arXiv subjects

Fan Wu

Publications and source records attributed to Fan Wu.

At least 361 records · Page 20Linked to original sources

arXiv2020

Ascertaining price formation in cryptocurrency markets with DeepLearning

The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity. This paper is inspired by the recent success of using deep learning for stock market prediction. In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. In particular, we applied a deep learning approach to predict the direction of the mid-price changes on the upcoming tick. We monitored live tick-level data from $8$ cryptocurrency pairs and applied both statistical and machine learning techniques to provide a live prediction. We reveal that promising results are possible for cryptocurrencies, and in particular, we achieve a consistent $78\%$ accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs US dollars.

q-fin.GN↗

arXiv2020

Theory of strongly paired fermions with arbitrary short-range interactions

We develop an effective field theory to describe the superfluid pairing in strongly interacting fermions with arbitrary short-range attractions, by extending Kaplan's idea of coupling fermions to a fictitious boson state in Nucl. Phys. B \textbf{494}, 471 (1997). This boson field is assigned with unconventional kinetic term to recover the exact scattering phase shift obtained either from scattering data or model calculations. The theory works even if the explicit form of the interaction potential has not been constructed from scattering data. The contact boson-fermion coupling allows us to go beyond mean-field to include Gaussian pair fluctuations, yielding reliable predictions on equations of state. As an application, we use our theory to address the non-univerisal ground-state energy of strongly paired fermions, due to the non-trivial momentum dependence of the phase shift characterized, for example, by effective range. We find a good agreement between our predictions and recent quantum Monte Carlo simulations on the effective-range dependence in both three and two spatial dimensions. We propose that in cold-atom experiments, the non-universal dependence in thermodynamics can be probed using dark-state optical control of Feshbach resonances.

cond-mat.quant-gas↗

arXiv2019

Online Pricing with Reserve Price Constraint for Personal Data Markets

The society's insatiable appetites for personal data are driving the emergency of data markets, allowing data consumers to launch customized queries over the datasets collected by a data broker from data owners. In this paper, we study how the data broker can maximize her cumulative revenue by posting reasonable prices for sequential queries. We thus propose a contextual dynamic pricing mechanism with the reserve price constraint, which features the properties of ellipsoid for efficient online optimization, and can support linear and non-linear market value models with uncertainty. In particular, under low uncertainty, our pricing mechanism provides a worst-case regret logarithmic in the number of queries. We further extend to other similar application scenarios, including hospitality service, online advertising, and loan application, and extensively evaluate three pricing instances of noisy linear query, accommodation rental, and impression over MovieLens 20M dataset, Airbnb listings in U.S. major cities, and Avazu mobile ad click dataset, respectively. The analysis and evaluation results reveal that our proposed pricing mechanism incurs low practical regret, online latency, and memory overhead, and also demonstrate that the existence of reserve price can mitigate the cold-start problem in a posted price mechanism, and thus can reduce the cumulative regret.

cs.CE↗

arXiv2019

Secure Federated Submodel Learning

Federated learning was proposed with an intriguing vision of achieving collaborative machine learning among numerous clients without uploading their private data to a cloud server. However, the conventional framework requires each client to leverage the full model for learning, which can be prohibitively inefficient for resource-constrained clients and large-scale deep learning tasks. We thus propose a new framework, called federated submodel learning, where clients download only the needed parts of the full model, namely submodels, and then upload the submodel updates. Nevertheless, the "position" of a client's truly required submodel corresponds to her private data, and its disclosure to the cloud server during interactions inevitably breaks the tenet of federated learning. To integrate efficiency and privacy, we have designed a secure federated submodel learning scheme coupled with a private set union protocol as a cornerstone. Our secure scheme features the properties of randomized response, secure aggregation, and Bloom filter, and endows each client with a customized plausible deniability, in terms of local differential privacy, against the position of her desired submodel, thus protecting her private data. We further instantiated our scheme with the e-commerce recommendation scenario in Alibaba, implemented a prototype system, and extensively evaluated its performance over 30-day Taobao user data. The analysis and evaluation results demonstrate the feasibility and scalability of our scheme from model accuracy and convergency, practical communication, computation, and storage overheads, as well as manifest its remarkable advantages over the conventional federated learning framework.

cs.LG↗

arXiv2019

Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations

Proteins are the major building blocks of life, and actuators of almost all chemical and biophysical events in living organisms. Their native structures in turn enable their biological functions which have a fundamental role in drug design. This motivates predicting the structure of a protein from its sequence of amino acids, a fundamental problem in computational biology. In this work, we demonstrate state-of-the-art protein structure prediction (PSP) results using embeddings and deep learning models for prediction of backbone atom distance matrices and torsion angles. We recover 3D coordinates of backbone atoms and reconstruct full atom protein by optimization. We create a new gold standard dataset of proteins which is comprehensive and easy to use. Our dataset consists of amino acid sequences, Q8 secondary structures, position specific scoring matrices, multiple sequence alignment co-evolutionary features, backbone atom distance matrices, torsion angles, and 3D coordinates. We evaluate the quality of our structure prediction by RMSD on the latest Critical Assessment of Techniques for Protein Structure Prediction (CASP) test data and demonstrate competitive results with the winning teams and AlphaFold in CASP13 and supersede the results of the winning teams in CASP12. We make our data, models, and code publicly available.

q-bio.BM↗

arXiv2019

Visual Natural Language Query Auto-Completion for Estimating Instance Probabilities

We present a new task of query auto-completion for estimating instance probabilities. We complete a user query prefix conditioned upon an image. Given the complete query, we fine tune a BERT embedding for estimating probabilities of a broad set of instances. The resulting instance probabilities are used for selection while being agnostic to the segmentation or attention mechanism. Our results demonstrate that auto-completion using both language and vision performs better than using only language, and that fine tuning a BERT embedding allows to efficiently rank instances in the image. In the spirit of reproducible research we make our data, models, and code available.

cs.CV↗

arXiv2019

High-resolution 3D refractive index microscopy of multiple-scattering samples from intensity images

Optical diffraction tomography (ODT) reconstructs a samples volumetric refractive index (RI) to create high-contrast, quantitative 3D visualizations of biological samples. However, standard implementations of ODT use interferometric systems, and so are sensitive to phase instabilities, complex mechanical design, and coherent noise. Furthermore, their reconstruction framework is typically limited to weakly-scattering samples, and thus excludes a whole class of multiple-scattering samples. Here, we implement a new 3D RI microscopy technique that utilizes a computational multi-slice beam propagation method to invert the optical scattering process and reconstruct high-resolution (NA>1.0) 3D RI distributions of multiple-scattering samples. The method acquires intensity-only measurements from different illumination angles, and then solves a non-linear optimization problem to recover the sample 3D RI distribution. We experimentally demonstrate reconstruction of samples with varying amounts of multiple scattering: a 3T3 fibroblast cell, a cluster of C. elegans embryos, and a whole C. elegans worm, with lateral and axial resolutions of 250 nm and 900 nm, respectively.

eess.IV↗

arXiv2019

Large Fermi Surface Expansion through Anisotropic c-f Mixing in the Semimetallic Kondo Lattice System CeBi

Using angle-resolved photoemission spectroscopy (ARPES) and resonant ARPES, we report evidence of strong anisotropic conduction-f electron mixing (c-f mixing) in CeBi by observing a largely expanded Ce-5d pocket at low temperature, with no change in the Bi-6p bands. The Fermi surface (FS) expansion is accompanied by a pronounced spectral weight transfer from the local 4f 0 peak of Ce (corresponding to Ce3+) to the itinerant conduction bands near the Fermi level. Careful analysis suggests that the observed large FS change (with a volume expansion of the electron pocket up to 40%) can most naturally be explained by a small valence change (~ 1%) of Ce, which coexists with a very weak Kondo screening. Our work therefore provides evidence for a FS change driven by real charge fluctuations deep in the Kondo limit, which is made possible by the low carrier density.

cond-mat.str-el↗

arXiv2019

Factor Graph Neural Network

Most of the successful deep neural network architectures are structured, often consisting of elements like convolutional neural networks and gated recurrent neural networks. Recently, graph neural networks have been successfully applied to graph structured data such as point cloud and molecular data. These networks often only consider pairwise dependencies, as they operate on a graph structure. We generalize the graph neural network into a factor graph neural network (FGNN) in order to capture higher order dependencies. We show that FGNN is able to represent Max-Product Belief Propagation, an approximate inference algorithm on probabilistic graphical models; hence it is able to do well when Max-Product does well. Promising results on both synthetic and real datasets demonstrate the effectiveness of the proposed model.

cs.LG↗

arXiv2019

Two-body bound state of ultracold Fermi atoms with two-dimensional spin-orbit coupling

In a recent experiment, a two-dimensional spin-orbit coupling (SOC) was realized for fermions in the continuum [Nat. Phys. 12, 540 (2016)], which represents an important step forward in the study of synthetic gauge field using cold atoms. In the experiment, it was shown that a Raman-induced two-dimensional SOC exists in the dressed-state basis close to a Dirac point of the single-particle spectrum. By contrast, the short-range inter-atomic interactions of the system are typically expressed in the hyperfine-spin basis. The interplay between synthetic SOC and interactions can potentially lead to interesting few- and many-body phenomena but has so far eluded theoretical attention. Here we study in detail properties of two-body bound states of such a system. We find that, due to the competition between SOC and interaction, the stability region of the two-body bound state is in general reduced. Particularly, the threshold of the lowest two-body bound state is shifted to a positive, SOC-dependent scattering length. Furthermore, the center-of-mass momentum of the lowest two-body bound state becomes nonzero, suggesting the emergence of Fulde-Ferrell pairing states in a many-body setting. Our results reveal the critical difference between the experimentally realized two-dimensional SOC and the more symmetric Rashba or Dresselhaus SOCs in an interacting system, and paves the way for future characterizations of topological superfluid states in the experimentally relevant systems.

cond-mat.quant-gas↗

arXiv2019

Anomalous quantum oscillations and evidence for a non-trivial Berry phase in SmSb

Topologically non-trivial electronic structures can give rise to a range of unusual physical phenomena, and the interplay of band topology with other effects such as electronic correlations and magnetism requires further exploration. The rare earth monopnictides $X$(Sb,Bi) ($X$ = lanthanide) are a large family of semimetals where these different effects may be tuned by the substitution of rare-earth elements. Here we observe anomalous behavior in the quantum oscillations of one member of this family, antiferromagnetic SmSb. The analysis of Shubnikov-de Haas (SdH) oscillations provides evidence for a non-zero Berry phase, indicating a non-trivial topology of the $α$-band. Furthermore, striking differences are found between the temperature dependence of the amplitudes of de Haas-van Alphen effect oscillations, which are well fitted by the Lifshitz-Kosevich (LK) formula across the measured temperature range, and those from SdH measurements which show a significant disagreement with LK behavior at low temperatures. Our findings of unusual quantum oscillations in an antiferromagnetic, mixed valence semimetal with a possible non-trivial band topology can provide an opportunity for studying the interplay between topology, electronic correlations and magnetism.

cond-mat.str-el↗

arXiv2019

DenseBody: Directly Regressing Dense 3D Human Pose and Shape From a Single Color Image

Recovering 3D human body shape and pose from 2D images is a challenging task due to high complexity and flexibility of human body, and relatively less 3D labeled data. Previous methods addressing these issues typically rely on predicting intermediate results such as body part segmentation, 2D/3D joints, silhouette mask to decompose the problem into multiple sub-tasks in order to utilize more 2D labels. Most previous works incorporated parametric body shape model in their methods and predict parameters in low-dimensional space to represent human body. In this paper, we propose to directly regress the 3D human mesh from a single color image using Convolutional Neural Network(CNN). We use an efficient representation of 3D human shape and pose which can be predicted through an encoder-decoder neural network. The proposed method achieves state-of-the-art performance on several 3D human body datasets including Human3.6M, SURREAL and UP-3D with even faster running speed.

cs.CV↗

arXiv2019

Direct generation of tunable orbital angular momentum beams in microring lasers with broadband exceptional points

Non-Hermitian exceptional points (EPs) represent a special type of degeneracy where not only the eigenvalues coalesce, but also the eigenstates tend to collapse on each other. Recent studies have shown that in the presence of an EP, light-matter interactions are profoundly modified, leading to a host of novel optical phenomena ranging from enhanced sensitivity to chiral light transport. As of now, however, in order to stabilize a system at the vicinity of an exceptional point, its related parameters must be carefully tuned and/or continuously controlled. To overcome this limitation, here we introduce a new family of broadband exceptional points based on unidirectional coupling, implemented by incorporating an Sshaped waveguide in a microring cavity. In active settings, the resulting unidirectionality exhibits unprecedented resilience to perturbations, thus providing a robust and tunable approach for directly generating beams with distinct orbital angular momentum (OAM). This work could open up new possibilities for manipulating OAM degrees of freedom in applications pertaining to telecommunications and quantum computing, while at the same time may expand the notions of non-Hermiticity in the orbital angular momentum space.

physics.optics↗

arXiv2019

Tunable Electronic Structure and Surface States in Rare Earth Mono-Bismuthides with Partially Filled f Shell

Here we report the evolution of bulk band structure and surface states in rare earth mono-bismuthides with partially filled f shell. Utilizing synchrotron-based photoemission spectroscopy, we determined the three-dimensional bulk band structure and identified the bulk band inversions near the X points, which, according to the topological theory, could give rise to nontrivial band topology with odd number of gapless topological surface states. Near the surface Gamma bar point, no clear evidence for predicted gapless topological surface state is observed due to its strong hybridization with the bulk bands. Near the M bar point, the two surface states, due to projections from two inequivalent bulk band inversions, interact and give rise to two peculiar sets of gapped surface states. The bulk band inversions and corresponding surface states can be tuned substantially by varying rare earth elements, in good agreement with density-functional theory calculations assuming local f electrons. Our study therefore establishes rare earth mono-bismuthides as an interesting class of materials possessing tunable electronic properties and magnetism, providing a promising platform to search for novel properties in potentially correlated topological materials.

cond-mat.mes-hall↗

arXiv2019

Probing the Origin of Extreme Magnetoresistance in Pr/Sm Mono-Antimonides/Bismuthides

Combining angle-resolved photoemission spectroscopy and magneto-transport measurements, we systematically investigated the possible origin of the extreme magnetoresistance in Pr/Sm mono-antimonides/bismuthides (PrSb, SmSb, PrBi, SmBi). Our photoemission measurements reveal that the bulk band inversion and surface states are absent (present) in Pr/Sm antimonides (bismuthides), implying that topological surface states are unlikely to play an important role for the observed extreme magnetoresistance. We found that the electron-hole compensation is well satisfied in all these compounds and the bulk band structure exhibits no obvious temperature dependence from 10 K up to 150 K. Simultaneous fittings of the magnetoresistance and Hall coefficient reveal that the carrier mobility is dramatically enhanced at low temperature, which naturally explains the suppression of extreme magnetoresistance at high temperatures. Our results therefore show that the extreme magnetoresistance in these compounds can be well accounted for by the two-band model with good electron-hole compensation. Finally, we found that both PrSb and SmSb exhibit highly linear bulk bands near the X point and lie close to the transition point between a topologically trivial and nontrivial phase, which might be relevant for the observed anomalous quantum oscillations.

cond-mat.mes-hall↗

arXiv2018

Achieving Data Truthfulness and Privacy Preservation in Data Markets

As a significant business paradigm, many online information platforms have emerged to satisfy society's needs for person-specific data, where a service provider collects raw data from data contributors, and then offers value-added data services to data consumers. However, in the data trading layer, the data consumers face a pressing problem, i.e., how to verify whether the service provider has truthfully collected and processed data? Furthermore, the data contributors are usually unwilling to reveal their sensitive personal data and real identities to the data consumers. In this paper, we propose TPDM, which efficiently integrates data Truthfulness and Privacy preservation in Data Markets. TPDM is structured internally in an Encrypt-then-Sign fashion, using partially homomorphic encryption and identity-based signature. It simultaneously facilitates batch verification, data processing, and outcome verification, while maintaining identity preservation and data confidentiality. We also instantiate TPDM with a profile matching service and a distribution fitting service, and extensively evaluate their performances on Yahoo! Music ratings dataset and 2009 RECS dataset, respectively. Our analysis and evaluation results reveal that TPDM achieves several desirable properties, while incurring low computation and communication overheads when supporting large-scale data markets.

cs.DB↗

arXiv2018

Fine-Grained User Profiling for Personalized Task Matching in Mobile Crowdsensing

In mobile crowdsensing, finding the best match between tasks and users is crucial to ensure both the quality and effectiveness of a crowdsensing system. Existing works usually assume a centralized task assignment by the crowdsensing platform, without addressing the need of fine-grained personalized task matching. In this paper, we argue that it is essential to match tasks to users based on a careful characterization of both the users' preference and reliability. To that end, we propose a personalized task recommender system for mobile crowdsensing, which recommends tasks to users based on a recommendation score that jointly takes each user's preference and reliability into consideration. We first present a hybrid preference metric to characterize users' preference by exploiting their implicit feedback. Then, to profile users' reliability levels, we formalize the problem as a semi-supervised learning model, and propose an efficient block coordinate descent algorithm to solve the problem. For some tasks that lack users' historical information, we further propose a matrix factorization method to infer the users' reliability levels on those tasks. We conduct extensive experiments to evaluate the performance of our system, and the evaluation results demonstrate that our system can achieve superior performance to the benchmarks in both user profiling and personalized task recommendation.

cs.IR↗

arXiv2018

Tunable Electronic Structure and Topological Properties of $LnPn$ ($Ln$=Ce, Pr, Gd, Sm, Yb; $Pn$=Sb, Bi)

We have performed systematic first principles study of the electronic structure and band topology properties of $LnPn$ compounds ($Ln$=Ce, Pr, Gd, Sm, Yb; $Pn$=Sb, Bi). Assuming the $f$-electrons are well localized in these materials, both hybrid functional and modified Becke-Johnson calculations yield electronic structure in good agreement with experimental observations, while generalized gradient approximation calculations severely overestimate the band inversions. From Ce to Yb, a systematic reduction of band inversion with respect to the increasing $Ln$ atomic number is observed, and $\mathcal{Z}_2$ for Ce$Pn$ and Yb$Pn$ are [1;000] and [0;000], respectively. In both hybrid functional and modified Becke-Johns calculations, a topologically nontrivial to trivial transition is expected around SmSb for the antimonides and around DyBi for the bismuthides. Such variation is related with lanthanide contraction, but is different from simple pressure effect.

cond-mat.str-el↗