Miracle-dz/UniFAR-FTUs huggingface dataset:
Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble

Sign language is commonly used by deaf or mute people to communicate but requires extensive effort to master. It is usually performed with the fast yet delicate movement of hand gestures, body posture, and even facial expressions. Current Sign Language Recognition (SLR) methods usually extract features via deep neural networks and suffer overfitting due to limited and noisy data. Recently, skeleton-based action recognition has attracted increasing attention due to its subject-invariant and background-invariant nature, whereas skeleton-based SLR is still under exploration due to the lack of hand annotations. Some researchers have tried to use off-line hand pose trackers to obtain hand keypoints and aid in recognizing sign language via recurrent neural networks. Nevertheless, none of them outperforms RGB-based approaches yet. To this end, we propose a novel Skeleton Aware Multi-modal Framework with a Global Ensemble Model (GEM) for isolated SLR (SAM-SLR-v2) to learn and fuse multi-modal feature representations towards a higher recognition rate. Specifically, we propose a Sign Language Graph Convolution Network (SL-GCN) to model the embedded dynamics of skeleton keypoints and a Separable Spatial-Temporal Convolution Network (SSTCN) to exploit skeleton features. The skeleton-based predictions are fused with other RGB and depth based modalities by the proposed late-fusion GEM to provide global information and make a faithful SLR prediction. Experiments on three isolated SLR datasets demonstrate that our proposed SAM-SLR-v2 framework is exceedingly effective and achieves state-of-the-art performance with significant margins. Our code will be available at https://github.com/jackyjsy/SAM-SLR-v2
A large scale lexical and semantic analysis of Spanish language variations in Twitter

Dialectometry is a discipline devoted to studying the variations of a language around a geographical region. One of their goals is the creation of linguistic atlases capturing the similarities and diﬀerences of the language under study around the area in question. For instance, Spanish is one of the most spoken languages across the world, but not necessarily Spanish is written and spoken in the same way in diﬀerent countries. This manuscript presents a broad analysis describing lexical and semantic relationships among 26 Spanish-speaking countries around the globe. For this study, we analyze four-year of the Twitter geotagged public stream to provide an extensive survey of the Spanish language vocabularies of diﬀerent countries, its distributions, semantic usage of terms, and emojis. We also oﬀer open regional word-embedding resources for Spanish Twitter to help other researchers and practitioners take advantage of regionalized models.
Fast Block Linear System Solver Using Q-Learning Schduling for Unified Dynamic Power System Simulations

We present a fast block direct solver for the unified dynamic simulations of power systems. This solver uses a novel Q-learning based method for task scheduling. Unified dynamic simulations of power systems represent a method in which the electric-mechanical transient, medium-term and long-term dynamic phenomena are organically united. Due to the high rank and large numbers in solving, fast solution of these equations is the key to speeding up the simulation. The sparse systems of simulation contain complex nested block structure, which could be used by the solver to speed up. For the scheduling of blocks and frontals in the solver, we use a learning based task-tree scheduling technique in the framework of Markov Decision Process. That is, we could learn optimal scheduling strategies by offline training on many sample matrices. Then for any systems, the solver would get optimal task partition and scheduling on the learned model. Our learning-based algorithm could help improve the performance of sparse solver, which has been verified in some numerical experiments. The simulation on some large power systems shows that our solver is 2-6 times faster than KLU, which is the state-of-the-art sparse solver for circuit simulation problems.
Perturbation Theory-Aided Learned Digital Back-Propagation Scheme for Optical Fiber Nonlinearity Compensation

Derived from the regular perturbation treatment of the nonlinear Schrödinger equation, a machine learning-based scheme to mitigate the intra-channel optical fiber nonlinearity is proposed. Referred to as the perturbation theory-aided (PA) learned digital back propagation (LDBP), the proposed scheme constructs a deep neural network (DNN) in a way similar to the split-step Fourier method: linear and nonlinear operations alternate. Inspired by the perturbation analysis, the intra-channel cross phase modulation term is conveniently represented by matrix operations in the DNN. The introduction of this term in each nonlinear operation considerably improves the performance, as well as enables the flexibility of PA-LDBP by adjusting the numbers of spans per step. The proposed scheme is evaluated by numerical simulations of a single-carrier optical fiber communication system operating at 32 Gbaud with 64-quadrature amplitude modulation and 20 × 80 km transmission distance. The results show that the proposed scheme achieves approximately 1 dB, 1.2 dB, 1.2 dB, and 0.5 dB performance gain in terms of ${\it Q}$ factor over LDBP, when the numbers of spans per step are 1, 2, 4, and 10, respectively. Two methods are proposed to reduce the complexity of PA-LDBP, i.e., pruning the number of perturbation coefficients and chromatic dispersion compensation in the frequency domain for multi-span per step cases. Investigation of the performance and complexity suggests that PA-LDBP attains improved performance gains with reduced complexity when compared to LDBP in the cases of 4 and 10 spans per step.
AVoE: A Synthetic 3D Dataset on Understanding Violation of Expectation for Artificial Cognition

Recent work in cognitive reasoning and computer vision has engendered an increasing popularity for the Violation-of-Expectation (VoE) paradigm in synthetic datasets. Inspired by work in infant psychology, researchers have started evaluating a model's ability to discriminate between expected and surprising scenes as a sign of its reasoning ability. Existing VoE-based 3D datasets in physical reasoning only provide vision data. However, current cognitive models of physical reasoning by psychologists reveal infants create high-level abstract representations of objects and interactions. Capitalizing on this knowledge, we propose AVoE: a synthetic 3D VoE-based dataset that presents stimuli from multiple novel sub-categories for five event categories of physical reasoning. Compared to existing work, AVoE is armed with ground-truth labels of abstract features and rules augmented to vision data, paving the way for high-level symbolic predictions in physical reasoning tasks.
Genotoxic and anti-proliferative effects of aminoguanidine on gamma-irradiated MCF-7 breast cancer cells.

The intracellular production of nitric oxide is studied as a relevant phenomenon in exposure to ionizing radiation. There is evidence of local nitric oxide production in solid tumours. Its effects were observed on the relationship between their presence with tumor progression, linked to the emergence of potential genotoxic or cytotoxic damage, or loss of proliferative capacities of tumour cells. The study evaluated the effects of the administration of aminoguanidine, a selective inhibitor of an isoform of nitric oxide synthase on the frequency of genotoxic damage, loss of clonogenic potential, induction of cytotoxicity and nitrite production after exposure to ionizing radiation in radiotherapeutic doses. Human breast tumor (MCF7) cells were treated with aminoguanidine (1 or 2 mM) and irradiated by gamma radiation at doses between 0.5 and 8Gy. The study used a well stablished technique with some modifications for evaluation of genotoxic damage by frequency of micronuclei in binucleated cells. In cultures treated with 1 mM, we observed increased cytotoxicity and genotoxicity, and reduction of the clonogenic potential of the colonies. Alternatively, 2 mM aminoguanidine produced the opposite effect, apparently protecting cultures from the effects of exposures. The experiments suggested that the administration of aminoguanidine may reduce the in vitro radiossensitivity of tumors due to the increase of the frequency of genotoxic damage.
Game Theory for Adversarial Attacks and Defenses

Adversarial attacks can generate adversarial inputs by applying small but intentionally worst-case perturbations to samples from the dataset, which leads to even state-of-the-art deep neural networks outputting incorrect answers with high confidence. Hence, some adversarial defense techniques are developed to improve the security and robustness of the models and avoid them being attacked. Gradually, a game-like competition between attackers and defenders formed, in which both players would attempt to play their best strategies against each other while maximizing their own payoffs. To solve the game, each player would choose an optimal strategy against the opponent based on the prediction of the opponent's strategy choice. In this work, we are on the defensive side to apply game-theoretic approaches on defending against attacks. We use two randomization methods, random initialization and stochastic activation pruning, to create diversity of networks. Furthermore, we use one denoising technique, super resolution, to improve models' robustness by preprocessing images before attacks. Our experimental results indicate that those three methods can effectively improve the robustness of deep-learning neural networks.
Towards Robust Representations of Limit Orders Books for Deep Learning Models

The success of deep learning-based limit order book forecasting models is highly dependent on the quality and the robustness of the input data representation. A significant body of the quantitative finance literature focuses on utilising different deep learning architectures without taking into consideration the key assumptions these models make with respect to the input data representation. In this paper, we highlight the issues associated with the commonly-used representations of limit order book data from both a theoretical and practical perspectives. We also show the fragility of the representations under adversarial perturbations and propose two simple modifications to the existing representations that match the theoretical assumptions of deep learning models. Finally, we show experimentally how our proposed representations lead to state-of-the-art performance in both accuracy and robustness utilising very simple neural network architectures.
Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image

We present a method that takes as input a single dual-pixel image, and simultaneously estimates the image’s defocus map—the amount of defocus blur at each pixel—and recovers an all-in-focus image. Our method is inspired from recent works that leverage the dual-pixel sensors available in many consumer cameras to assist with autofocus, and use them for recovery of defocus maps or all-in-focus images. These prior works have solved the two recovery problems independently of each other, and often require large labeled datasets for supervised training. By contrast, we show that it is beneficial to treat these two closely-connected problems simultaneously. To this end, we set up an optimization problem that, by carefully modeling the optics of dual-pixel images, jointly solves both problems. We use data captured with a consumer smartphone camera to demonstrate that, after a one-time calibration step, our approach improves upon prior works for both defocus map estimation and blur removal, despite being entirely unsupervised.
First-Order Optimization Inspired from Finite-Time Convergent Flows

In this paper, we investigate the performance of two ﬁrst-order optimization algorithms, obtained from forward Euler discretization of ﬁnite-time optimization ﬂows. These ﬂows are the rescaled-gradient ﬂow (RGF) and the signed-gradient ﬂow (SGF), and consist of non-Lipscthiz or discontinuous dynamical systems that converge locally in ﬁnite time to the minima of gradient-dominated functions. We propose an Euler discretization for these ﬁrst-order ﬁnite-time ﬂows, and provide convergence guarantees, in the deterministic and the stochastic setting. We then apply the proposed algorithms to academic examples, as well as deep neural networks training, where we empirically test their performances on the SVHN dataset. Our results show that our schemes demonstrate faster convergences against standard optimization alternatives.
Rethinking supervised pre-training for better downstream transferring

The pretrain-finetune paradigm has shown outstanding performance on many applications of deep learning, where a model is pre-trained on a upstream large dataset (e.g. ImageNet), and is then fine-tuned to different downstream tasks. Though for most cases, the pre-training stage is conducted based on supervised methods, recent works on self-supervised pre-training have shown powerful transferability and even outperform supervised pre-training on multiple downstream tasks. It thus remains an open question how to better generalize supervised pre-training model to downstream tasks. In this paper, we argue that the worse transferability of existing supervised pre-training methods arise from the negligence of valuable intra-class semantic difference. This is because these methods tend to push images from the same class close to each other despite of the large diversity in their visual contents, a problem to which referred as"overfit of upstream tasks". To alleviate this problem, we propose a new supervised pre-training method based on Leave-One-Out K-Nearest-Neighbor, or LOOK for short. It relieves the problem of overfitting upstream tasks by only requiring each image to share its class label with most of its k nearest neighbors, thus allowing each class to exhibit a multi-mode distribution and consequentially preserving part of intra-class difference for better transferring to downstream tasks. We developed efficient implementation of the proposed method that scales well to large datasets. Experimental studies on multiple downstream tasks show that LOOK outperforms other state-of-the-art methods for supervised and self-supervised pre-training.
A deterministic source of indistinguishable photons in a cluster state

Measurement-based quantum communication relies on the availability of highly entangled multi-photon cluster states. The inbuilt redundancy in the cluster allows communication between remote nodes using repeated local measurements, compensating for photon losses and probabilistic Bell-measurements. For feasible applications, the cluster generation should be fast, deterministic, and its photons indistinguishable. We present a novel source based on a semiconductor quantum-dot device. The dot confines a heavy-hole, precessing in a finely tuned external weak magnetic field while periodically excited by a sequence of optical pulses. Consequently, the dot emits indistinguishable polarization-entangled photons, where the field strength optimizes the entanglement. We demonstrate Gigahertz rate deterministic generation of >90% indistinguishable photons in a cluster state with more than 10 photons characteristic entanglement-length.
ABO: Dataset and Benchmarks for Real-World 3D Object Understanding

We introduce Amazon Berkeley Objects (ABO), a new large-scale dataset designed to help bridge the gap between real and virtual 3D worlds. ABO contains product catalog images, metadata, and artist-created 3D models with com-plex geometries and physically-based materials that cor-respond to real, household objects. We derive challenging benchmarks that exploit the unique properties of ABO and measure the current limits of the state-of-the-art on three open problems for real-world 3D object understanding: single-view 3D reconstruction, material estimation, and cross-domain multi-view object retrieval.
2021 Kolmogorov complexity in quantum mechanics and quantum computing

The Kolmogorov complexity of a quantum state is known as quantum Kolmogorov complexity. We deﬁne the Kolmogorov complexity of a unitary evolution of a quantum state and establish its fundamental relation with quantum Kolmogorov complexity. Thus, the concept of Kolmogorov complexity is naturally relevant for quantum mechanics and quantum computing.
HyperCube: Implicit Field Representations of Voxelized 3D Models

Recently introduced implicit field representations offer an effective way of generating 3D object shapes. They leverage implicit decoder trained to take a 3D point coordinate concatenated with a shape encoding and to output a value which indicates whether the point is outside the shape or not. Although this approach enables efficient rendering of visually plausible objects, it has two significant limitations. First, it is based on a single neural network dedicated for all objects from a training set which results in a cumbersome training procedure and its application in real life. More importantly, the implicit decoder takes only points sampled within voxels (and not the entire voxels) which yields problems at the classification boundaries and results in empty spaces within the rendered mesh. To solve the above limitations, we introduce a new HyperCube architecture based on interval arithmetic network, that enables direct processing of 3D voxels, trained using a hypernetwork paradigm to enforce model convergence. Instead of processing individual 3D samples from within a voxel, our approach allows to input the entire voxel (3D cube) represented with its convex hull coordinates, while the target network constructed by a hypernet assigns it to an inside or outside category. As a result our HyperCube model outperforms the competing approaches both in terms of training and inference efficiency, as well as the final mesh quality.
Photonic Networks-on-Chip Employing Multilevel Signaling: A Cross-Layer Comparative Study

Photonic network-on-chip (PNoC) architectures employ photonic links with dense wavelength-division multiplexing (DWDM) to enable high throughput on-chip transfers. Unfortunately, increasing the DWDM degree (i.e., using a larger number of wavelengths) to achieve a higher aggregated data rate in photonic links and, hence, higher throughput in PNoCs, requires sophisticated and costly laser sources along with extra photonic hardware. This extra hardware can introduce undesired noise to the photonic link and increase the bit error rate (BER), power, and area consumption of PNoCs. To mitigate these issues, the use of 4-pulse amplitude modulation (4-PAM) signaling, instead of the conventional on-off keying (OOK) signaling, can halve the wavelength signals utilized in photonic links for achieving the target aggregate data rate while reducing the overhead of crosstalk noise, BER, and photonic hardware. There are various designs of 4-PAM modulators reported in the literature. For example, the signal superposition (SS)–, electrical digital-to-analog converter (EDAC)–, and optical digital-to-analog converter (ODAC)–based designs of 4-PAM modulators have been reported. However, it is yet to be explored how these SS-, EDAC-, and ODAC-based 4-PAM modulators can be utilized to design DWDM-based photonic links and PNoC architectures. In this article, we provide a systematic analysis of the SS, EDAC, and ODAC types of 4-PAM modulators from prior work with regards to their applicability and utilization overheads. We then present a heuristic-based search method to employ these 4-PAM modulators for designing DWDM-based SS, EDAC, and ODAC types of 4-PAM photonic links with two different design goals: (i) to attain the desired BER of 10-9 at the expense of higher optical power and lower aggregate data rate and (ii) to attain maximum aggregate data rate with the desired BER of 10-9 at the expense of longer packet transfer latency. We then employ our designed 4-PAM SS–, 4-PAM EDAC–, 4-PAM ODAC–, and conventional OOK modulator–based photonic links to constitute corresponding variants of the well-known CLOS and SWIFT PNoC architectures. We eventually compare our designed SS-, EDAC-, and ODAC-based variants of 4-PAM links and PNoCs with the conventional OOK links and PNoCs in terms of performance and energy efficiency in the presence of inter-channel crosstalk. From our link-level and PNoC-level evaluation, we have observed that the 4-PAM EDAC–based variants of photonic links and PNoCs exhibit better performance and energy efficiency compared with the OOK-, 4-PAM SS–, and 4-PAM ODAC–based links and PNoCs.
Phragmites stimulation and natural attenuation of indigenous microbial community accelerated bioremediation of oil contaminated soil

Abstract Illumina high-throughput sequencing method was used to reflect fluctuation of indigenous microbial community during phragmites stimulation process in this study. Results showed Proteobacteria, Fimicutes, and Actinobacteria were most influential in process, Firmicutes and Deinococcus-Thermus were positively affected by nutrient amendment. Firmicutes can be seen as indicator of degradation rate. Dietzia, Salinimicrobium, and Halomonas were the most dominant genus in both Phragmites and Blank setups. These results indicated phragmites addition improved the efficiency of biodegradation and significantly regulated the microbial community structure and functions during remediation process.
Anticancer Attributes of Gold Nanoparticles Synthesized by Novel Isolate Streptomyces tuirus DBZ39

In the present investigation, an attempt was made to explore the anticancer activity of extracellular gold nanoparticles from Streptomyces sp. The synthesized gold nanoparticles were characterized by UV-vis spectrophotometry and scanning electron microscopy. The presence of gold nanoparticles after the synthesis was examined by recording the UV-vis spectra of the solution and also by visual biomonitoring of the solution. The peaks recorded in the UV-vis spectra of the solutions was in between the wavelength of 500 to 600nm which confirms the presence of gold nanoparticles. Scanning electron microscopy reveals the synthesis of gold nanoparticles with an average size 27 to 56nm in the solution. Different concentrations of gold nanoparticles (200 10μl) were subjected to illustrate the anticancer activity using cell lines namely adeno carcinoma (HT 29), kidney cancer (K 293) and breast cancer (MDA 231). The percent inhibition for these cell lines was recorded and observed to be very high at 200μl. The investigation revealing the anticancer activity of gold nanoparticles has been reported for the first time. Gold nanoparticles could be used as cost effective and efficient means of future devices in medical sciences.