Miracle-dz/UniFAR-FTUs huggingface dataset:
An End-to-End Geometric Deficiency Elimination Algorithm for 3D Meshes

The 3D mesh is an important representation of geometric data. In the generation of mesh data, geometric deficiencies (e.g., duplicate elements, degenerate faces, isolated vertices, self-intersection, and inner faces) are unavoidable and may violate the topology structure of an object. In this paper, we propose an effective and efficient geometric deficiency elimination algorithm for 3D meshes. Specifically, duplicate elements can be eliminated by assessing the occurrence times of vertices or faces; degenerate faces can be removed according to the outer product of two edges; since isolated vertices do not appear in any face vertices, they can be deleted directly; self-intersecting faces are detected using an AABB tree and remeshed afterward; by simulating whether multiple random rays that shoot from a face can reach infinity, we can judge whether the surface is an inner face, then decide to delete it or not. Experiments on ModelNet40 dataset illustrate that our method can eliminate the deficiencies of the 3D mesh thoroughly.
Dash

Byte-addressable persistent memory (PM) brings hash tables the potential of low latency, cheap persistence and instant recovery. The recent advent of Intel Optane DC Persistent Memory Modules (DCPMM) further accelerates this trend. Many new hash table designs have been proposed, but most of them were based on emulation and perform sub-optimally on real PM. They were also piece-wise and partial solutions that side-step many important properties, in particular good scalability, high load factor and instant recovery. We present Dash, a holistic approach to building dynamic and scalable hash tables on real PM hardware with all the aforementioned properties. Based on Dash, we adapted two popular dynamic hashing schemes (extendible hashing and linear hashing). On a 24-core machine with Intel Optane DCPMM, we show that compared to state-of-the-art, Dash-enabled hash tables can achieve up to ∼3.9× higher performance with up to over 90% load factor and an instant recovery time of 57ms regardless of data size.
An imaging line survey of OMC-1 to OMC-3

Context. Recently, sensitive wide-bandwidth receivers in the millimetre regime have enabled us to combine large spatial and spectral coverage for observations of molecular clouds. The resulting capability to map the distributions of lines from many molecules simultaneously yields unbiased coverage of the various environments within star-forming regions. Aims. Our aim is to identify the dominant molecular cooling lines and characteristic emission features in the 1.3 mm window of distinct regions in the northern part of the Orion A molecular cloud. By defining and analysing template regions, we also intend to help with the interpretation of observations from more distant sources which cannot be easily spatially resolved. Methods. We analyse an imaging line survey covering the area of OMC-1 to OMC-3 from 200.2 to 281.8 GHz obtained with the PI230 receiver at the APEX telescope. Masks are used to define regions with distinct properties (e.g. column density or temperature ranges) from which we obtain averaged spectra. Lines of 29 molecular species (55 isotopologues) are fitted for each region to obtain the respective total intensity. Results. We find that strong sources like Orion KL have a clear impact on the emission on larger scales. Although not spatially extended, their line emission contributes substantially to spectra averaged over large regions. Conversely, the emission signatures of dense, cold regions like OMC-2 and OMC-3 (e.g. enhanced N2H+ emission and low HCN/HNC ratio) seem to be difficult to pick up on larger scales, where they are eclipsed by signatures of stronger sources. In all regions, HCO+ appears to contribute between 3 and 6% to the total intensity, the most stable value for all bright species. N2H+ shows the strongest correlation with column density, but not with typical high-density tracers like HCN, HCO+, H2CO, or HNC. Common line ratios associated with UV illumination, CN/HNC and CN/HCO+, show ambiguous results on larger scales, suggesting that the identification of UV illuminated material may be more challenging. The HCN/HNC ratio may be related to temperature over varying scales.
Key Phrase Classification in Complex Assignments

Complex assignments typically consist of open-ended questions with large and diverse content in the context of both classroom and online graduate programs. With the sheer scale of these programs comes a variety of problems in peer and expert feedback, including rogue reviews. As such with the hope of identifying important contents needed for the review, in this work we present a very first work on key phrase classification with a detailed empirical study on traditional and most recent language modeling approaches. From this study, we find that the task of classification of key phrases is ambiguous at a human level producing Cohen's kappa of 0.77 on a new data set. Both pretrained language models and simple TFIDF SVM classifiers produce similar results with a former producing average of 0.6 F1 higher than the latter. We finally derive practical advice from our extensive empirical and model interpretability results for those interested in key phrase classification from educational reports in the future.
Data-Driven Model Predictive Control using Interpolated Koopman Generators

In recent years, the success of the Koopman operator in dynamical systems analysis has also fueled the development of Koopman operator-based control frameworks. In order to preserve the relatively low data requirements for an approximation via Dynamic Mode Decomposition, a quantization approach was recently proposed in [Peitz \& Klus, Automatica 106, 2019]. This way, control of nonlinear dynamical systems can be realized by means of switched systems techniques, using only a finite set of autonomous Koopman operator-based reduced models. These individual systems can be approximated very efficiently from data. The main idea is to transform a control system into a set of autonomous systems for which the optimal switching sequence has to be computed. In this article, we extend these results to continuous control inputs using relaxation. This way, we combine the advantages of the data efficiency of approximating a finite set of autonomous systems with continuous controls. We show that when using the Koopman generator, this relaxation --- realized by linear interpolation between two operators --- does not introduce any error for control affine systems. This allows us to control high-dimensional nonlinear systems using bilinear, low-dimensional surrogate models. The efficiency of the proposed approach is demonstrated using several examples with increasing complexity, from the Duffing oscillator to the chaotic fluidic pinball.
Optimizing Medical Treatment for Sepsis in Intensive Care: from Reinforcement Learning to Pre-Trial Evaluation

Our aim is to establish a framework where reinforcement learning (RL) of optimizing interventions retrospectively allows us a regulatory compliant pathway to prospective clinical testing of the learned policies in a clinical deployment. We focus on infections in intensive care units which are one of the major causes of death and difficult to treat because of the complex and opaque patient dynamics, and the clinically debated, highly-divergent set of intervention policies required by each individual patient, yet intensive care units are naturally data rich. In our work, we build on RL approaches in healthcare ("AI Clinicians"), and learn off-policy continuous dosing policy of pharmaceuticals for sepsis treatment using historical intensive care data under partially observable MDPs (POMDPs). POMPDs capture uncertainty in patient state better by taking in all historical information, yielding an efficient representation, which we investigate through ablations. We compensate for the lack of exploration in our retrospective data by evaluating each encountered state with a best-first tree search. We mitigate state distributional shift by optimizing our policy in the vicinity of the clinicians' compound policy. Crucially, we evaluate our model recommendations using not only conventional policy evaluations but a novel framework that incorporates human experts: a model-agnostic pre-clinical evaluation method to estimate the accuracy and uncertainty of clinician's decisions versus our system recommendations when confronted with the same individual patient history ("shadow mode").
Field-induced transitions in the Kitaev material 
α−RuCl3
 probed by thermal expansion and magnetostriction

High-resolution thermal expansion and magnetostriction measurements were performed on single crystals of $\ensuremath{\alpha}\ensuremath{-}{\mathrm{RuCl}}_{3}$ in magnetic fields applied parallel to the Ru-Ru bonds. The length changes were measured in the direction perpendicular to the honeycomb planes. Our data show clear thermodynamic characteristics for the field-induced phase transition at the critical field ${\ensuremath{\mu}}_{0}{H}_{\mathrm{c}1}=7.8(2)\phantom{\rule{0.16em}{0ex}}\mathrm{T}$ where the antiferromagnetic zigzag order is suppressed. At higher fields, a kink in the magnetostriction coefficient signals an additional transition or crossover around ${\ensuremath{\mu}}_{0}{H}_{\mathrm{c}2}\ensuremath{\approx}11\phantom{\rule{0.16em}{0ex}}\mathrm{T}$. The extracted Gr\"uneisen ratio shows typical hallmarks for quantum criticality near ${H}_{\mathrm{c}1}$ but also displays anomalous behavior above ${H}_{\mathrm{c}1}$. We compare our experimental data with spin-wave calculations employing a minimal Kitaev-Heisenberg model in the semiclassical limit. Most of the salient features are in agreement with each other, however, the peculiar features in the region above ${H}_{\mathrm{c}1}$ cannot be accounted for in our semiclassical modeling and hence suggest a genuine quantum nature. We construct a phase diagram for $\ensuremath{\alpha}\ensuremath{-}{\mathrm{RuCl}}_{3}$ in a magnetic field along the Ru-Ru bonds, displaying a zigzag ordered state below ${H}_{\mathrm{c}1}$, a quantum paramagnetic regime between ${H}_{\mathrm{c}1}$ and ${H}_{\mathrm{c}2}$, and a semiclassical partially polarized state above ${H}_{\mathrm{c}2}$.
Recurrent convolutional neural networks for mandible segmentation from computed tomography

Recently, accurate mandible segmentation in CT scans based on deep learning methods has attracted much attention. However, there still exist two major challenges, namely, metal artifacts among mandibles and large variations in shape or size among individuals. To address these two challenges, we propose a recurrent segmentation convolutional neural network (RSegCNN) that embeds segmentation convolutional neural network (SegCNN) into the recurrent neural network (RNN) for robust and accurate segmentation of the mandible. Such a design of the system takes into account the similarity and continuity of the mandible shapes captured in adjacent image slices in CT scans. The RSegCNN infers the mandible information based on the recurrent structure with the embedded encoder-decoder segmentation (SegCNN) components. The recurrent structure guides the system to exploit relevant and important information from adjacent slices, while the SegCNN component focuses on the mandible shapes from a single CT slice. We conducted extensive experiments to evaluate the proposed RSegCNN on two head and neck CT datasets. The experimental results show that the RSegCNN is significantly better than the state-of-the-art models for accurate mandible segmentation.
On Approximation, Bounding & Exact Calculation of Block Error Probability for Random Codes

This paper presents a method to calculate the exact average block error probability of some random code ensembles under maximum-likelihood decoding. Deviating from Shannon’s 1959 solid angle argument, we project the problem into two dimensions and apply standard trigonometry. This enables us to also analyze Gaussian random codes in additive white Gaussian noise and binary random codes for the binary symmetric channel. We find that the Voronoi regions harden doubly-exponential in the blocklength and utilize that to propose the new median bound that outperforms Shannon’s 1959 sphere packing bound for the uniform spherical ensemble, whenever the code contains more than three codewords. Furthermore, we propose a very tight approximation to simplify computation of both exact error probability and the two bounds.
Can Celebrities Burst Your Bubble?

Polarization is a growing, global problem. As such, many social media based solutions have been proposed in order to reduce it. In this study, we propose a new solution that recommends topics to celebrities to encourage them to join a polarized debate and increase exposure to contrarian content - bursting the filter bubble. Using a state-of-the art model that quantifies the degree of polarization, this paper makes a first attempt to empirically answer the question: Can celebrities burst filter bubbles? We use a case study to analyze how people react when celebrities are involved in a controversial topic and conclude with a list possible research directions.
Effective edge-based approach for promoting the spreading of SIR model

Promoting some typical spreading dynamics, for instance, the spreading of information, commercial message, vaccination guidance, innovation, and political movement, can bring benefits to all aspects of the socio-economic systems. In this study, we propose a strategy for promoting the spreading of the susceptible-infected-recovered model, which is widely applied to describe these common spreading dynamics in real life. Specifically, we first quantify the potential influence that the addition of each latent edge (that is, edges that do not exist before) could cause to the spreading dynamics. Then, we strategically add the latent edges to the original networks according to the potential influence of each latent edge. Numerical simulations verify the effectiveness of our strategy and demonstrate that our strategy outperforms several static strategies, namely, adding the latent edges between nodes with the largest degree or eigenvector centrality. This study provides an effective way of promoting the spreading of the susceptible-infected-recovered model by modifying the network structure slightly and helps in understanding what a better network structure for the spreading dynamics is. Besides, the theoretical framework established in this study provides inspirations for the further investigations of edge-based promoting strategies for other spreading models.
Stochastic gradient descent with random learning rate

We propose to optimize neural networks with a uniformly-distributed random learning rate. The associated stochastic gradient descent algorithm can be approximated by continuous stochastic equations and analyzed with the Fokker-Planck formalism. In the small learning rate approximation, the training process is characterized by an effective temperature which depends on the average learning rate, the mini-batch size and the momentum of the optimization algorithm. By comparing the random learning rate protocol with cyclic and constant protocols, we suggest that the random choice is generically the best strategy in the small learning rate regime, yielding better regularization without extra computational cost. We provide supporting evidence through experiments on both shallow, fully-connected and deep, convolutional neural networks for image classification on the MNIST and CIFAR10 datasets.
OccuSeg: Occupancy-Aware 3D Instance Segmentation

3D instance segmentation, with a variety of applications in robotics and augmented reality, is in large demands these days. Unlike 2D images that are projective observations of the environment, 3D models provide metric reconstruction of the scenes without occlusion or scale ambiguity. In this paper, we define “3D occupancy size”, as the number of voxels occupied by each instance. It owns advantages of robustness in prediction, on which basis, OccuSeg, an occupancy-aware 3D instance segmentation scheme is proposed. Our multi-task learning produces both occupancy signal and embedding representations, where the training of spatial and feature embeddings varies with their difference in scale-aware. Our clustering scheme benefits from the reliable comparison between the predicted occupancy size and the clustered occupancy size, which encourages hard samples being correctly clustered and avoids over segmentation. The proposed approach achieves state-of-theart performance on 3 real-world datasets, i.e. ScanNetV2, S3DIS and SceneNN, while maintaining high efficiency.
Anomalous Instance Detection in Deep Learning: A Survey

Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in incorrect outputs. To make DL more robust, several posthoc anomaly detection techniques to detect (and discard) these anomalous samples have been proposed in the recent past. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection for DL based applications. We provide a taxonomy for existing techniques based on their underlying assumptions and adopted approaches. We discuss various techniques in each of the categories and provide the relative strengths and weaknesses of the approaches. Our goal in this survey is to provide an easier yet better understanding of the techniques belonging to different categories in which research has been done on this topic. Finally, we highlight the unsolved research challenges while applying anomaly detection techniques in DL systems and present some high-impact future research directions.
Synonymous Generalization in Sequence-to-Sequence Recurrent Networks

When learning a language, people can quickly expand their understanding of the unknown content by using compositional skills, such as from two words "go" and "fast" to a new phrase "go fast. " In recent work of Lake and Baroni (2017), modern Sequence-to-Sequence(seq2seq) Recurrent Neural Networks (RNNs) can make powerful zero-shot generalizations in specifically controlled experiments. However, there is a missing regarding the property of such strong generalization and its precise requirements. This paper explores this positive result in detail and defines this pattern as the synonymous generalization, an ability to recognize an unknown sequence by decomposing the difference between it and a known sequence as corresponding existing synonyms. To better investigate it, I introduce a new environment called Colorful Extended Cleanup World (CECW), which consists of complex commands paired with logical expressions. While demonstrating that sequential RNNs can perform synonymous generalizations on foreign commands, I conclude their prerequisites for success. I also propose a data augmentation method, which is successfully verified on the Geoquery (GEO) dataset, as a novel application of synonymous generalization for real cases.
New Complementary Sets With Low PAPR Property Under Spectral Null Constraints

Complementary set sequences (CSSs) are useful for dealing with the high peak-to-average power ratio (PAPR) problem in orthogonal frequency division multiplexing (OFDM) systems. In practical OFDM transmission, however, certain sub-carriers maybe reserved and/or prohibited to transmit signals, leading to the so-called spectral null constraint (SNC) design problem. For example, the DC sub-carrier is reserved to avoid the offsets in D/A and A/D converter in the LTE systems. While most of the current research focus on the design of low PAPR CSSs to improve the code-rate, few works address the aforementioned SNC in their designs. This motivates us to investigate CSSs with SNC as well as low PAPR property. In this article, we present systematic constructions of CSSs under SNCs and low PAPR. First, we show that mutually orthogonal complementary sets (MOCSs) can be used as seed sequences to generate new CSSs with SNC and low PAPR, and then provide an iterative technique for the construction of MOCSs which can be further used to generate complementary sets (CSs) with low PAPRs and spectral nulls at varying positions in the designed sequences. Next, inspired by a recent idea of Chen, we propose a novel construction of these seed MOCSs with non-power-of-two lengths from generalized Boolean functions.
CoinMagic: A Differential Privacy Framework for Ring Signature Schemes

By allowing users to obscure their transactions via including "mixins" (chaff coins), ring signature schemes have been widely used to protect a sender's identity of a transaction in privacy-preserving blockchain systems, like Monero and Bytecoin. However, recent works point out that the existing ring signature scheme is vulnerable to the "chain-reaction" analysis (i.e., the spent coin in a given ring signature can be deduced through elimination). Especially, when the diversity of mixins is low, the spent coin will have a high risk to be detected. To overcome the weakness, the ring signature should be consisted of a set of mixins with high diversity and produce observations having "similar" distributions for any two coins. In this paper, we propose a notion, namely $\epsilon$-coin-indistinguishability ($\epsilon$-CI), to formally define the "similar" distribution guaranteed through a differential privacy scheme. Then, we formally define the CI-aware mixins selection problem with disjoint-superset constraint (CIA-MS-DS), which aims to find a mixin set that has maximal diversity and satisfies the constraints of $\epsilon$-CI and the budget. In CIA-MS-DS, each ring signature is either disjoint with or the superset of its preceding ring signatures. We prove that CIA-MS-DS is NP-hard and thus intractable. To solve the CIA-MS-DS problem, we propose two approximation algorithms, namely the Progressive Algorithm and the Game Theoretic Algorithm, with theoretic guarantees. Through extensive experiments on both real data sets and synthetic data sets, we demonstrate the efficiency and the effectiveness of our approaches.
Loss-tolerant transmission of multiphoton-qubit information via hybrid entanglement

It was shown that using multiphoton qubits, a nearly deterministic Bell-state measurement can be performed with linear optics and on-off photodetectors [Phys. Rev. Lett. 114, 113603 (2015)]. However, multiphoton qubits are generally more fragile than single-photon qubits under a lossy environment. In this paper, we propose and analyze a scheme to teleport multiphoton-qubit information using hybrid entanglement with a loss-tolerant carrier qubit. We consider three candidates for the carrier qubit: a coherent-state qubit, a single-photon polarization qubit, and a vacuum-andsingle-photon qubit. We find that teleportation with the vacuum-and-single-photon qubit tolerates about 10 times greater photon losses than with the multiphoton qubit of the photon number N ≥ 4 in the high fidelity regime (F ≥ 90%). The coherent-state qubit encoding may be even better than the vacuum-and-single-photon qubit as the carrier when its amplitude is as small as α < 0.78. We further point out that the fidelity of the teleported state by our scheme is determined by loss in the carrier qubit while the success probability depends on loss only in the multiphoton qubit to be teleported. Our study implies that the hybrid architecture may complement the weaknesses of each qubit encoding.