Showing 21–40 of 375 results
Bo Peng
We show that the topological conjugacy relation of diffeomorphisms on any manifold of dimension at least 2 is not classifiable by countable structures. This answers a question of Foreman and Gorodetski. We also prove that $E_0$ is reducible into the topological conjugacy relation of minimal diffeomorphisms on the 2-torus, which answers a question of Foreman.
Xinyi Ling, Hanwen Du, Bo Peng, Zhihui Zhu, Xia Ning
Leveraging multimodal data to drive breakthroughs in e-commerce applications through Multimodal Foundation Models (MFMs) is gaining increasing attention from the research community. However, there are significant challenges that hinder the optimal use of multimodal e-commerce data by foundation models: (1) the scarcity of large-scale, high-quality multimodal benchmark datasets; and (2) the lack of effective multimodal information integration methods. To address these challenges, in this paper, we introduce MMECInstruct, the first-ever, large-scale, and high-quality multimodal instruction dataset for e-commerce. We also develop CASLIE, a simple, lightweight, yet effective framework for integrating multimodal information for e-commerce. Leveraging MMECInstruct, we fine-tune a series of e-commerce MFMs within CASLIE, denoted as CASLIE models. Our comprehensive evaluation demonstrates that CASLIE models substantially outperform 5 categories of advanced baseline models in the in-domain evaluation. Moreover, CASLIE models show strong generalizability to out-of-domain settings. MMECInstruct and CASLIE models are publicly accessible through https://ninglab.github.io/CASLIE/.
Bo Peng, Michele Pizzochero
Monolayer fullerene (C$_{60}$) networks combine molecular-level rigidity with crystalline connectivity, offering a promising platform for numerous applications. In this Feature article, we review the physical and chemical properties of fullerene monolayers, focusing on first-principles studies. We first explore the structural stability of monolayer phases and investigate their thermal expansion behaviours. We then outline criteria for photocatalytic water splitting and introduce theoretical predictions which are supported by recent experimental verification. Finally, we show how interlayer stacking, molecular size, and dimensional tuning (from 2D monolayers into 3D crystals, 1D chains, or nanoribbons) offer versatile approaches to modulate their chemical functionality. Together, these insights establish fullerene networks as a novel class of carbon-based materials with tailored properties for catalysis, photovoltaics, and flexible electronics.
Bo Peng, Michele Pizzochero
Using first-principles calculations, we examine the electronic structure of quasi-one-dimensional fullerene nanoribbons derived from two-dimensional fullerene networks. Depending on the edge geometry and width, these nanoribbons exhibit a rich variety of properties, including direct and indirect band gaps, positive and negative effective masses, as well as dispersive and flat bands. Our findings establish a comprehensive understanding of the electronic properties of fullerene nanoribbons, with potential implications for the design of future nanoscale devices.
Bo Peng, Yuan Liu, Karol Kowalski
We present COMPOSER, a compile-once modular parametric oracle for similarity-encoded effective reduction of electronic-structure operators (e.g., Schrieffer-Wolff-type constructions). Low-rank factorizations compress Hamiltonians and anti-Hermitian generators into rank-one bilinear and projected-quadratic ladders with near-linear scaling at fixed thresholds; each ladder admits deterministic, number-conserving preparation and a block encoding using constant number of signal ancillas. A fixed PREP-SELECT-PREP template multiplexes these ladders, and one QSP polynomial performs the spectral transformation with degree set by operator norms. For a fixed orbital pool and qubit register, the two-qubit fabric is compiled once; geometry, active-space (mask) updates, and truncations are absorbed by re-dialed single-qubit rotations. We introduce a mask-aware similarity-sandwich effective-Hamiltonian construction and benchmark stability under low-rank and second-order-perturation-guided screening. COMPOSER is an execution architecture: algorithmic errors (block-encoding and QSP approximation) are tunable for any supplied parameters, while physical accuracy depends on how those parameters are obtained if not refined.
Bo Peng, Fabrizio Arrigoni Battaia, Amit Vishwas, Mingyu Li, Edoardo Iani, Fengwu Sun, Qiong Li, Carl Ferkinhoff, Gordon Stacey, Zheng Cai, Rob Ivison
The circumgalactic medium (CGM) plays a vital role in galaxy evolution, however, studying the emission from CGM is challenging due to its low surface brightness and the complexities involved in interpreting resonant lines such as Ly$α$. The near-infrared coverage, unprecedented sensitivity, and high spatial resolution of JWST enable us to study the optical strong lines associated with the extended Ly$α$ "nebulae" at redshifts of 2--3. These lines serve as diagnostic tools to infer the physical conditions in the CGM gas reservoir of these systems. In deep medium-band images taken by the JWST, we serendipitously discovered the [O III] emission from the CGM around a massive interacting galaxy system at a redshift z~2.8, known to be embedded in a bright extended (100 kpc) Ly$α$ "nebula." This is the first time that the [O III] lines have been detected from a Ly$α$ "nebula." The JWST images reveal that the CGM gas actually resides in narrow (~ 2.5 kpc) filamentary structures with strong [O III] emission, tracing the same extent as the Ly$α$ emission. An analysis of the [O III] suggests that the emitting CGM is fully ionized and is energetically dominated by mechanical heating. We also find that the density and pressure are higher than those commonly predicted by simulations of the CGM. We conclude that the observed CGM emission originates from the gas expelled by the episodic feedback processes, cooling down and enriching the CGM, while traveling a distance of at least 60 kpc. These observations demonstrate how intensive feedback processes shape gas distribution and properties in the CGM around massive halos. While access to such deep, high-resolution imaging opens up a new discovery space for investigating the CGM, it also challenges numerical simulations with respect to explaining and reproducing the exquisitely complex structures revealed by the observations.
Bo Peng, Xiaohui Yao, Shannon L. Risacher, Andrew J. Saykin, Li Shen, Xia Ning
Background:Cognitive assessments represent the most common clinical routine for the diagnosis of Alzheimer's Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, it is important to determine a proper set of cognitive tests for different subjects. Most current studies create guidelines of cognitive test selection for a targeted population, but they are not customized for each individual subject. In this manuscript, we develop a machine learning paradigm enabling personalized cognitive assessments prioritization. Method: We adapt a newly developed learning-to-rank approach PLTR to implement our paradigm. This method learns the latent scoring function that pushes the most effective cognitive assessments onto the top of the prioritization list. We also extend PLTR to better separate the most effective cognitive assessments and the less effective ones. Results: Our empirical study on the ADNI data shows that the proposed paradigm outperforms the state-of-the-art baselines on identifying and prioritizing individual-specific cognitive biomarkers. We conduct experiments in cross validation and level-out validation settings. In the two settings, our paradigm significantly outperforms the best baselines with improvement as much as 22.1% and 19.7%, respectively, on prioritizing cognitive features. Conclusions: The proposed paradigm achieves superior performance on prioritizing cognitive biomarkers. The cognitive biomarkers prioritized on top have great potentials to facilitate personalized diagnosis, disease subtyping, and ultimately precision medicine in AD.
Yaqian Wang, Longjiang Deng, Qilin Wei, Yi Wan, Zhen Liu, Xiao Lu, Yue Li, Lei Bi, Li Zhang, Haipeng Lu, Haiyan Chen, Peiheng Zhou, Linbo Zhang, Yingchun Cheng, Xiaoxu Zhao, Yu Ye, Wei Huang, Stephen J. Pennycook, Kian Ping Loh, Bo Peng
Valley pseudospin in two-dimensional (2D) transition-metal dichalcogenides (TMDs) allows optical control of spin-valley polarization and intervalley quantum coherence. Defect states in TMDs give rise to new exciton features and theoretically exhibit spin-valley polarization; however, experimental achievement of this phenomenon remains challenges. Here, we report unambiguous valley pseudospin of defect-bound localized excitons in CVD-grown monolayer MoS2; enhanced valley Zeeman splitting with an effective g-factor of -6.2 is observed. Our results reveal that all five d-orbitals and the increased effective electron mass contribute to the band shift of defect states, demonstrating a new physics of the magnetic responses of defect-bound localized excitons, strikingly different from that of A excitons. Our work paves the way for the manipulation of the spin-valley degrees of freedom through defects toward valleytronic devices.
Zhiyun Ren, Bo Peng, Titus K. Schleyer, Xia Ning
With increasing and extensive use of electronic health records, clinicians are often under time pressure when they need to retrieve important information efficiently among large amounts of patients' health records in clinics. While a search function can be a useful alternative to browsing through a patient's record, it is cumbersome for clinicians to search repeatedly for the same or similar information on similar patients. Under such circumstances, there is a critical need to build effective recommender systems that can generate accurate search term recommendations for clinicians. In this manuscript, we developed a hybrid collaborative filtering model using patients' encounter and search term information to recommend the next search terms for clinicians to retrieve important information fast in clinics. For each patient, the model will recommend terms that either have high co-occurrence frequencies with his/her most recent ICD codes or are highly relevant to the most recent search terms on this patient. We have conducted comprehensive experiments to evaluate the proposed model, and the experimental results demonstrate that our model can outperform all the state-of-the-art baseline methods for top-N search term recommendation on different datasets.
Bo Peng, Hao Zhang, Weiwen Chen, Zhi-Jun Qiu, Hezhu Shao, Heyuan Zhu, Bartomeu Monserrat, Desheng Fu, Hongming Weng
Photo-induced phase transitions (PIPTs) provide an ultrafast, energy-efficient way for precisely manipulating the topological properties of transition-metal ditellurides, and can be used to stabilize a topological phase in an otherwise semiconducting material. Using first-principles calculations, we demonstrate that the PIPT in monolayer MoTe$_2$ from the semiconducting 2H phase to the topological 1T$'$ phase can be triggered purely by electronic excitations that soften multiple lattice vibrational modes. These softenings, driven by a Peierls-like mechanism within the conduction bands, lead to structural symmetry breaking within sub-picosecond timescales, which is shorter than the timescale of a thermally driven phase transition. The transition is predicted to be triggered by photons with energies over $1.96$\,eV, with an associated excited carrier density of $3.4\times10^{14}$\,cm$^{-2}$, which enables a controllable phase transformation by varying the laser wavelength. Our results provide insight into the underlying physics of the phase transition in 2D transition-metal ditellurides, and show an ultrafast phase transition mechanism for manipulation of the topological properties of 2D systems.
Bo Peng, Changming Yue, Hao Zhang, Zhong Fang, Hongming Weng
Predicting a new Dirac semimetal (DSM), as well as other topological materials, is quite challenging, since the relationship between crystal structure, composing atoms and the band topology is complex and elusive. Here, we demonstrate an approach to design DSMs via exploring the chemical degree of freedom. Based on the understanding of the well-known DSM Na$_3$Bi, three compounds in one family, namely Na$_2$MgSn, Na$_2$MgPb and Na$_2$CdSn, have been exactly located. Further hybrid-functional calculations with improved estimation of band inversion show that two of them, Na$_2$MgPb and Na$_2$CdSn, have band topology of DSMs. The nontrivial surface states with Fermi arcs on the (010) and (100) side surfaces are shown to connect the projection of bulk Dirac nodes. Most importantly, the candidate compounds are dynamically stable and have been experimentally synthesized. The ideas in this work would stimulate more designs on locating topological materials based on the understanding of existing ones.
Bo Peng, Shuichi Murakami, Bartomeu Monserrat, Tiantian Zhang
Degenerate points/lines in the bulk band structures of crystals have become a staple of the growing number of topological materials. The bulk-boundary correspondence provides a relation between bulk topology and surface states. While line degeneracies of bulk excitations have been extensively characterized, line degeneracies of surface states are not well understood. We show that SnIP, a quasi-one-dimensional van der Waals material with a double helix crystal structure, exhibits topological nodal rings/lines in both the bulk phonon modes and their corresponding surface states. Using a combination of first-principles calculations, symmetry-based indicator theories and Zak phase analysis, we find that two neighbouring bulk nodal rings form doubly degenerate lines in their drumhead-like surface states, which are protected by the combination of time-reversal and glide mirror symmetries $\mathcal{T}\bar{M}_y$. Our results indicate that surface degeneracies can be generically protected by symmetries such as $\mathcal{T}\bar{M}_y$, and phonons provide an ideal platform to explore such degeneracies.
Dmitry A. Fedorov, Bo Peng, Niranjan Govind, Yuri Alexeev
The variational quantum eigensolver (VQE) is a method that uses a hybrid quantum-classical computational approach to find eigenvalues and eigenvalues of a Hamiltonian. VQE has been proposed as an alternative to fully quantum algorithms such as quantum phase estimation because fully quantum algorithms require quantum hardware that will not be accessible in the near future. VQE has been successfully applied to solve the electronic Schrödinger equation for a variety of small molecules. However, the scalability of this method is limited by two factors: the complexity of the quantum circuits and the complexity of the classical optimization problem. Both of these factors are affected by choice of the variational ansatz used to represent the trial wave function. Hence, the construction of efficacious ansatz is an active area of research. Put another way, modern quantum computers are not capable of executing deep quantum circuits produced by using currently available ansatze for problems that map onto more than several qubits. In this review, we present recent developments in the field of designing effective ansatzes that fall into two categories -- chemistry inspired and hardware efficient -- that produce quantum circuits that are easier to run on modern hardware. We discuss the shortfalls of ansatzes originally formulated for VQE simulations, how they are addressed in more sophisticated methods, and the potential ways for further improvements.
Kai Guo, Bowen Deng, Zhongtai Shi, Yue Li, Xin Wang, Lei Bi, Li Zhang, Haipeng Lu, Peiheng Zhou, Linbo Zhang, Yingchun Cheng, Bo Peng
Long-range magnetic orders in atomically thin ferromagnetic CrI3 give rise to new fascinating physics and application perspectives. The physical properties of two-dimensional (2D) ferromagnetism CrI3 are significantly influenced by interlayer spacing and stacking order, which are sensitive to the hydrostatic pressure and external environments. However, there remains debate on the stacking order at low temperature. Here, we study the interlayer coupling and stacking order of non-encapsulated 2-5 layer and bulk CrI3 at 10 K by Raman spectroscopy; demonstrate a rhombohedral stacking in both antiferromagnetic and ferromagnetic CrI3. The opposite helicity dependence of Ag and Eg modes arising from phonon symmetry further validate the rhombohedral stacking. An anomalous temperature-dependent behavior is observed due to spin-phonon coupling below 60 K. Our work provides insights into the interlayer coupling and stacking orders of 2D ferromagnetic materials.
Fei Hua, Meng Wang, Gushu Li, Bo Peng, Chenxu Liu, Muqing Zheng, Samuel Stein, Yufei Ding, Eddy Z. Zhang, Travis S. Humble, Ang Li
The success of a quantum algorithm hinges on the ability to orchestrate a successful application induction. Detrimental overheads in mapping general quantum circuits to physically implementable routines can be the deciding factor between a successful and erroneous circuit induction. In QASMTrans, we focus on the problem of rapid circuit transpilation. Transpilation plays a crucial role in converting high-level, machine-agnostic circuits into machine-specific circuits constrained by physical topology and supported gate sets. The efficiency of transpilation continues to be a substantial bottleneck, especially when dealing with larger circuits requiring high degrees of inter-qubit interaction. QASMTrans is a high-performance C++ quantum transpiler framework that demonstrates up to 369X speedups compared to the commonly used Qiskit transpiler. We observe speedups on large dense circuits such as uccsd_n24 and qft_n320 which require O(10^6) gates. QASMTrans successfully transpiles the aforementioned circuits in 69s and 31s, whilst Qiskit exceeded an hour of transpilation time. With QASMTrans providing transpiled circuits in a fraction of the time of prior transpilers, potential design space exploration, and heuristic-based transpiler design becomes substantially more tractable. QASMTrans is released at http://github.com/pnnl/qasmtrans.
Bo Peng, Srinivasan Parthasarathy, Xia Ning
Sequential recommendation aims to recommend the next item of users' interest based on their historical interactions. Recently, the self-attention mechanism has been adapted for sequential recommendation, and demonstrated state-of-the-art performance. However, in this manuscript, we show that the self-attention-based sequential recommendation methods could suffer from the localization-deficit issue. As a consequence, in these methods, over the first few blocks, the item representations may quickly diverge from their original representations, and thus, impairs the learning in the following blocks. To mitigate this issue, in this manuscript, we develop a recursive attentive method with reused item representations (RAM) for sequential recommendation. We compare RAM with five state-of-the-art baseline methods on six public benchmark datasets. Our experimental results demonstrate that RAM significantly outperforms the baseline methods on benchmark datasets, with an improvement of as much as 11.3%. Our stability analysis shows that RAM could enable deeper and wider models for better performance. Our run-time performance comparison signifies that RAM could also be more efficient on benchmark datasets.
Yangliu Wu, Deju Zhang, Yanning Zhang, Longjiang Deng, Bo Peng
Multiferroic materials provide robust and efficient routes for the control of magnetism by electric fields, which has been diligently sought after for a long time. The two-dimensional (2D) vdW multiferroics is a more exciting endeavour. To date, the nonvolatile manipulation of magnetism through ferroelectric polarization still remains challenging in a 2D vdW heterostructure multiferroic. Here, we report a van der Waals (vdW) heterostructure multiferroic comprising atomically thin layered antiferromagnet (AFM) CrI3 and ferroelectric (FE) α-In2Se3. We demonstrate anomalously layer-selective nonreciprocal and nonvolatile electric-field control of magnetization by the ferroelectric polarization. The nonreciprocal electric control originates from an intriguing antisymmetric enhancement of interlayer ferromagnetic coupling in the opposite ferroelectric polarization configurations of α-In2Se3, which favor to selectively switch the spins in the second layer. Our work provides numerous possibilities for creating diverse heterostructure multiferroics at the limit of few atomic layers for multi-stage magnetic memories and brain inspired in-memory computing.
Bo Peng, Zhiyong Chen, Yue Li, Zhen Liu, Difei Liang, Longjiang Deng
Two-dimensional (2D) van der Waals (vdW) ferromagnets have opened new avenues for manipulating spin at the limits of single or few atomic layers, and for creating unique magneto-exciton devices through the coupling of long-range ferromagnetic (FM) orders and excitons. However, 2D vdW ferromagnets explored so far have rarely possessed exciton behaviors; to date, FM CrI3 have been recently revealed to show ligand-field photoluminescence correlated with FM ordering, but typically with a broad emission peak. Alternatively, many-body excitons have been observed in antiferromagnetic (AFM) NiPS3, but the coupling of excitons with AFM orders is exponentially more difficult, owing to extremely high coercivity. Here, we report a straightforward approach to realize strong coupling of narrow helical emission and FM orders at a low magnetic field in CrI3 through a relatively simple microsphere cavity. We show that the resonant whispering-gallery-modes (WGM) of SiO2 microspheres give rising to a series of strong oscillation helical emissions with a full width at half-maximum (FWHM) of ~5 nm under continuous wave excitation. Reversible magnetic control and coding of helical luminescence with multiwavelength is realized in the range of 950-1100 nm. This work enables plenty of opportunities for creating magnetic encoding lasing for photonic integrated chips.
Bo Peng, Wei Xiang, Yue Jiang, Wei Wang, Jing Dong, Zhenan Sun, Zhen Lei, Siwei Lyu
This paper presents the summary report on our DFGC 2022 competition. The DeepFake is rapidly evolving, and realistic face-swaps are becoming more deceptive and difficult to detect. On the contrary, methods for detecting DeepFakes are also improving. There is a two-party game between DeepFake creators and defenders. This competition provides a common platform for benchmarking the game between the current state-of-the-arts in DeepFake creation and detection methods. The main research question to be answered by this competition is the current state of the two adversaries when competed with each other. This is the second edition after the last year's DFGC 2021, with a new, more diverse video dataset, a more realistic game setting, and more reasonable evaluation metrics. With this competition, we aim to stimulate research ideas for building better defenses against the DeepFake threats. We also release our DFGC 2022 dataset contributed by both our participants and ourselves to enrich the DeepFake data resources for the research community (https://github.com/NiCE-X/DFGC-2022).
Yangliu Wu, Haipeng Lu, Xiaocang Han, Chendi Yang, Nanshu Liu, Xiaoxu Zhao, Liang Qiao, Wei Ji, Renchao Che, Longjiang Deng, Bo Peng
The search for two-dimensional (2D) van der Waals (vdW) multiferroics is an exciting yet challenging endeavor. Room-temperature 2D vdW few-layer multiferroic is a much bigger insurmountable obstacle. Here we report the discovery of an unconventional 2D vdW multiferroic with out-of-plane ferroelectric polarization and long-range magnetic orders in trilayer NiI2 device from 10 K to 295 K. The evolutions of magnetic domains with magnetic field, and the evolutions between ferroelectric and antiferroelectric phase have been unambiguously observed. More significantly, we realize a robust mutual control of magnetism and ferroelectricity at room temperature. The magnetic domains are manipulated by a small voltage ranging from 1 V to 6 V at 0 T and 295 K. This work opens opportunities for exploring multiferroic physics at the limit of few atomic layers.