Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning

We propose spatial semantic embedding network (SSEN), a simple, yet efficient algorithm for 3D instance segmentation using deep metric learning. The raw 3D reconstruction of an indoor environment suffers from occlusions, noise, and is produced without any meaningful distinction between individual entities. For high-level intelligent tasks from a large scale scene, 3D instance segmentation recognizes individual instances of objects. We approach the instance segmentation by simply learning the correct embedding space that maps individual instances of objects into distinct clusters that reflect both spatial and semantic information. Unlike previous approaches that require complex pre-processing or post-processing, our implementation is compact and fast with competitive performance, maintaining scalability on large scenes with high resolution voxels. We demonstrate the state-of-the-art performance of our algorithm in the ScanNet 3D instance segmentation benchmark on AP score.
The Sample Complexity of Best-k Items Selection from Pairwise Comparisons

This paper studies the sample complexity (aka number of comparisons) bounds for the active best-$k$ items selection from pairwise comparisons. From a given set of items, the learner can make pairwise comparisons on every pair of items, and each comparison returns an independent noisy result about the preferred item. At any time, the learner can adaptively choose a pair of items to compare according to past observations (i.e., active learning). The learner's goal is to find the (approximately) best-$k$ items with a given confidence, while trying to use as few comparisons as possible. In this paper, we study two problems: (i) finding the probably approximately correct (PAC) best-$k$ items and (ii) finding the exact best-$k$ items, both under strong stochastic transitivity and stochastic triangle inequality. For PAC best-$k$ items selection, we first show a lower bound and then propose an algorithm whose sample complexity upper bound matches the lower bound up to a constant factor. For the exact best-$k$ items selection, we first prove a worst-instance lower bound. We then propose two algorithms based on our PAC best items selection algorithms: one works for $k=1$ and is sample complexity optimal up to a loglog factor, and the other works for all values of $k$ and is sample complexity optimal up to a log factor.
Kidney Exchange with Inhomogeneous Edge Existence Uncertainty

Motivated by kidney exchange, we study a stochastic cycle and chain packing problem, where we aim to identify structures in a directed graph to maximize the expectation of matched edge weights. All edges are subject to failure, and the failures can have nonidentical probabilities. To the best of our knowledge, the state-of-the-art approaches are only tractable when failure probabilities are identical. We formulate a relevant non-convex optimization problem and propose a tractable mixed-integer linear programming reformulation to solve it. In addition, we propose a model that integrates both risks and the expected utilities of the matching by incorporating conditional value at risk (CVaR) into the objective function, providing a robust formulation for this problem. Subsequently, we propose a sample-average-approximation (SAA) based approach to solve this problem. We test our approaches on data from the United Network for Organ Sharing (UNOS) and compare against state-of-the-art approaches. Our model provides better performance with the same running time as a leading deterministic approach (PICEF). Our CVaR extensions with an SAA-based method improves the $\alpha \times 100\%$ ($0<\alpha\leqslant 1$) worst-case performance substantially compared to existing models.
In vitro gas production of wheat grain flour coated with different fat types and levels

Gas production (GP) is a rapid method for feedstuffs assessment. A study was done to investigate wheat grain coated with hydrogenated tallow (HT) and hydrogenated palm oil (HP) of different fatty acids types and levels to study total gas production. Approximately, 200 mg (DM basis) of sample was weighed and inserted in glass syringes, mixed with the inoculum and artificial saliva, then incubated at 39°C in a ventilated oven. The outcome of this study showed that experimental fat reduced in vitro degradability using the gas test technique. The addition of experimental fat (HT or HP) to wheat grains significantly decreased GP during incubation (P < 0.01). In comparison to HP fat, coating wheat grains with HT fat resulted in significantly reduced GP (P < 0.01). Accordingly, it seems that experimental fats could be used as a coating substance to reduce the speed of cereal grains (like barley and wheat grains) fermentation in the rumen. Consequently, it could prevent dairy cows from metabolic diseases like acidosis. Moreover, this processing could alter the amount of starch reaching the small intestine to produce more glucose for high producing animals like dairy cows.
Characterization of Errors in Interferometry with Entangled Atoms

Recent progress in generating entangled spin states of neutral atoms provides opportunities to advance quantum sensing technology. In particular, entanglement can enhance the performance of accelerometers and gravimeters based on light-pulse atom interferometry. We study the effects of error sources that may limit the sensitivity of such devices, including errors in the preparation of the initial entangled state, imperfections in the laser pulses, momentum spread of the initial atomic wave packet, measurement errors, spontaneous emission, and atom loss. We determine that, for each of these errors, the expectation value of the parity operator $\Pi$ has the general form, $\overline{\langle \Pi \rangle} = \Pi_0 \cos( N \phi )$, where $\phi$ is the interferometer phase and $N$ is the number of atoms prepared in the maximally entangled Greenberger--Horne--Zeilinger state. Correspondingly, the minimum phase uncertainty has the general form, $\Delta\phi = (\Pi_0 N)^{-1}$. Each error manifests itself through a reduction of the amplitude of the parity oscillations, $\Pi_0$, below the ideal value of $\Pi_0 = 1$. For each of the errors, we derive an analytic result that expresses the dependence of $\Pi_0$ on error parameter(s) and $N$, and also obtain a simplified approximate expression valid when the error is small. Based on the performed analysis, entanglement-enhanced atom interferometry appears to be feasible with existing experimental capabilities.
Lichen communities in two old-growth pine (Pinus) forests

Abstract Many lichen species have specific environmental requirements for colonization. Old-growth forests contain microhabitats required by a particular suite of lichens. In Ontario, Canada, old-growth forests are increasingly uncommon and the lichen communities within some of these forests are not well known. To better understand the lichen communities that inhabit old-growth forests in the province, we examined the lichen biota on coarse woody debris (CWD) and trees in a red pine (Pinus resinosa) and a white pine (Pinus strobus) dominated stand in northern Ontario. Lichen diversity was assessed on different forms of CWD and trees in each forest. Lichen diversity did not differ significantly between CWD types in the red pine forest, but was significantly different in the white pine forest. There was no significant difference in lichen diversity amongst different decay stages of CWD in either forest. In both forests, lichen communities on stumps, logs, and snags differed from lichen communities present on trees. A variety of CWD types is important for overall lichen species richness in the red pine forest. Our results demonstrate to land managers that different types of old-growth forests are ecologically unique, even those dominated by tree species in the same genus. Management of an old-growth forest should suit its individual ecology.
Inverse-Compton scattering in the resolved jet of the high-redshift quasar PKS J1421−0643

Despite the fact that kpc-scale inverse-Compton (iC) scattering of cosmic microwave background (CMB) photons into the X-ray band is mandated, proof of detection in resolved quasar jets is often insecure. High redshift provides favourable conditions due to the increased energy density of the CMB, and it allows constraints to be placed on the radio synchrotron-emitting electron component at high energies that are otherwise inaccessible. We present new X-ray, optical and radio results from Chandra, HST and the VLA for the core and resolved jet in the $z=3.69$ quasar PKS J1421-0643. The X-ray jet extends for about $4.5''$ (32 kpc projected length). The jet's radio spectrum is abnormally steep and consistent with electrons being accelerated to a maximum Lorentz factor of about 5000. Results argue in favour of the detection of inverse-Compton X-rays for modest magnetic field strength of a few nT, Doppler factor of about 4, and viewing angle of about $15^\circ$, and predict the jet to be largely invisible in most other spectral bands including the far- and mid-infrared and high-energy gamma-ray. The jet power is estimated to be about $3 \times 10^{46}$ erg s$^{-1}$ which is of order a tenth of the quasar bolometric power, for an electron--positron jet. The jet radiative power is only about 0.07 per cent of the jet power, with a smaller radiated power ratio if the jet contains heavy particles, so most of the jet power is available for heating the intergalactic medium.
Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications

In this paper, we deepen the analysis of continuous time Fictitious Play learning algorithm to the consideration of various finite state Mean Field Game settings (finite horizon, $\gamma$-discounted), allowing in particular for the introduction of an additional common noise. We first present a theoretical convergence analysis of the continuous time Fictitious Play process and prove that the induced exploitability decreases at a rate $O(\frac{1}{t})$. Such analysis emphasizes the use of exploitability as a relevant metric for evaluating the convergence towards a Nash equilibrium in the context of Mean Field Games. These theoretical contributions are supported by numerical experiments provided in either model-based or model-free settings. We provide hereby for the first time converging learning dynamics for Mean Field Games in the presence of common noise.
X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech

Many articles have used voice analysis to detect Parkinson's disease (PD), but few have focused on the early stages of the disease and the gender effect. In this article, we have adapted the latest speaker recognition system, called x-vectors, in order to detect PD at an early stage using voice analysis. X-vectors are embeddings extracted from Deep Neural Networks (DNNs), which provide robust speaker representations and improve speaker recognition when large amounts of training data are used. Our goal was to assess whether, in the context of early PD detection, this technique would outperform the more standard classifier MFCC-GMM (Mel-Frequency Cepstral Coefficients—Gaussian Mixture Model) and, if so, under which conditions. We recorded 221 French speakers (recently diagnosed PD subjects and healthy controls) with a high-quality microphone and via the telephone network. Men and women were analyzed separately in order to have more precise models and to assess a possible gender effect. Several experimental and methodological aspects were tested in order to analyze their impacts on classification performance. We assessed the impact of the audio segment durations, data augmentation, type of dataset used for the neural network training, kind of speech tasks, and back-end analyses. X-vectors technique provided better classification performances than MFCC-GMM for the text-independent tasks, and seemed to be particularly suited for the early detection of PD in women (7–15% improvement). This result was observed for both recording types (high-quality microphone and telephone).
Self-Organized Critical Coexistence Phase in Repulsive Active Particles.

We revisit motility-induced phase separation in two models of active particles interacting by pairwise repulsion and uncover new qualitative features: the resulting dense phase contains gas bubbles distributed algebraically up to a typically extremely large cutoff scale. At large enough system size and/or global density, all the gas may be contained inside the bubbles, at which point the system is microphase separated with a finite cutoff bubble scale. We further observe that the ordering is clearly anomalous, with different dynamics for the coarsening of the dense phase and of the gas bubbles. This self-organized critical phenomenology is reproduced by a "reduced bubble model" that implements the basic idea of reverse Ostwald ripening put forward in Tjhung et al. [Phys. Rev. X 8, 031080 (2018)PRXHAE2160-330810.1103/PhysRevX.8. 031080].
RAPTOR: Robust and Perception-Aware Trajectory Replanning for Quadrotor Fast Flight

Recent advances in trajectory replanning have enabled quadrotor to navigate autonomously in unknown environments. However, high-speed navigation still remains a significant challenge. Given very limited time, existing methods have no strong guarantee on the feasibility or quality of the solutions. Moreover, most methods do not consider environment perception, which is the key bottleneck to fast flight. In this article, we present RAPTOR, a robust and perception-aware replanning framework to support fast and safe flight, which addresses these issues systematically. A path-guided optimization approach that incorporates multiple topological paths is devised, to ensure finding feasible and high-quality trajectories in very limited time. We also introduce two perception-aware planning approaches to actively observe and avoid unknown obstacles. A risk-aware trajectory refinement ensures that unknown obstacles which may endanger the quadrotor can be observed earlier and avoid in time. The motion of yaw angle is planned to actively explore the surrounding space that is relevant for safe navigation. The proposed methods are tested extensively through benchmark comparisons and challenging indoor and outdoor aggressive flights. We release our implementation as an open-source package1 for the community.
Molecular dissection of ionotropic glutamate receptor delta-family interactions with trans-synaptic proteins

Correct functioning of the brain relies upon the precise connectivity between the billions of neurons that make up this crucial organ. Aberrations in the formation of these elaborate neural networks lead to neurodegenerative and neuropsychiatric disorders. A synapse-spanning molecular triad, involving members of the Neurexin, Cbln and ionotropic glutamate receptor delta families of proteins, is crucial for the accurate formation and proper function of synapses in the cerebellum. This trans-synaptic complex has been implicated in the molecular mechanisms behind motor control and motor learning, and furthermore individual members have been linked to diseases such as Alzheimer’s, autism spectrum disorders and schizophrenia. The major findings presented in this thesis include: crystal structures of the amino-terminal domains (ATD) of the two members of the ionotropic glutamate receptor delta (iGluR-Delta) family, functional characterisation of the effects of disrupting the ATD interface in one member of the iGluR-Delta family, a crystal structure of the C1q domain of Cbln1, biophysical analysis of the molecular interactions within the Neurexin-Cbln1-GluD2 trans-synaptic complex, as well as evidence for the domain arrangement of the ecto-domain of the iGluR-Delta proteins. Together, these data enhance our knowledge of the molecular details of this macro-molecular complex and provide evidence to support models for the mechanisms of their involvement in synapse formation and function, thereby making a contribution to the vast and medically relevant field of molecular neurobiology.
On the heat kernel and semiclassical $p$-branes in hyperbolic space

The one-loop effects to the Dirac action of $p$-branes in a hyperbolic background from the path integral and the solution of the Wheeler-DeWitt equation are analysed. The objective of comparing the equivalent quantization procedures is to study in detail the validity of the semiclassical approximation and divergences associated to one-loop corrections. This is in line with a bottom-up approach to holographic Wilson loops. We employ the heat kernel regularization method for both quantization procedures and we study in great detail one-loop corrections to geodesics in a 2-dimensional hyperbolic space and semi-spheres in a 3-dimensional hyperbolic space. We show that the divergences, given by the high energy expansion of the heat kernel, can be classified by their compatibility with the semiclassical approximation and geometric nature.
Monitoring Browsing Behavior of Customers in Retail Stores via RFID Imaging

In this paper, we propose to use commercial off-the-shelf (COTS) <italic>monostatic</italic> RFID devices (i.e. which use a single antenna at a time for both transmitting and receiving RFID signals to and from the tags) to monitor browsing activity of customers in front of display items in places such as retail stores. To this end, we propose <italic>TagSee</italic>, a multi-person imaging system based on monostatic RFID imaging. TagSee is based on the insight that when customers are browsing the items on a shelf, they stand between the tags deployed along the boundaries of the shelf and the reader, which changes the multi-paths that the RFID signals travel along, and both the RSS and phase values of the RFID signals that the reader receives change. Based on these variations observed by the reader, TagSee constructs a coarse grained image of the customers. Afterwards, TagSee identifies the items that are being browsed by the customers by analyzing the constructed images. The key novelty of this paper is on achieving browsing behavior monitoring of multiple customers in front of display items by constructing coarse grained images via robust, analytical model-driven deep learning based, RFID imaging. To achieve this, we first mathematically formulate the problem of imaging humans using monostatic RFID devices and derive an approximate analytical imaging model that correlates the variations caused by human obstructions in the RFID signals. Based on this model, we then develop a deep learning framework to robustly image customers with high accuracy. We implement TagSee scheme using a Impinj Speedway R420 reader and SMARTRAC DogBone RFID tags. TagSee can achieve a TPR of more than <inline-formula><tex-math notation="LaTeX">${\sim }90\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>∼</mml:mo><mml:mn>90</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="ali-ieq1-3019652.gif"/></alternatives></inline-formula> and a FPR of less than <inline-formula><tex-math notation="LaTeX">${\sim }10\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>∼</mml:mo><mml:mn>10</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="ali-ieq2-3019652.gif"/></alternatives></inline-formula> in multi-person scenarios using training data from just 3-4 users.
Chitosan/calcium–alginate beads for oral delivery of insulin

A mild chitosan/calcium alginate microencapsulation process, as applied to encapsulation of biological macromolecules such as albumin and insulin, was investigated. The microcapsules were derived by adding dropwise a protein-containing sodium alginate mixture into a chitosan–CaCl2 system. The beads containing a high concentration of entrapped bovine serum albumin (BSA) as more than 70% of the initial concentration were achieved via varying chitosan coat. It was observed that approximately 70% of the content is being released into Tris-HCl buffer, pH 7.4 within 24 h and no significant release of BSA was observed during treatment with 0.1M HCl pH 1.2 for 4 h. But the acid-treated beads had released almost all the entrapped protein into Tris-HCl pH 7.4 media within 24 h. Instead of BSA, the insulin preload was found to be very low in the chitosan/calcium alginate system; the release characteristics were similar to that of BSA. From scanning electron microscopic studies, it appears that the chitosan modifies the alginate microspheres and subsequently the protein loading. The results indicate the possibility of modifying the formulation in order to obtain the desired controlled release of bioactive peptides (insulin), for a convenient gastrointestinal tract delivery system. © 1996 John Wiley & Sons, Inc.
Genetic correlations and heritabilities for frequency of cracked eggs, egg number and egg weight in laying hens

Abstract 1. Data on egg weight and egg number were obtained from two consecutive generations in a White Leghorn line. Each egg was candled and the frequency of cracked eggs was calculated. 2. The estimates of heritability for the frequency of cracked eggs ranged from 0.11 to 0.43, for egg number from 0.12 to 0.29 and for egg weight from 0.58 to 0.65. 3. The estimates of the genetic correlations between the traits were variable but in general showed no evidence of antagonism between a reduction in crack frequency and an increase in the production traits. 4. The efficiency of selection for reduction in the frequency of cracked eggs based on early measurements is briefly discussed.
RGCF: Refined Graph Convolution Collaborative Filtering with concise and expressive embedding

Graph Convolution Networks (GCNs) have attracted significant attention and have become the most popular method for learning graph representations. In recent years, many efforts have focused on integrating GCNs into recommender tasks and have made remarkable progress. At its core is to explicitly capture the high-order connectivities between nodes in the user-item bipartite graph. However, we found some potential drawbacks existed in the traditional GCN-based recommendation models are that the excessive information redundancy yield by the nonlinear graph convolution operation reduces the expressiveness of the resultant embeddings, and the important popularity features that are effective in sparse recommendation scenarios are not encoded in the embedding generation process. In this work, we develop a novel GCN-based recommendation model, named Refined Graph convolution Collaborative Filtering (RGCF), where a refined graph convolution structure is designed to match non-semantic ID inputs. In addition, a new fine-tuned symmetric normalization is proposed to mine node popularity characteristics and further incorporate the popularity features into the embedding learning process. Extensive experiments were conducted on three public million-size datasets, and the RGCF improved by an average of 13.45% over the state-of-the-art baseline. Further comparative experiments validated the effectiveness and rationality of each part of our proposed RGCF. We released our code at https://github.com/hfutmars/RGCF.
Divide-and-Shuffle Synchronization for Distributed Machine Learning

Distributed Machine Learning suffers from the bottleneck of synchronization to all-reduce workers' updates. Previous works mainly consider better network topology, gradient compression, or stale updates to speed up communication and relieve the bottleneck. However, all these works ignore the importance of reducing the scale of synchronized elements and inevitable serial executed operators. To address the problem, our work proposes the Divide-and-Shuffle Synchronization(DS-Sync), which divides workers into several parallel groups and shuffles group members. DS-Sync only synchronizes the workers in the same group so that the scale of a group is much smaller. The shuffle of workers maintains the algorithm's convergence speed, which is interpreted in theory. Comprehensive experiments also show the significant improvements in the latest and popular models like Bert, WideResnet, and DeepFM on challenging datasets.