outcomes of Carotid Revascularization in Retrospective Case Series

Background: Most data in carotid stenosis treatment arise from randomized control trials (RCTs) and cohort studies. The aim of this meta-analysis was to compare 30-day outcomes in real-world practice from centers providing both modalities. Methods: A data search of the English literature was conducted, using PubMed, EMBASE and CENTRAL databases, until December 2019, using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement (PRISMA) guidelines. Only studies reporting on 30-day outcomes from centers, where both techniques were performed, were eligible for this analysis. Results: In total, 15 articles were included (16,043 patients). Of the patients, 68.1% were asymptomatic. Carotid artery stenting (CAS) did not differ from carotid endarterectomy (CEA) in terms of stroke (odds ratio (OR) 0.98; 0.77–1.25; I2 = 0%), myocardial ischemic events (OR 1.03; 0.72–1.48; I2 = 0%) and all events (OR 1.0; 0.82–1.21; I2 = 0%). Pooled stroke incidence in asymptomatic patients was 1% (95% CI: 0–2%) for CEA and 1% for CAS (95% CI: 0–2%). Pooled stroke rate in symptomatic patients was 3% (95% CI: 1–4%) for CEA and 3% (95% CI: 1–4%) for CAS. The two techniques did not differ in either outcome both in asymptomatic and symptomatic patients. Conclusion: Carotid revascularization, performed in centers providing both CAS and CEA, is safe and effective. Both techniques did not differ in terms of post-procedural neurological and cardiac events, both in asymptomatic and symptomatic patients. These findings reiterate the importance of a tailored therapeutic strategy and that “real-world” outcomes may only be valid from centers providing both treatments.
Proof of Concept for a Quick and Highly Sensitive On-Site Detection of SARS-CoV-2 by Plasmonic Optical Fibers and Molecularly Imprinted Polymers

The rapid spread of the Coronavirus Disease 2019 (COVID-19) pandemic, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pathogen has generated a huge international public health emergency. Currently the reference diagnostic technique for virus determination is Reverse Transcription Polymerase Chain Reaction (RT-PCR) real time analysis that requires specialized equipment, reagents and facilities and typically 3–4 h to perform. Thus, the realization of simple, low-cost, small-size, rapid and point-of-care diagnostics tests has become a global priority. In response to the current need for quick, highly sensitive and on-site detection of the SARS-CoV-2 virus in several aqueous solutions, a specific molecularly imprinted polymer (MIP) receptor has been designed, realized, and combined with an optical sensor. More specifically, the proof of concept of a SARS-CoV-2 sensor has been demonstrated by exploiting a plasmonic plastic optical fiber sensor coupled with a novel kind of synthetic MIP nano-layer, especially designed for the specific recognition of Subunit 1 of the SARS-CoV-2 Spike protein. First, we have tested the effectiveness of the developed MIP receptor to bind the Subunit 1 of the SARS-CoV-2 spike protein, then the results of preliminary tests on SARS-CoV-2 virions, performed on samples of nasopharyngeal (NP) swabs in universal transport medium (UTM) and physiological solution (0.9% NaCl), were compared with those obtained with RT-PCR. According to these preliminary results, the sensitivity of the proposed optical-chemical sensor proved to be higher than the RT-PCR one. Furthermore, a relatively fast response time (about 10 min) to the virus was obtained without the use of additional reagents.
Enhanced LDM for Next-Generation Digital Broadcasting Transmission

In the traditional layered division multiplexing (LDM) system, by simply adjusting the injection level, the reception performance of the core layer (CL) mobile services will be decreased significantly, resulting in the deterioration of system coverage performance. Thus, it is necessary to improve the performance of the enhanced layer (EL) service reception without affecting the reception threshold of CL service. To achieve this, in this paper, an enhanced LDM (En-LDM) scheme that supports multi-service transmission is proposed for the next-generation broadcasting network. In this scheme, at the transmitter end, part of the low-density parity-check (LDPC) coded data stream of fixed service conveyed in the EL will be extracted out through puncturing, and then, it will be transmitted over the CL of the LDM signal along with the original CL data in a frequency domain multiplexing (FDM) manner. At the receiving end, the punctured data of the EL fixed service will be recovered with a higher signal-to-noise ratio (SNR). Compared to the traditional LDM scheme, the proposed En-LDM scheme can significantly improve the reception performance of fixed services without decreasing the SNR threshold of mobile services. Moreover, the En-LDM can achieve a higher channel capacity than that of the traditional LDM for both the fixed services and the overall services. The superiority of the proposed En-LDM scheme over the traditional LDM scheme is validated by the simulation under the additive white Gaussian noise (AWGN) and fading channels.