arXiv subjects

Di Wang

Publications and source records attributed to Di Wang.

At least 577 records · Page 32Linked to original sources

arXiv2019

XFe4Ge2 (X = Y, Lu) and Mn3Pt: Filling-enforced magnetic topological metals

Magnetism, coupled with nontrivial band topology, can bring about many interesting and exotic phenomena, so that magnetic topological materials have attracted persistent research interest. However, compared with non-magnetic topological materials (TMs), the magnetic TMs are less studied, since their magnetic structures and topological phase transitions are usually complex and the first-principles predictions are usually sensitive on the effect of Coulomb interaction. In this work, we present a comprehensive investigation of XFe4Ge2 (X = Y, Lu) and Mn3Pt, and find these materials to be filling-enforced magnetic topological metals. Our first-principles calculations show that XFe4Ge2 (X = Y, Lu) host Dirac points near the Fermi level at high symmetry point S. These Dirac points are protected by PT symmetry (P and T are inversion and time-reversal transformations, respectively) and a 2-fold screw rotation symmetry. Moreover, through breaking PT symmetry, the Dirac points would split into Weyl nodes. Mn3Pt is found to host 4-fold degenerate band crossings in the whole high symmetry path of A-Z. We also utilize the GGA+U scheme to take into account the effect of Coulomb repulsion and find that the filling-enforced topological properties are naturally insensitive on U.

cond-mat.mes-hall↗

arXiv2019

Facility Location Problem in Differential Privacy Model Revisited

In this paper we study the uncapacitated facility location problem in the model of differential privacy (DP) with uniform facility cost. Specifically, we first show that, under the hierarchically well-separated tree (HST) metrics and the super-set output setting that was introduced in Gupta et. al., there is an $ε$-DP algorithm that achieves an $O(\frac{1}ε)$(expected multiplicative) approximation ratio; this implies an $O(\frac{\log n}ε)$ approximation ratio for the general metric case, where $n$ is the size of the input metric. These bounds improve the best-known results given by Gupta et. al. In particular, our approximation ratio for HST-metrics is independent of $n$, and the ratio for general metrics is independent of the aspect ratio of the input metric. On the negative side, we show that the approximation ratio of any $ε$-DP algorithm is lower bounded by $Ω(\frac{1}{\sqrtε})$, even for instances on HST metrics with uniform facility cost, under the super-set output setting. The lower bound shows that the dependence of the approximation ratio for HST metrics on $ε$ can not be removed or greatly improved. Our novel methods and techniques for both the upper and lower bound may find additional applications.

cs.DS↗

arXiv2019

Flowless: Extracting Densest Subgraphs Without Flow Computations

We propose a simple and computationally efficient method for dense subgraph discovery, which is a classic problem both in theory and in practice. It is well known that dense subgraphs can have strong correlation with structures of interest in real-world networks across various domains ranging from social networks to biological systems [Gionis and Tsourakakis `15]. For the densest subgraph problem, Charikar's greedy algorithm [Asashiro `00, Charikar `00] is very simple, and can typically find result of quality much better than the provable factor 2-approximation, which makes it very popular in practice. However, it is also known to give suboptimal output in many real-world examples. On the other hand, finding the exact optimal solution requires the computation of maximum flow. Despite the existence of highly optimized maximum flow solvers, such computation still incurs prohibitive computational costs for the massive graphs arising in modern data science applications. We devise an iterative algorithm which naturally generalizes the greedy algorithm of Charikar. Our algorithm draws insights from the iterative approaches from convex optimization, and also exploits the dual interpretation of the densest subgraph problem. We have empirical evidence that our algorithm is much more robust against the structural heterogeneities in real-world datasets, and converges to the optimal subgraph density even when the simple greedy algorithm fails. On the other hand, in instances where Charikar's algorithm performs well, our algorithm is able to quickly verify its optimality. Furthermore, we demonstrate that our method is significantly faster than the maximum flow based exact optimal algorithm. We conduct experiments on datasets from broad domains, and our algorithm achieves $\sim$145$\times$ speedup on average to find subgraphs whose density is at least 90\% of the optimal value.

cs.DS↗

arXiv2019

Terahertz dual phase gradient metasurface: high-efficiency binary-channel spoof surface plasmon excitation

Spoof surface plasmon meta-couplers are compact antennas which link propagating waves and surface waves. However, most of them are designed with a fixed phase gradient and channel for the incident waves with specific polarization, which limits their further applications in multichannel scenarios. In this Letter, we propose, to the best of our knowledge, a new method that combines the Brillouin folds theory with the Generalized Snell Law. We demonstrate that when the phase gradient of the metasurface is large enough, Brillouin folds effect will occur, which will create dual phase gradient space in a single metasurface. With this method, we design two novel terahertz meta-couplers with functionalities of symmetrical and asymmetrical binary-channel/bidirectional SSP excitation. Furthermore, finite element method (FEM) simulations are performed to demonstrate their functionalities. Considering the orthogonality of the incident waves, there can be a total of four independent space channels to excite SSP on one metasurface. This work may open up new routes in multi-channel SSP meta-couplers and multi-beam surface wave antennas.

physics.app-ph↗

arXiv2019

Chip-compatible quantum plasmonic launcher

Integrated on-demand single-photon sources are critical for the implementation of photonic quantum information processing systems. To enable practical quantum photonic devices, the emission rates of solid-state quantum emitters need to be substantially enhanced and the emitted signal must be directly coupled to an on-chip circuitry. The photon emission rate speed-up is best achieved via coupling to plasmonic antennas, while on-chip integration can be easily obtained by directly coupling emitters to photonic waveguides. The realization of practical devices requires that both the emission speed-up and efficient out-couping are achieved in a single architecture. Here, we propose a novel platform that effectively combines on-chip compatibility with high radiative emission rates, a quantum plasmonic launcher. The proposed launchers contain single nitrogen-vacancy (NV) centers in nanodiamonds as quantum emitters that offer record-high average fluorescence lifetime shortening factors of about 7000 times. Nanodiamonds with single NV are sandwiched between two silver films that couple more than half of the emission into in-plane propagating surface plasmon polaritons. This simple, compact, and scalable architecture represents a crucial step towards the practical realization of high-speed on-chip quantum networks.

quant-ph↗

arXiv2019

F-theory superspace backgrounds

F-theory is the theory proposed to incorporate superstring theory in a way such that STU dualities are manifest. A useful description uses a current superalgebra on a higher-dimensional worldvolume, following from an action for a selfdual gauge field. Here the group "metric" appearing in the Schwinger (central charge) term of this current superalgebra is generalized to a tensor, in analogy to the usual generalization of the structure constants to the torsion (and curvature). This allows introduction of a massless background describing F-supergravity on the original bosonic worldvolume. The isotropy group is represented on superspace, while the (exceptional) symmetry is represented on the worldvolume. As an example, we solve off shell the linearized superspace constraints of the massless sector of the F-theory that generalizes the N=2 supergravity (+ matter) of 3D S(tring)-theory, the corresponding manifestly T-dual theory of T-theory, and the N=1 supergravity of 4D M-theory. The results for the prepotential, its gauge transformation, and action agree with those that were derived previously without reference to the current algebra of the full F-theory.

hep-th↗

arXiv2019

Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations

Messages in human conversations inherently convey emotions. The task of detecting emotions in textual conversations leads to a wide range of applications such as opinion mining in social networks. However, enabling machines to analyze emotions in conversations is challenging, partly because humans often rely on the context and commonsense knowledge to express emotions. In this paper, we address these challenges by proposing a Knowledge-Enriched Transformer (KET), where contextual utterances are interpreted using hierarchical self-attention and external commonsense knowledge is dynamically leveraged using a context-aware affective graph attention mechanism. Experiments on multiple textual conversation datasets demonstrate that both context and commonsense knowledge are consistently beneficial to the emotion detection performance. In addition, the experimental results show that our KET model outperforms the state-of-the-art models on most of the tested datasets in F1 score.

cs.CL↗

arXiv2019

INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps

Behavior-related research areas such as motion prediction/planning, representation/imitation learning, behavior modeling/generation, and algorithm testing, require support from high-quality motion datasets containing interactive driving scenarios with different driving cultures. In this paper, we present an INTERnational, Adversarial and Cooperative moTION dataset (INTERACTION dataset) in interactive driving scenarios with semantic maps. Five features of the dataset are highlighted. 1) The interactive driving scenarios are diverse, including urban/highway/ramp merging and lane changes, roundabouts with yield/stop signs, signalized intersections, intersections with one/two/all-way stops, etc. 2) Motion data from different countries and different continents are collected so that driving preferences and styles in different cultures are naturally included. 3) The driving behavior is highly interactive and complex with adversarial and cooperative motions of various traffic participants. Highly complex behavior such as negotiations, aggressive/irrational decisions and traffic rule violations are densely contained in the dataset, while regular behavior can also be found from cautious car-following, stop, left/right/U-turn to rational lane-change and cycling and pedestrian crossing, etc. 4) The levels of criticality span wide, from regular safe operations to dangerous, near-collision maneuvers. Real collision, although relatively slight, is also included. 5) Maps with complete semantic information are provided with physical layers, reference lines, lanelet connections and traffic rules. The data is recorded from drones and traffic cameras. Statistics of the dataset in terms of number of entities and interaction density are also provided, along with some utilization examples in a variety of behavior-related research areas. The dataset can be downloaded via https://interaction-dataset.com.

cs.RO↗

arXiv2019

Faster width-dependent algorithm for mixed packing and covering LPs

In this paper, we give a faster width-dependent algorithm for mixed packing-covering LPs. Mixed packing-covering LPs are fundamental to combinatorial optimization in computer science and operations research. Our algorithm finds a $1+\eps$ approximate solution in time $O(Nw/ \eps)$, where $N$ is number of nonzero entries in the constraint matrix and $w$ is the maximum number of nonzeros in any constraint. This run-time is better than Nesterov's smoothing algorithm which requires $O(N\sqrt{n}w/ \eps)$ where $n$ is the dimension of the problem. Our work utilizes the framework of area convexity introduced in [Sherman-FOCS'17] to obtain the best dependence on $\eps$ while breaking the infamous $\ell_{\infty}$ barrier to eliminate the factor of $\sqrt{n}$. The current best width-independent algorithm for this problem runs in time $O(N/\eps^2)$ [Young-arXiv-14] and hence has worse running time dependence on $\eps$. Many real life instances of the mixed packing-covering problems exhibit small width and for such cases, our algorithm can report higher precision results when compared to width-independent algorithms. As a special case of our result, we report a $1+\eps$ approximation algorithm for the densest subgraph problem which runs in time $O(md/ \eps)$, where $m$ is the number of edges in the graph and $d$ is the maximum graph degree.

math.OC↗

arXiv2019

Compact Autoregressive Network

Autoregressive networks can achieve promising performance in many sequence modeling tasks with short-range dependence. However, when handling high-dimensional inputs and outputs, the huge amount of parameters in the network lead to expensive computational cost and low learning efficiency. The problem can be alleviated slightly by introducing one more narrow hidden layer to the network, but the sample size required to achieve a certain training error is still large. To address this challenge, we rearrange the weight matrices of a linear autoregressive network into a tensor form, and then make use of Tucker decomposition to represent low-rank structures. This leads to a novel compact autoregressive network, called Tucker AutoRegressive (TAR) net. Interestingly, the TAR net can be applied to sequences with long-range dependence since the dimension along the sequential order is reduced. Theoretical studies show that the TAR net improves the learning efficiency, and requires much fewer samples for model training. Experiments on synthetic and real-world datasets demonstrate the promising performance of the proposed compact network.

cs.LG↗

arXiv2019

Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications

With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization. First, the utilization of black box models (e.g., deep neural networks) calls for advanced techniques in exploring and interpreting model behaviors. Second, the rapid growth in computing has produced enormous datasets that require techniques that can handle millions or more samples. Although some solutions to these interpretability challenges have been proposed, they typically do not scale beyond thousands of samples, nor do they provide the high-level intuition scientists are looking for. Here, we present the first scalable solution to explore and analyze high-dimensional functions often encountered in the scientific data analysis pipeline. By combining a new streaming neighborhood graph construction, the corresponding topology computation, and a novel data aggregation scheme, namely topology aware datacubes, we enable interactive exploration of both the topological and the geometric aspect of high-dimensional data. Following two use cases from high-energy-density (HED) physics and computational biology, we demonstrate how these capabilities have led to crucial new insights in both applications.

cs.LG↗

arXiv2019

Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation

We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks that transform any inputs into natural language, such as machine translation, summarization, dialog, content manipulation, and so forth. With the design goals of modularity, versatility, and extensibility in mind, Texar extracts common patterns underlying the diverse tasks and methodologies, creates a library of highly reusable modules, and allows arbitrary model architectures and algorithmic paradigms. In Texar, model architecture, inference, and learning processes are properly decomposed. Modules at a high concept level can be freely assembled and plugged in/swapped out. The toolkit also supports a rich set of large-scale pretrained models. Texar is thus particularly suitable for researchers and practitioners to do fast prototyping and experimentation. The versatile toolkit also fosters technique sharing across different text generation tasks. Texar supports both TensorFlow and PyTorch, and is released under Apache License 2.0 at https://www.texar.io.

cs.CL↗

arXiv2019

Single-Path Mobile AutoML: Efficient ConvNet Design and NAS Hyperparameter Optimization

Can we reduce the search cost of Neural Architecture Search (NAS) from days down to only few hours? NAS methods automate the design of Convolutional Networks (ConvNets) under hardware constraints and they have emerged as key components of AutoML frameworks. However, the NAS problem remains challenging due to the combinatorially large design space and the significant search time (at least 200 GPU-hours). In this work, we alleviate the NAS search cost down to less than 3 hours, while achieving state-of-the-art image classification results under mobile latency constraints. We propose a novel differentiable NAS formulation, namely Single-Path NAS, that uses one single-path over-parameterized ConvNet to encode all architectural decisions based on shared convolutional kernel parameters, hence drastically decreasing the search overhead. Single-Path NAS achieves state-of-the-art top-1 ImageNet accuracy (75.62%), hence outperforming existing mobile NAS methods in similar latency settings (~80ms). In particular, we enhance the accuracy-runtime trade-off in differentiable NAS by treating the Squeeze-and-Excitation path as a fully searchable operation with our novel single-path encoding. Our method has an overall cost of only 8 epochs (24 TPU-hours), which is up to 5,000x faster compared to prior work. Moreover, we study how different NAS formulation choices affect the performance of the designed ConvNets. Furthermore, we exploit the efficiency of our method to answer an interesting question: instead of empirically tuning the hyperparameters of the NAS solver (as in prior work), can we automatically find the hyperparameter values that yield the desired accuracy-runtime trade-off? We open-source our entire codebase at: https://github.com/dstamoulis/single-path-nas.

cs.LG↗

arXiv2019

Flows in Almost Linear Time via Adaptive Preconditioning

We present algorithms for solving a large class of flow and regression problems on unit weighted graphs to $(1 + 1 / poly(n))$ accuracy in almost-linear time. These problems include $\ell_p$-norm minimizing flow for $p$ large ($p \in [ω(1), o(\log^{2/3} n) ]$), and their duals, $\ell_p$-norm semi-supervised learning for $p$ close to $1$. As $p$ tends to infinity, $\ell_p$-norm flow and its dual tend to max-flow and min-cut respectively. Using this connection and our algorithms, we give an alternate approach for approximating undirected max-flow, and the first almost-linear time approximations of discretizations of total variation minimization objectives. This algorithm demonstrates that many tools previous viewed as limited to linear systems are in fact applicable to a much wider range of convex objectives. It is based on the the routing-based solver for Laplacian linear systems by Spielman and Teng (STOC '04, SIMAX '14), but require several new tools: adaptive non-linear preconditioning, tree-routing based ultra-sparsification for mixed $\ell_2$ and $\ell_p$ norm objectives, and decomposing graphs into uniform expanders.

cs.DS↗

arXiv2019

$^{81}$Kr dating at the Guliya ice cap, Tibetan Plateau

We present radiometric $^{81}$Kr dating results for ice samples collected at the outlets of the Guliya ice cap in the western Kunlun Mountains of the Tibetan Plateau. This first application of $^{81}$Kr dating on mid-latitude glacier ice was made possible by recent advances in Atom Trap Trace Analysis, particularly a reduction in the required sample size down to 1 $μ$L STP of krypton. Eight ice blocks were sampled from the bottom of the glacier at three different sites along the southern edges. The $^{81}$Kr data yield upper age limits in the range of 15-74 ka (90% confidence level). This is an order of magnitude lower than the ages exceeding 500 ka which the previous $^{36}$Cl data suggest for the bottom of the Guliya ice core. It is also significantly lower than the widely used chronology up to 110 ka established for the upper part of the core based on $δ^{18}$O in the ice.

physics.geo-ph↗

arXiv2019

Neural Learning of Online Consumer Credit Risk

This paper takes a deep learning approach to understand consumer credit risk when e-commerce platforms issue unsecured credit to finance customers' purchase. The "NeuCredit" model can capture both serial dependences in multi-dimensional time series data when event frequencies in each dimension differ. It also captures nonlinear cross-sectional interactions among different time-evolving features. Also, the predicted default probability is designed to be interpretable such that risks can be decomposed into three components: the subjective risk indicating the consumers' willingness to repay, the objective risk indicating their ability to repay, and the behavioral risk indicating consumers' behavioral differences. Using a unique dataset from one of the largest global e-commerce platforms, we show that the inclusion of shopping behavioral data, besides conventional payment records, requires a deep learning approach to extract the information content of these data, which turns out significantly enhancing forecasting performance than the traditional machine learning methods.

q-fin.RM↗

arXiv2019

Two-dimensional topological materials discovery by symmetry-indicator method

Two-dimensional (2D) topological materials (TMs) have attracted tremendous attention due to the promise of revolutionary devices with non-dissipative electric or spin currents. Unfortunately, the scarcity of 2D TMs holds back the experimental realization of such devices. In this work, based on our recently developed, highly efficient TM discovery algorithm using symmetry indicators, we explore the possible 2D TMs in all non-magnetic compounds in four recently proposed materials databases for possible 2D materials. We identify hundreds of 2D TM candidates, including 205 topological (crystalline) insulators and 299 topological semimetals. In particular, we highlight MoS, with a mirror Chern number of -4, as a possible experimental platform for studying the interaction-induced modification to the topological classification of materials. Our results winnow out the topologically interesting 2D materials from these databases and provide a TM gene pool which for further experimental studies.

cond-mat.mes-hall↗

arXiv2019

Sum rules for $CP$ asymmetries of charmed baryon decays in the $SU(3)_F$ limit

Motivated by the recent LHCb observation of $CP$ violation in charm, we study $CP$ violation in the charmed baryon decays. A simple method to search for the $CP$ violation relations in the flavor $SU(3)$ limit, which is associated with a complete interchange of $d$ and $s$ quarks, is proposed. With this method, hundreds of $CP$ violation sum rules in the doubly and singly charmed baryon decays can be found. As examples, the $CP$ violation sum rules in two-body charmed baryon decays are presented. Some of the $CP$ violation sum rules could help the experiment to find better observables. As byproducts, the branching fraction of $Ξ^+_c\to pK^-π^+$ is predicted to be $(1.7\pm 0.5)\%$ in the $U$-spin limit and the fragmentation-fraction ratio is determined as $f_{Ξ_b}/f_{Λ_b}=0.065\pm 0.020$ using the LHCb data.

hep-ph↗