Miracle-dz/UniFAR-FTUs huggingface dataset:
Generative Model-Based Loss to the Rescue: A Method to Overcome Annotation Errors for Depth-Based Hand Pose Estimation

We propose to use a model-based generative loss for training hand pose estimators on depth images based on a volumetric hand model. This additional loss allows training of a hand pose estimator that accurately infers the entire set of 21 hand keypoints while only using supervision for 6 easy-to-annotate keypoints (fingertips and wrist). We show that our partially-supervised method achieves results that are comparable to those of fully-supervised methods which enforce articulation consistency. Moreover, for the first time we demonstrate that such an approach can be used to train on datasets that have erroneous annotations, i.e. “ground truth” with notable measurement errors, while obtaining predictions that explain the depth images better than the given “ground truth. ”
Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Deep learning (DL) can achieve impressive results across a wide variety of tasks, but this often comes at the cost of training models for extensive periods on specialized hardware accelerators. This energy-intensive workload has seen immense growth in recent years. Machine learning (ML) may become a significant contributor to climate change if this exponential trend continues. If practitioners are aware of their energy and carbon footprint, then they may actively take steps to reduce it whenever possible. In this work, we present Carbontracker, a tool for tracking and predicting the energy and carbon footprint of training DL models. We propose that energy and carbon footprint of model development and training is reported alongside performance metrics using tools like Carbontracker. We hope this will promote responsible computing in ML and encourage research into energy-efficient deep neural networks.
SpinalNet: Deep Neural Network With Gradual Input

Deep neural networks (DNNs) have achieved the state-of-the-art (SOTA) performance in numerous fields. However, DNNs need high computation times, and people always expect better performance in a lower computation. Therefore, we study the human somatosensory system and design a neural network (SpinalNet) to achieve higher accuracy with fewer computations. Hidden layers (HLs) in traditional NNs receive inputs in the previous layer, apply activation function, and then transfer the outcomes to the next layer. In the proposed SpinalNet, each layer is split into three splits: 1) input split, 2) intermediate split, and 3) output split. Input split of each layer receives a part of the inputs. The intermediate split of each layer receives outputs of the intermediate split of the previous layer and outputs of the input split of the current layer. The number of incoming weights becomes significantly lower than traditional DNNs. The SpinalNet can also be used as the fully connected or classification layer of DNN and supports both traditional learning and transfer learning. We observe significant error reductions with lower computational costs in most of the DNNs. Traditional learning on the VGG-5 network with SpinalNet classification layers provided the SOTA performance on QMNIST, Kuzushiji-MNIST, and EMNIST (Letters, Digits, and Balanced) datasets. Traditional learning with ImageNet pretrained initial weights and SpinalNet classification layers provided the SOTA performance on STL-10, Fruits 360, Bird225, and Caltech-101 datasets. The scripts of the proposed SpinalNet training are available at the following link: https://github.com/dipuk0506/SpinalNet.
Hierarchical nucleation in deep neural networks

Deep convolutional networks (DCNs) learn meaningful representations where data that share the same abstract characteristics are positioned closer and closer. Understanding these representations and how they are generated is of unquestioned practical and theoretical interest. In this work we study the evolution of the probability density of the ImageNet dataset across the hidden layers in some state-of-the-art DCNs. We find that the initial layers generate a unimodal probability density getting rid of any structure irrelevant for classification. In subsequent layers density peaks arise in a hierarchical fashion that mirrors the semantic hierarchy of the concepts. Density peaks corresponding to single categories appear only close to the output and via a very sharp transition which resembles the nucleation process of a heterogeneous liquid. This process leaves a footprint in the probability density of the output layer where the topography of the peaks allows reconstructing the semantic relationships of the categories.
The Statistical Relationship between White-light Emission and Photospheric Magnetic Field Changes in Flares

Continuum emission, also called white-light emission (WLE), and permanent changes of the magnetic field (ΔBLOS) are often observed during solar flares. However, their relation and precise mechanisms are still unknown. We study statistically the relationship between ΔBLOS and WLE during 75 solar flares of different strengths and locations on the solar disk. We analyze SDO/HMI data and determine for each pixel in each flare if it exhibited WLE and/or ΔBLOS. We then investigate the occurrence, strength, and spatial size of the WLE, its dependence on flare energy, and its correlation to the occurrence of ΔBLOS. We detected WLE in 44/75 flares and ΔBLOS in 59/75 flares. We find that WLE and ΔBLOS are related, and their locations often overlap between 0% and 60%. Not all locations coincide, thus potentially indicating differences in their origin. We find that the WL area is related to the flare class by a power law, and extend the findings of previous studies, that the WLE is related to the flare class by a power law, to also be valid for C-class flares. To compare unresolved (Sun-as-a-star) WL measurements with our data, we derive a method to calculate temperatures and areas of such data under the blackbody assumption. The calculated unresolved WLE areas improve, but still differ to the resolved flaring area by about a factor of 5–10 (previously 10–20), which could be explained by various physical or instrumental causes. This method could also be applied to stellar flares to determine their temperatures and areas independently.
Abundance and reproduction of Nilsson's pipefish on tidal flats

Nilsson's pipefish Syngnathus rostellatus were found on tidal flats in the Dollard (Ems estuary, Wadden Sea) in high numbers in May and June (20 to 150 1000 m -2 ) and August and September (150 to 300 1000m -2 ). Low numbers were found in April, July, November and December. Nilsson's pipefish with eggs and larvae in their brood pouch were most numerous in May and August and September. Both the fraction of Nilsson's pipefish with a brood pouch and the fraction of brood pouches with eggs and larvae increased with fish length. Analysis of length-frequency distributions suggests that two cohorts live in the Dollard (spring cohort, April-August; summer cohort, July-December). Reproducing animals were found in both cohorts. It was assumed that the spring cohort was the previous year's summer cohort, which migrate to the tidal flats in spring, reproduced and then disappeared. The summer cohort was their offspring (0 group) which leave the tidal flats during November and December.
Hierarchical and Unsupervised Graph Representation Learning with Loukas's Coarsening

We propose a novel algorithm for unsupervised graph representation learning with attributed graphs. It combines three advantages addressing some current limitations of the literature: (i) The model is inductive: it can embed new graphs without re-training in the presence of new data; (ii) The method takes into account both micro-structures and macro-structures by looking at the attributed graphs at different scales; (iii) The model is end-to-end differentiable: it is a building block that can be plugged into deep learning pipelines and allows for back-propagation. We show that combining a coarsening method having strong theoretical guarantees with mutual information maximization suffices to produce high quality embeddings. We evaluate them on classification tasks with common benchmarks of the literature. We show that our algorithm is competitive with state of the art among unsupervised graph representation learning methods.
Morphological and molecular analysis of ‘Melania’ jacqueti Dautzenberg and Fischer, 1906: from anonymous orphan to critical basal offshoot of the Semisulcospiridae (Gastropoda: Cerithioidea)

Morphological and molecular studies have been inconclusive in assessing monophyly of the Pleuroceridae s.l and the affinity of western North American Juga to Asian members of the family currently grouped in the Semisulcospirinae. In part, this has been hampered by the rarity of anatomical accounts for Asian pleurocerids (s.l.). The present study provides a comprehensive anatomical description of ‘Melania’jacqueti— a Vietnamese species of uncertain generic placement forgotten in the scientific literature for over 100 years. This investigation confirmed that ‘Melania’jacqueti and Juga possess a number of features that differentiate them from eastern North American species, including features of the kidney, prostate and pallial oviduct. However, comparative data remain inadequate to allow phylogenetic analysis based on morphological data alone. Consequently, a data set of partial mitochondrial 16S ribosomal RNA gene sequences for 51 pleurocerids (s.l.) and five outgroups (Melanopsidae, Thiaridae), was assembled. Parsimony, Bayesian and Maximum Likelihood analyses are largely congruent and support monophyly of the Pleuroceridae s.l. , and of two large clades: (i) a clade of eastern North American species, and (ii) an Asian–American clade with ‘Melania’jacqueti as the critical basal offshoot, decisively anchoring Juga within the Semisulcospirinae as sister to all remaining Asian forms. Given the morphological disparity between these two clades, this result is consistent with restriction of the concept of Pleuroceridae s.s. to eastern North American species, and elevation of the Asian–American clade to the rank of family — the Semisulcospiridae. Examination of available genus‐group names for Oriental semisulcospirids supports placement of ‘Melania’jacqueti in the genus Hua. Molecular calibration of the basal split between Semisulcospiridae and Pleuroceridae suggests they diverged in the Cretaceous (approximately 90 million years ago). The origin and diversification of these clades are roughly coincident with the subdivision of Laurasia by epicontinental seas and the opening of the Atlantic basin, consistent with a primarily vicariant explanation for their modern biogeographical distributions modified by dispersal in Juga.
Lossless CNN Channel Pruning via Gradient Resetting and Convolutional Re-parameterization

Channel pruning (a.k.a. filter pruning) aims to slim down a convolutional neural network (CNN) by reducing the width (i.e., numbers of output channels) of convolutional layers. However, as CNN's representational capacity depends on the width, doing so tends to degrade the performance. A traditional learning-based channel pruning paradigm applies a penalty on parameters to improve the robustness to pruning, but such a penalty may degrade the performance even before pruning. Inspired by the neurobiology research about the independence of remembering and forgetting, we propose to re-parameterize a CNN into the remembering parts and forgetting parts, where the former learn to maintain the performance and the latter learn for efficiency. By training the re-parameterized model using regular SGD on the former but a novel update rule with penalty gradients on the latter, we achieve structured sparsity, enabling us to equivalently convert the re-parameterized model into the original architecture with narrower layers. With our method, we can slim down a standard ResNet-50 with 76.15\% top-1 accuracy on ImageNet to a narrower one with only 43.9\% FLOPs and no accuracy drop. Code and models are released at this https URL.
An Entropy Equation for Energy

This paper describes an entropy equation, but one that should be used for measuring energy and not information. In relation to the human brain therefore, both of these quantities can be used to represent the stored information. The human brain makes use of energy efficiency to form its structures, which is likely to be linked to the neuron wiring. This energy efficiency can also be used as the basis for a clustering algorithm, which is described in a different paper. This paper is more of a discussion about global properties, where the rules used for the clustering algorithm can also create the entropy equation E = (mean * variance). This states that work is done through the energy released by the 'change' in entropy. The equation is so simplistic and generic that it can offer arguments for completely different domains, where the journey ends with a discussion about global energy properties in physics and beyond. A comparison with Einstein's relativity equation is made and also the audacious suggestion that a black hole has zero-energy inside.
Sets with large density at infinity contain all large copies of finite sets

We prove that if $E \subseteq \mathbb{R}^d$ ($d\geq 2$) is a Lebesgue measurable set with density larger than $\frac{n-2}{n-1}$ then $E$ contains similar copies of every $n$-point set $P$ at all sufficiently large scales. Moreover `sufficiently large' can be taken to be uniform in the `size' of the pattern $P$. We also prove that if the set has positive density, then it contains all sufficiently large copies of all finite chains.
Imprint of anisotropic primordial non-Gaussianity on halo intrinsic alignments in simulations

Using $N$-body simulations for the first time, we show that the anisotropic primordial non-Gaussianity (PNG) causes a scale-dependent modification, given by $1/k^2$ at small $k$ limit, in the three-dimensional power spectra of halo shapes (intrinsic alignments), whilst the conventional power spectrum of halo number density field remains unaffected. We discuss that wide-area imaging and spectroscopic surveys observing the same region of the sky allow us to constrain the quadrupole PNG coefficient $f_{\rm NL}^{s=2}$ at a precision comparable with that of the cosmic microwave background.
Solvable two-particle systems with time-asymmetric interactions in de Sitter space

The two-particle models in de Sitter space-time with time-asymmetric retarded-advanced interactions are constructed. Particular cases of the field-type electromagnetic and scalar interactions are considered. The manifestly covariant descriptions of the models within the Lagrangian and Hamiltonian formalisms with constraints are proposed. It is shown that the models are de Sitter-invariant and integrable. An explicit solution of equations of motion is derived in quadratures by means of projection operator technique.
GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction

Advertising and feed ranking are essential to many Internet companies such as Facebook. Among many real-world advertising and feed ranking systems, click through rate (CTR) prediction plays a central role. In recent years, many neural network based CTR models have been proposed and achieved success such as Factorization-Machine Supported Neural Networks, DeepFM and xDeepFM. Many of them contain two commonly used components: embedding layer and MLP hidden layers. On the other side, gating mechanism is also widely applied in many research fields such as computer vision(CV) and natural language processing(NLP). Some research has proved that gating mechanism improves the trainability of non-convex deep neural networks. Inspired by these observations, we propose a novel model named GateNet which introduces either the feature embedding gate or the hidden gate to the embedding layer or hidden layers of DNN CTR models, respectively. The feature embedding gate provides a learnable feature gating module to select salient latent information from the feature-level. The hidden gate helps the model to implicitly capture the high-order interaction more effectively. Extensive experiments conducted on three real-world datasets demonstrate its effectiveness to boost the performance of various state-of-the-art models such as FM, DeepFM and xDeepFM on all datasets.
Growth, Development, and Response of Peach Somaclones to the Root-knot Nematode, Meloidogyne incognita

Somaclonal variation has been reported in many plant species, and several phenotypic and genetic changes, including pathogen and pest resistance, have been described. This study was designed to evaluate somaclonal variation in peach (Prunus persica (L.) Batsch) regenerants in response to the root-knot nematode, Meloidogyne incognita (Kofoid & White) Chitwood. Regenerants SH-156-1, SH-156-7, SH-156-11, and SH-156-12, derived from 'Sunhigh' (susceptible) embryo no. 156, and regenerants RH-30-1, RH-30-2, RH-30-4, RH-30-6, RH-30-7, and RH-30-8, derived from 'Redhaven' (moderately resistant) embryo no. 30, were screened in vitro for resistance to the root-knot nematode. Under in vitro conditions, fewest nematodes developed on regenerants SH-156-1 and SH-156-11, 'Redhaven', and all 'Redhaven' embryo no. 30 regenerants. The most nematodes developed on 'Sunhigh', 'Sunhigh' seedlings (SHS), and regenerant SH-156-7. Nematodes did not develop on 'Nemaguard'. In greenhouse tests, fewer nematodes developed and reproduced on the no. 156-series regenerants than on 'Sunhigh'. Under in vitro conditions, significant differences among uninfected (control) regenerants, cultivars, and rootstock 'Nemaguard' were observed for shoot height and fresh root weights. Significant differences were also observed among infected regenerants, cultivars, and 'Nemaguard' for these characteristics, but differences were not observed between control and infected regenerants. Different concentrations of α- naphthaleneacetic acid in half-strength Murashige and Skoog salt medium induced rooting of two peach cultivars, one rootstock, and four regenerants.
The Transfer of Typhlodromus pyri on Grape Leaves for Biological Control of Panonychus ulmi (Acari: Phytoseiidae, Tetranychidae) in Vineyards in Ontario, Canada

The phytophagous mite Panonychus ulmi Koch has become a significant problem in Ontario vineyards. We attempted to introduce and establish populations of the predatory mite Typhlodromus pyri Scheuten for P. ulmi biological control. Grape leaves were transferred from a vineyard containing T. pyri in early summer 1998, by picking leaves from a donor vineyard and attaching them onto leaves in the release vineyard where T. pyri were extremely rare. Two release treatments were used with rates of 8.5 (13) and 25.5 (33) mobiles per vine. In the first season, T. pyri established in similar densities in both release treatments, which were significantly higher than controls. However, there were no differences among treatments in P. ulmi densities in 1998 as a result of predator release. During summer 1999, significantly fewer P. ulmi mite-days were observed in release plots compared to the control. Amblyseius fallacis (Garman) was common throughout the release vineyard in 1998 and in 1999, but appeared on the vines too late in the season to maintain low P. ulmi densities. T. pyri appeared to out-compete A. fallacis in 1999 because A. fallacis densities were significantly lower in plots where T. pyri had been released than in control plots. We conclude that T. pyri can be effective for P. ulmi biological control in Ontario vineyards and may be introduced by transferring leaves. In Europe, transferring prunings has been the standard method of inoculating T. pyri into new vineyards. Here we show that transferring leaves is another practical method. © 2001 Academic Press
Optimal Witnessing of Healthcare IoT Data Using Blockchain Logging Contract

Verification of data generated by wearable sensors is increasingly becoming of concern to health service providers and insurance companies. These devices are typically vulnerable to a wide range of cybersecurity attacks, attempting to manipulate sensing data. Most of these disastrous attacks would remain undetected since neither healthcare servers nor Internet-of-Things (IoT) sensors are aware of the existence of attackers in the middle of communication. Thus, there is a need for a verification framework that various authorities can request a verification service for the local network data of a target IoT device. In this article, we leverage blockchain as a distributed platform to realize an on-demand verification scheme. This allows authorities to automatically transact with connected devices for witnessing services. A public request is made for witness statements on the data of a target IoT that is transmitted on its local network, and subsequently, devices (in close vicinity of the target IoT) offer witnessing service. Our contributions are threefold: 1) we develop a system architecture based on blockchain and smart contract that enables authorities to dynamically avail a verification service for data of a subject device from a distributed set of witnesses which are willing to provide (in a privacy-preserving manner) their local wireless measurement in exchange of monetary return; 2) we then develop a method to optimally select witnesses in such a way that the verification error is minimized subject to monetary cost constraints; and 3) finally, we evaluate the efficacy of our scheme using real Wi-Fi session traces collected from a five-storeyed building with more than thirty access points, representative of a hospital. According to the current pricing schedule of the Ethereum public blockchain, our scheme enables healthcare authorities to verify data transmitted from a typical wearable device with the verification error of the order 0.01% at cost of less than $ \$ $ 2 for 1-hr witnessing service.
An Integer Programming Approach to Deep Neural Networks with Binary Activation Functions

We study deep neural networks with binary activation functions (BDNN), i.e. the activation function only has two states. We show that the BDNN can be reformulated as a mixed-integer linear program which can be solved to global optimality by classical integer programming solvers. Additionally, a heuristic solution algorithm is presented and we study the model under data uncertainty, applying a two-stage robust optimization approach. We implemented our methods on random and real datasets and show that the heuristic version of the BDNN outperforms classical deep neural networks on the Breast Cancer Wisconsin dataset while performing worse on random data.
RGBT Salient Object Detection: A Large-Scale Dataset and Benchmark

Salient object detection (SOD) in complex scenes and environments is a challenging research topic. Most works focus on RGB-based SOD, which limits its performance of real-life applications when confronted with adverse conditions such as dark environments and complex backgrounds. Since thermal infrared spectrum provides the complementary information, RGBT SOD has become a new research direction. However, current research for RGBT SOD is limited by the lack of a large-scale dataset and comprehensive benchmark. This work contributes such a RGBT image dataset named VT5000, including 5000 spatially aligned RGBT image pairs with ground truth annotations. VT5000 has 11 challenges collected in different scenes and environments for exploring the robustness of algorithms. With this dataset, we propose a powerful baseline approach, which extracts multilevel features of each modality and aggregates these features of all modalities with the attention mechanism for accurate RGBT SOD. To further solve the problem of blur boundaries of salient objects, we also use an edge loss to refine the boundaries. Extensive experiments show that the proposed baseline approach outperforms the state-of-the-art methods on VT5000 dataset and other two public datasets. In addition, we carry out a comprehensive analysis of different algorithms of RGBT SOD on VT5000 dataset, and then make several valuable conclusions and provide some potential research directions for RGBT SOD.