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Bo Wen

Bo Wen appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
cond-mat.mes-hallcond-mat.mtrl-sciMachine LearningArtificial IntelligenceComputation and LanguageComputer Visioneess.IVEmerging Technologiescond-mat.stat-mechcond-mat.str-elHardware Architecturemath.OCNeural and Evolutionary Computing

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Published work

18 published item(s)

preprint2026arXiv

Establishing Robust Retinal Eye Tracking: A Weakly Supervised Algorithmic Framework

Retinal image-based eye tracking is widely used in ophthalmic imaging and vision science, and is a promising path to deliver higher gaze accuracy than the pupil- and cornea-based approaches commonly used in modern AR/VR devices. Nevertheless, existing retinal tracking algorithms still primarily rely on classical template-matching registration, which can be insufficiently robust to retinal feature variability and real-world imaging conditions. In this work, we propose a novel weakly-supervised, learning-based framework for robust retinal eye tracking. Initial studies demonstrate high accuracy, achieving the 95th-percentile gaze error < 0.45 deg across a cohort of 6 participants.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Universal Vessel Segmentation for Multi-Modality Retinal Images

We identify two major limitations in the existing studies on retinal vessel segmentation: (1) Most existing works are restricted to one modality, i.e., the Color Fundus (CF). However, multi-modality retinal images are used every day in the study of the retina and diagnosis of retinal diseases, and the study of vessel segmentation on other modalities is scarce; (2) Even though a few works extended their experiments to new modalities such as the Multi-Color Scanning Laser Ophthalmoscopy (MC), these works still require fine-tuning a separate model for the new modality. The fine-tuning will require extra training data, which is difficult to acquire. In this work, we present a novel universal vessel segmentation model (URVSM) for multi-modality retinal images. In addition to performing the study on a much wider range of image modalities, we also propose a universal model to segment the vessels in all these commonly used modalities. While being much more versatile compared with existing methods, our universal model also demonstrates comparable performance to the state-of-the-art fine-tuned methods. To the best of our knowledge, this is the first work that achieves modality-agnostic retinal vessel segmentation and the first to study retinal vessel segmentation in several novel modalities.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Experimentally realized memristive memory augmented neural network

Lifelong on-device learning is a key challenge for machine intelligence, and this requires learning from few, often single, samples. Memory augmented neural network has been proposed to achieve the goal, but the memory module has to be stored in an off-chip memory due to its size. Therefore the practical use has been heavily limited. Previous works on emerging memory-based implementation have difficulties in scaling up because different modules with various structures are difficult to integrate on the same chip and the small sense margin of the content addressable memory for the memory module heavily limited the degree of mismatch calculation. In this work, we implement the entire memory augmented neural network architecture in a fully integrated memristive crossbar platform and achieve an accuracy that closely matches standard software on digital hardware for the Omniglot dataset. The successful demonstration is supported by implementing new functions in crossbars in addition to widely reported matrix multiplications. For example, the locality-sensitive hashing operation is implemented in crossbar arrays by exploiting the intrinsic stochasticity of memristor devices. Besides, the content-addressable memory module is realized in crossbars, which also supports the degree of mismatches. Simulations based on experimentally validated models show such an implementation can be efficiently scaled up for one-shot learning on the Mini-ImageNet dataset. The successful demonstration paves the way for practical on-device lifelong learning and opens possibilities for novel attention-based algorithms not possible in conventional hardware.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognition

Named entity recognition (NER) task aims at identifying entities from a piece of text that belong to predefined semantic types such as person, location, organization, etc. The state-of-the-art solutions for flat entities NER commonly suffer from capturing the fine-grained semantic information in underlying texts. The existing span-based approaches overcome this limitation, but the computation time is still a concern. In this work, we propose a novel span-based NER framework, namely Global Pointer (GP), that leverages the relative positions through a multiplicative attention mechanism. The ultimate goal is to enable a global view that considers the beginning and the end positions to predict the entity. To this end, we design two modules to identify the head and the tail of a given entity to enable the inconsistency between the training and inference processes. Moreover, we introduce a novel classification loss function to address the imbalance label problem. In terms of parameters, we introduce a simple but effective approximate method to reduce the training parameters. We extensively evaluate GP on various benchmark datasets. Our extensive experiments demonstrate that GP can outperform the existing solution. Moreover, the experimental results show the efficacy of the introduced loss function compared to softmax and entropy alternatives.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

PKGM: A Pre-trained Knowledge Graph Model for E-commerce Application

In recent years, knowledge graphs have been widely applied as a uniform way to organize data and have enhanced many tasks requiring knowledge. In online shopping platform Taobao, we built a billion-scale e-commerce product knowledge graph. It organizes data uniformly and provides item knowledge services for various tasks such as item recommendation. Usually, such knowledge services are provided through triple data, while this implementation includes (1) tedious data selection works on product knowledge graph and (2) task model designing works to infuse those triples knowledge. More importantly, product knowledge graph is far from complete, resulting error propagation to knowledge enhanced tasks. To avoid these problems, we propose a Pre-trained Knowledge Graph Model (PKGM) for the billion-scale product knowledge graph. On the one hand, it could provide item knowledge services in a uniform way with service vectors for embedding-based and item-knowledge-related task models without accessing triple data. On the other hand, it's service is provided based on implicitly completed product knowledge graph, overcoming the common the incomplete issue. We also propose two general ways to integrate the service vectors from PKGM into downstream task models. We test PKGM in five knowledge-related tasks, item classification, item resolution, item recommendation, scene detection and sequential recommendation. Experimental results show that PKGM introduces significant performance gains on these tasks, illustrating the useful of service vectors from PKGM.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The Role of Permanent and Induced Electrostatic Dipole Moments for Schottky Barriers in Janus MXY/Graphene Heterostructures: a First Principles Study

The Schottky barrier height ($E_{SBH}$) is a crucial factor in determining the transport properties of semiconductor materials as it directly regulates the carrier mobility in opto-electronics devices. In principle, van der Waals (vdW) Janus heterostructures offer an appealing avenue to controlling the ESBH. However, the underlying atomistic mechanisms are far from understood conclusively, which prompts for further research in the topic. To this end, here, we carry out an extensive first principles study of the electronic properties and $E_{SBH}$ of several vdW Janus MXY/Graphene (M=Mo, W; X, Y=S, Se, Te) heterostructures. The results of the simulations show that by changing the composition and geometry of the heterostructure's interface, it is possible to control its electrical contact, thence electron transport properties, from Ohmic to Schottky with nearly one order of magnitude variations in the $E_{SBH}$. Detailed analysis of the simulations enables rationalization of this highly attractive property on the basis of the interplay between the permanent dipole moment of the Janus MXY sheet and the induced one due to interfacial charge redistribution at the MXY/Gr interface. Such an interplay is shown to be highly effective in altering the electrostatic potential difference across the vdW Janus heterostructure, determining its ESBH, thence Schottky (Ohmic) contact type. These computational findings contribute guidelines to control electrical contacts in Janus heterostructures towards rational design of electrical contacts in nanoscale devices.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

ZLPR: A Novel Loss for Multi-label Classification

In the era of deep learning, loss functions determine the range of tasks available to models and algorithms. To support the application of deep learning in multi-label classification (MLC) tasks, we propose the ZLPR (zero-bounded log-sum-exp \& pairwise rank-based) loss in this paper. Compared to other rank-based losses for MLC, ZLPR can handel problems that the number of target labels is uncertain, which, in this point of view, makes it equally capable with the other two strategies often used in MLC, namely the binary relevance (BR) and the label powerset (LP). Additionally, ZLPR takes the corelation between labels into consideration, which makes it more comprehensive than the BR methods. In terms of computational complexity, ZLPR can compete with the BR methods because its prediction is also label-independent, which makes it take less time and memory than the LP methods. Our experiments demonstrate the effectiveness of ZLPR on multiple benchmark datasets and multiple evaluation metrics. Moreover, we propose the soft version and the corresponding KL-divergency calculation method of ZLPR, which makes it possible to apply some regularization tricks such as label smoothing to enhance the generalization of models.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Electron optics with ballistic graphene junctions

Electrons transmitted across a ballistic semiconductor junction undergo refraction, analogous to light rays across an optical boundary. A pn junction theoretically provides the equivalent of a negative index medium, enabling novel electron optics such as negative refraction and perfect (Veselago) lensing. In graphene, the linear dispersion and zero-gap bandstructure admit highly transparent pn junctions by simple electrostatic gating, which cannot be achieved in conventional semiconductors. Moreover ballistic transport over micron length scales at ambient temperature has been realized, providing an ideal platform to realize a new generation of device based on electron lensing. Robust demonstration of these effects, however, has not been forthcoming. Here we employ transverse magnetic focusing to probe propagation across an electrostatically defined graphene junction. We find perfect agreement with the predicted Snells law for electrons, including observation of both positive and negative refraction. Resonant transmission across the pn junction provides a direct measurement of the angle dependent transmission coefficient, and we demonstrate good agreement with theory. Comparing experimental data with simulation reveals the crucial role played by the effective junction width, providing guidance for future device design. Our results pave the way for realizing novel electron optics based on graphene pn junctions.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Linear Convergence of Proximal Gradient Algorithm with Extrapolation for a Class of Nonconvex Nonsmooth Minimization Problems

In this paper, we study the proximal gradient algorithm with extrapolation for minimizing the sum of a Lipschitz differentiable function and a proper closed convex function. Under the error bound condition used in [19] for analyzing the convergence of the proximal gradient algorithm, we show that there exists a threshold such that if the extrapolation coefficients are chosen below this threshold, then the sequence generated converges $R$-linearly to a stationary point of the problem. Moreover, the corresponding sequence of objective values is also $R$-linearly convergent. In addition, the threshold reduces to $1$ for convex problems and, as a consequence, we obtain the $R$-linear convergence of the sequence generated by FISTA with fixed restart. Finally, we present some numerical experiments to illustrate our results.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Unconventional Correlation between Quantum Hall Transport Quantization and Bulk State Filling in Gated Graphene Devices

We report simultaneous transport and scanning microwave impedance microscopy to examine the correlation between transport quantization and filling of the bulk Landau levels in the quantum Hall regime in gated graphene devices. Surprisingly, a comparison of these measurements reveals that quantized transport typically occurs below the complete filling of bulk Landau levels, when the bulk is still conductive. This result points to a revised understanding of transport quantization when carriers are accumulated by gating. We discuss the implications on transport study of the quantum Hall effect in graphene and related topological states in other two-dimensional electron systems.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Fractional fractal quantum Hall effect in graphene superlattices

The Hofstadter energy spectrum provides a uniquely tunable system to study emergent topological order in the regime of strong interactions. Previous experiments, however, have been limited to the trivial case of low Bloch band filling where only the Landau level index plays a significant role. Here we report measurement of high mobility graphene superlattices where the complete unit cell of the Hofstadter spectrum is accessible. We observe coexistence of conventional fractional quantum Hall effect (QHE) states together with the integer QHE states associated with the fractal Hofstadter spectrum. At large magnetic field, a new series of states appear at fractional Bloch filling index. These fractional Bloch band QHE states are not anticipated by existing theoretical pictures and point towards a new type of many-body state.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Determination of Intrinsic Magnetic Response from Local Measurements of Fringing Fields

Micron-sized Hall bars and micro-SQUIDs are now used routinely to measure the local static and dynamic magnetic response with micron-scale spatial resolution. While this provides a powerful new tool, determining the intrinsic magnetization presents new challenges, as it requires correcting for demagnetization fields that vary widely with position on a sample. In this paper we develop a method to correct for the demagnetization effect at local points of a rectangular prism shaped sample using a finite element analysis of Maxwell's equation applied to local Hall sensor measurements calibrated by bulk measurements of the magnetization. This method can be generalized to other geometric shapes to analyze data obtained with local magnetic probes.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Quantum Fluctuations and Long-Range Order in Molecular Magnets

We review our studies of the effect of transverse fields on the susceptibility and magnetization of single crystals of the prototype single molecule magnet (SMM), Mn$_{12}$-acetate, and of a new high-symmetry variant, Mn$_{12}$-acetate-MeOH. SMM single crystals can exhibit long range ferromagnetic order associated with intermolecular dipole interactions. Transverse fields increase quantum spin fluctuation and quantum tunneling of the magnetization suppressing long range order. However, we have found that suppression of the Curie temperature by a transverse field in Mn$_{12}$-acetate is far more rapid than predicted by the Transverse-Field Ising Ferromagnetic Model (TFIFM). It appears that solvent disorder in Mn$_{12}$-acetate that results in an intrinsic distribution of small discrete tilts of the molecular magnetic easy axis from the global easy axis of the crystal ($\approx \pm 1^\circ$) gives rise to a distribution of random-fields that further suppresses long-range order. Semiquantitative agreement with the predictions of a Random-Field Ising Ferromagnet Model is found. Subsequent susceptibility studies we have conducted of the high symmetry Mn$_{12}$ variant, Mn$_{12}$-acetate-MeOH, with the same spin structure and similar lattice constants but without the same solvent disorder as Mn$_{12}$-acetate, agrees with the TFIFM. An important implication of our studies is that long-range order in these two chemically very similar SMMs are described by distinct physical models.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Critical Behavior of a Strongly Interacting 2D Electron System

With decreasing density $n_s$ the thermopower $S$ of a low-disorder 2D electron system in silicon is found to exhibit a sharp increase by more than an order of magnitude, tending to a divergence at a finite, disorder-independent density $n_t$ consistent with the critical form $(-T/S) \propto (n_s-n_t)^x$ with $x=1.0\pm 0.1$ ($T$ is the temperature). Our results provide clear evidence for an interaction-induced transition to a new phase at low density in a strongly-interacting 2D electron system.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Transverse Field Ising Ferromagnetism in Mn$_{12}$-acetate-MeOH

We report measurements of the magnetic susceptibility of single crystals of Mn$_{12}$-acetate-MeOH, a new high-symmetry variant of the original single molecule magnet Mn$_{12}$-acetate. A comparison of these data to theory and to data for the Mn$_{12}$ acetate material shows that Mn$_{12}$-acetate-MeOH is a realization of a transverse-field Ising ferromagnet in contrast to the original Mn$_{12}$ acetate material, in which solvent disorder leads to effects attributed to random field Ising ferromagnetism.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

Experimental determination of the Weiss temperature of Mn$_{12}$-ac and Mn$_{12}$-ac-MeOH

We report measurements of the susceptibility in the temperature range from $3.5$ K to $6.0$ K of a series of Mn$_{12}$-ac and Mn$_{12}$-ac-MeOH samples in the shape of rectangular prisms of length $l_c$ and square cross-section of side $l_a$. The susceptibility obeys a Curie-Weiss Law, $χ=C/(T-θ)$, where $θ$ varies systematically with sample aspect ratio. Using published demagnetization factors, we obtain $θ$ for an infinitely long sample corresponding to intrinsic ordering temperatures $T_c \approx 0.85$ K and $\approx 0.74$ K for Mn$_{12}$-ac and Mn$_{12}$-ac-MeOH, respectively. The difference in $T_c$ for two materials that have nearly identical unit cell volumes and lattice constant ratios suggests that, in addition to dipolar interactions, there is a non-dipolar (exchange) contribution to the Weiss temperature that differs in the two materials because of the difference in ligand molecules.

0 citations0 reviewsNo code linkedOpen access
preprint2009arXiv

Realization of random-field dipolar Ising ferromagnetism in a molecular magnet

The longitudinal magnetic susceptibility of single crystals of the molecular magnet Mn$_{12}$-acetate obeys a Curie-Weiss law, indicating a transition to a ferromagnetic phase due to dipolar interactions. With increasing magnetic field applied transverse to the easy axis, the transition temperature decreases considerably more rapidly than predicted by mean field theory to a T=0 quantum critical point. Our results are consistent with an effective Hamiltonian for a random-field Ising ferromagnet in a transverse field, where the randomness is induced by an external field applied to Mn$_{12}$-acetate crystals that are known to have an intrinsic distribution of locally tilted magnetic easy axes.

0 citations0 reviewsNo code linkedOpen access
preprint2009arXiv

Tuning Magnetic Avalanches in Mn12-ac

Using micron-sized Hall sensor arrays to obtain time-resolved measurements of the local magnetization, we report a systematic study in the molecular magnet Mn$_{12}$-acetate of magnetic avalanches controllably triggered in different fixed external magnetic fields and for different values of the initial magnetization. The speeds of propagation of the spin-reversal fronts are in good overall agreement with the theory of magnetic deflagration of Garanin and Chudnovsky \cite{Garanin}.

0 citations0 reviewsNo code linkedOpen access