arXiv subjects

Jingwen Zhang

Publications and source records attributed to Jingwen Zhang.

At least 73 records · Page 4Linked to original sources

arXiv2024

Revising Properties of Planet-Host Binary Systems. IV. The Radius Distribution of Small Planets in Binary Star Systems is Dependent on Stellar Separation

Small planets ($R_{p} \leq 4 R_{\oplus}$) are divided into rocky super-Earths and gaseous sub-Neptunes separated by a radius gap, but the mechanisms that produce these distinct planet populations remain unclear. Binary stars are the only main-sequence systems with an observable record of the protoplanetary disk lifetime and mass reservoir, and the demographics of planets in binaries may provide insights into planet formation and evolution. To investigate the radius distribution of planets in binary star systems, we observed 207 binary systems hosting 283 confirmed and candidate transiting planets detected by the Kepler mission, then recharacterized the planets while accounting for the observational biases introduced by the secondary star. We found that the population of planets in close binaries ($ρ\leq 100$ au) is significantly different from the planet population in wider binaries ($ρ> 300$ au) or single stars. In contrast to planets around single stars, planets in close binaries appear to have a unimodal radius distribution with a peak near the expected super-Earth peak of $R_{p} \sim 1.3 R_{\oplus}$ and a suppressed population of sub-Neptunes. We conclude that we are observing the direct impact of a reduced disk lifetime, smaller mass reservoir, and possible altered distribution of solids reducing the sub-Neptune formation efficiency. Our results demonstrate the power of binary stars as a laboratory for exploring planet formation and as a controlled experiment of the impact of varied initial conditions on mature planet populations.

astro-ph.EP↗

arXiv2024

Phonon heat conduction across slippery interfaces in twisted graphite

Interlayer rotation in van der Waals (vdW) materials offers great potential for manipulating phonon dynamics and heat flow in advanced electronics with ever higher compactness and power density. However, despite extensive theoretical efforts in recent years, experimental measurements remain scarce especially due to the critical challenges of preparing single-crystalline twisted interfaces and probing interfacial thermal transport with sufficient resolution. Here, we exploited the intrinsic twisted interfaces in highly oriented pyrolytic graphite (HOPG). By developing novel experimental schemes based on microfabricated mesas, we managed to achieve simultaneous mechanical characterizations and thermal measurements. In particular, we pushed the HOPG mesas with a microprobe to identify and rotate single-crystalline intrinsic interfaces owing to their slippery nature as is well known in structural superlubricity. Remarkably, we observed over 30-fold suppression of thermal conductance for the slippery interfaces by using epitaxial graphite as a control. Nonetheless, the interfacial conductance remains around 600 $\mathrm{MWm^{-2}K^{-1}}$ which surpasses the highest values for artificially stacked vdW structures by more than five times. Further, atomic simulations revealed the predominant role of the transverse acoustic phonons. Together, our findings highlight a general physical picture that directly correlates interfacial thermal transport with sliding resistance, and lay the foundation for twist-enabled thermal management which are particularly beneficial to twistronics and slidetronics.

cond-mat.mes-hall↗

arXiv2024

CDM-MPC: An Integrated Dynamic Planning and Control Framework for Bipedal Robots Jumping

Performing acrobatic maneuvers like dynamic jumping in bipedal robots presents significant challenges in terms of actuation, motion planning, and control. Traditional approaches to these tasks often simplify dynamics to enhance computational efficiency, potentially overlooking critical factors such as the control of centroidal angular momentum (CAM) and the variability of centroidal composite rigid body inertia (CCRBI). This paper introduces a novel integrated dynamic planning and control framework, termed centroidal dynamics model-based model predictive control (CDM-MPC), designed for robust jumping control that fully considers centroidal momentum and non-constant CCRBI. The framework comprises an optimization-based kinodynamic motion planner and an MPC controller for real-time trajectory tracking and replanning. Additionally, a centroidal momentum-based inverse kinematics (IK) solver and a landing heuristic controller are developed to ensure stability during high-impact landings. The efficacy of the CDM-MPC framework is validated through extensive testing on the full-sized humanoid robot KUAVO in both simulations and experiments.

cs.RO↗

arXiv2024

Prove Symbolic Regression is NP-hard by Symbol Graph

Symbolic regression (SR) is the task of discovering a symbolic expression that fits a given data set from the space of mathematical expressions. Despite the abundance of research surrounding the SR problem, there's a scarcity of works that confirm its NP-hard nature. Therefore, this paper introduces the concept of a symbol graph as a comprehensive representation of the entire mathematical expression space, effectively illustrating the NP-hard characteristics of the SR problem. Leveraging the symbol graph, we establish a connection between the SR problem and the task of identifying an optimally fitted degree-constrained Steiner Arborescence (DCSAP). The complexity of DCSAP, which is proven to be NP-hard, directly implies the NP-hard nature of the SR problem.

cs.CC↗

arXiv2024

Direct detection and characterization of exoplanets using imaging Fourier transform spectroscopy

Space-based direct imaging provides prospects for detection and spectral characterization of exoplanets at optical and near-infrared wavelengths. Integral field spectrographs (IFS) have been historically baselined for these mission concepts. However, multiple studies have revealed that detector noise is a serious obstacle for such instruments when observing extremely faint targets such as Earth-like planets. Imaging Fourier transform spectrographs (iFTS) are generally less sensitive to detector noise, and have several other compelling features such as simultaneous imaging and spectroscopy, smaller-format detector requirements, and variable spectral resolution. To date, they have not been studied as options for such missions. In this work, we compare the capabilities of integral field spectrographs and imaging Fourier transform spectrographs to directly obtain spectra from an Earth-like planet using analytic and numerical models. Specifically, we compare the required exposure time to achieve the same signal-to-noise ratio of the two architectures over a range of detector and optical system parameters. We find that for a 6-meter telescope, an IFS outperforms an iFTS at optical wavelengths. In the near-IR, the relative efficiency of an IFS and iFTS depends on the instrument design and detector noise. An iFTS will be more efficient than an IFS if the readout noise of near-IR detector is above 2-3 e-/pix/frame (t_frame=1000s), which correspond to half to one-third of detector noise of the state-of-art. However, if the readout noise is further reduced to below this threshold, the performance of an IFS will experience a substantial improvement and become more efficient. These results motivate consideration of an iFTS as an alternative option for future direct imaging space missions in the near-IR.

astro-ph.EP↗

arXiv2024

Dynamical Architectures of S-type Transiting Planets in Binaries I: Target Selection using Hipparcos and Gaia proper motion anomalies

The effect of stellar multiplicity on planetary architecture and orbital dynamics provides an important context for exoplanet demographics. We present a volume-limited catalog up to 300 pc of 66 stars hosting planets and planet candidates from Kepler, K2 and TESS with significant Hipparcos-Gaia proper motion anomalies, which indicate the presence of companions. We assess the reliability of each transiting planet candidate using ground-based follow-up observations, and find that the TESS Objects of Interest (TOIs) with significant proper motion anomalies show nearly four times more false positives due to Eclipsing Binaries compared to TOIs with marginal proper motion anomalies. In addition, we find tentative evidence that orbital periods of planets orbiting TOIs with significant proper motion anomalies are shorter than those orbiting TOIs without significant proper motion anomalies, consistent with the scenario that stellar companions can truncate planet-forming disks. Furthermore, TOIs with significant proper motion anomalies exhibit lower Gaia differential velocities in comparison to field stars with significant proper motion anomalies, suggesting that planets are more likely to form in binary systems with low-mass substellar companions or stellar companions at wider separation. Finally, we characterize the three-dimensional architecture of LTT 1445 ABC using radial velocities, absolute astrometry from Gaia and Hipparcos, and relative astrometry from imaging. Our analysis reveals that LTT 1445 is a nearly flat system, with a mutual inclination of 2.88 deg between the orbit of BC around A and that of C around B. The coplanarity may explain why multiple planets around LTT 1445 A survive in the dynamically hostile environment of this system.

astro-ph.EP↗

arXiv2024

How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs

Most traditional AI safety research has approached AI models as machines and centered on algorithm-focused attacks developed by security experts. As large language models (LLMs) become increasingly common and competent, non-expert users can also impose risks during daily interactions. This paper introduces a new perspective to jailbreak LLMs as human-like communicators, to explore this overlooked intersection between everyday language interaction and AI safety. Specifically, we study how to persuade LLMs to jailbreak them. First, we propose a persuasion taxonomy derived from decades of social science research. Then, we apply the taxonomy to automatically generate interpretable persuasive adversarial prompts (PAP) to jailbreak LLMs. Results show that persuasion significantly increases the jailbreak performance across all risk categories: PAP consistently achieves an attack success rate of over $92\%$ on Llama 2-7b Chat, GPT-3.5, and GPT-4 in $10$ trials, surpassing recent algorithm-focused attacks. On the defense side, we explore various mechanisms against PAP and, found a significant gap in existing defenses, and advocate for more fundamental mitigation for highly interactive LLMs

cs.CL↗

arXiv2024

A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends

General large language models (LLMs), represented by ChatGPT, have demonstrated significant potential in tasks such as code generation in software engineering. This has led to the development of specialized LLMs for software engineering, known as Code LLMs. A considerable portion of Code LLMs is derived from general LLMs through model fine-tuning. As a result, Code LLMs are often updated frequently and their performance can be influenced by the base LLMs. However, there is currently a lack of systematic investigation into Code LLMs and their performance. In this study, we conduct a comprehensive survey and analysis of the types of Code LLMs and their differences in performance compared to general LLMs. We aim to address three questions: (1) What LLMs are specifically designed for software engineering tasks, and what is the relationship between these Code LLMs? (2) Do Code LLMs really outperform general LLMs in software engineering tasks? (3) Which LLMs are more proficient in different software engineering tasks? To answer these questions, we first collect relevant literature and work from five major databases and open-source communities, resulting in 134 works for analysis. Next, we categorize the Code LLMs based on their publishers and examine their relationships with general LLMs and among themselves. Furthermore, we investigate the performance differences between general LLMs and Code LLMs in various software engineering tasks to demonstrate the impact of base models and Code LLMs. Finally, we comprehensively maintained the performance of LLMs across multiple mainstream benchmarks to identify the best-performing LLMs for each software engineering task. Our research not only assists developers of Code LLMs in choosing base models for the development of more advanced LLMs but also provides insights for practitioners to better understand key improvement directions for Code LLMs.

cs.SE↗

arXiv2023

Robust 3D Tracking with Quality-Aware Shape Completion

3D single object tracking remains a challenging problem due to the sparsity and incompleteness of the point clouds. Existing algorithms attempt to address the challenges in two strategies. The first strategy is to learn dense geometric features based on the captured sparse point cloud. Nevertheless, it is quite a formidable task since the learned dense geometric features are with high uncertainty for depicting the shape of the target object. The other strategy is to aggregate the sparse geometric features of multiple templates to enrich the shape information, which is a routine solution in 2D tracking. However, aggregating the coarse shape representations can hardly yield a precise shape representation. Different from 2D pixels, 3D points of different frames can be directly fused by coordinate transform, i.e., shape completion. Considering that, we propose to construct a synthetic target representation composed of dense and complete point clouds depicting the target shape precisely by shape completion for robust 3D tracking. Specifically, we design a voxelized 3D tracking framework with shape completion, in which we propose a quality-aware shape completion mechanism to alleviate the adverse effect of noisy historical predictions. It enables us to effectively construct and leverage the synthetic target representation. Besides, we also develop a voxelized relation modeling module and box refinement module to improve tracking performance. Favorable performance against state-of-the-art algorithms on three benchmarks demonstrates the effectiveness and generalization ability of our method.

cs.CV↗

arXiv2023

Pathology Steered Stratification Network for Subtype Identification in Alzheimer's Disease

Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by beta-amyloid, pathologic tau, and neurodegeneration. There are no effective treatments for Alzheimer's disease at a late stage, urging for early intervention. However, existing statistical inference approaches of AD subtype identification ignore the pathological domain knowledge, which could lead to ill-posed results that are sometimes inconsistent with the essential neurological principles. Integrating systems biology modeling with machine learning, we propose a novel pathology steered stratification network (PSSN) that incorporates established domain knowledge in AD pathology through a reaction-diffusion model, where we consider non-linear interactions between major biomarkers and diffusion along brain structural network. Trained on longitudinal multimodal neuroimaging data, the biological model predicts long-term trajectories that capture individual progression pattern, filling in the gaps between sparse imaging data available. A deep predictive neural network is then built to exploit spatiotemporal dynamics, link neurological examinations with clinical profiles, and generate subtype assignment probability on an individual basis. We further identify an evolutionary disease graph to quantify subtype transition probabilities through extensive simulations. Our stratification achieves superior performance in both inter-cluster heterogeneity and intra-cluster homogeneity of various clinical scores. Applying our approach to enriched samples of aging populations, we identify six subtypes spanning AD spectrum, where each subtype exhibits a distinctive biomarker pattern that is consistent with its clinical outcome. PSSN provides insights into pre-symptomatic diagnosis and practical guidance on clinical treatments, which may be further generalized to other neurodegenerative diseases.

q-bio.QM↗

arXiv2023

Uncovering the System Vulnerability and Criticality of Human Brain under Dynamical Neuropathological Events in Alzheimer's Disease

Background: Despite the striking efforts in investigating neurobiological factors behind the acquisition of amyloid-\b{eta} (A), protein tau (T), and neurodegeneration ([N]) biomarkers, the mechanistic pathways of how AT[N] biomarkers spreading throughout the brain remain elusive. Objectives: To disentangle the massive heterogeneities in AD progressions and identify vulnerable/critical brain regions to AD pathology. Methods: In this work, we characterized the interaction of AT[N] biomarkers and their propagation across brain networks using a novel bistable reaction-diffusion model, which allows us to establish a new systems biology underpinning of Alzheimer's disease (AD) progression. We applied our model to large-scale longitudinal neuroimages from the ADNI database and studied the systematic vulnerability and criticality of brains. Results: Our model yields long term prediction that is statistically significant linear correlated with temporal imaging data, produces clinically consistent risk prediction, and captures the Braak-like spreading pattern of AT[N] biomarkers in AD development. Conclusion: Our major findings include (i) tau is a stronger indicator of regional risk compared to amyloid, (ii) temporal lobe exhibits higher vulnerability to AD-related pathologies, (iii) proposed critical brain regions outperform hub nodes in transmitting disease factors across the brain, and (iv) comparing the spread of neuropathological burdens caused by amyloid-\b{eta} and tau diffusions, disruption of metabolic balance is the most determinant factor contributing to the initiation and progression of Alzheimer's disease.

q-bio.QM↗

arXiv2023

Optimization Based Motion Planning for Multi-Limbed Vertical Climbing Robots

Motion planning trajectories for a multi-limbed robot to climb up walls requires a unique combination of constraints on torque, contact force, and posture. This paper focuses on motion planning for one particular setup wherein a six-legged robot braces itself between two vertical walls and climbs vertically with end effectors that only use friction. Instead of motion planning with a single nonlinear programming (NLP) solver, we decoupled the problem into two parts with distinct physical meaning: torso postures and contact forces. The first part can be formulated as either a mixed-integer convex programming (MICP) or NLP problem, while the second part is formulated as a series of standard convex optimization problems. Variants of the two wall climbing problem e.g., obstacle avoidance, uneven surfaces, and angled walls, help verify the proposed method in simulation and experimentation.

cs.RO↗

arXiv2023

A close-in giant planet escapes engulfment by its star

When main-sequence stars expand into red giants, they are expected to engulf close-in planets. Until now, the absence of planets with short orbital periods around post-expansion, core-helium-burning red giants has been interpreted as evidence that short-period planets around Sun-like stars do not survive the giant expansion phase of their host stars. Here we present the discovery that the giant planet 8 Ursae Minoris b orbits a core-helium-burning red giant. At a distance of only 0.5 au from its host star, the planet would have been engulfed by its host star, which is predicted by standard single-star evolution to have previously expanded to a radius of 0.7 au. Given the brief lifetime of helium-burning giants, the nearly circular orbit of the planet is challenging to reconcile with scenarios in which the planet survives by having a distant orbit initially. Instead, the planet may have avoided engulfment through a stellar merger that either altered the evolution of the host star or produced 8 Ursae Minoris b as a second-generation planet. This system shows that core-helium-burning red giants can harbour close planets and provides evidence for the role of non-canonical stellar evolution in the extended survival of late-stage exoplanetary systems.

astro-ph.SR↗

arXiv2023

Design of a Jumping Control Framework with Heuristic Landing for Bipedal Robots

Generating dynamic jumping motions on legged robots remains a challenging control problem as the full flight phase and large landing impact are expected. Compared to quadrupedal robots or other multi-legged robots, bipedal robots place higher requirements for the control strategy given a much smaller footprint. To solve this problem, a novel heuristic landing planner is proposed in this paper. With the momentum feedback during the flight phase, landing locations can be updated to minimize the influence of uncertainties from tracking errors or external disturbances when landing. To the best of our knowledge, this is the first approach to take advantage of the flight phase to reduce the impact of the jump landing which is implemented in the actual robot. By integrating it with a modified kino-dynamics motion planner with centroidal momentum and a low-level controller which explores the whole-body dynamics to hierarchically handle multiple tasks, a complete and versatile jumping control framework is designed in this paper. Extensive results of simulation and hardware jumping experiments on a miniature bipedal robot with proprioceptive actuation are provided to demonstrate that the proposed framework is able to achieve human-like efficient and robust jumping tasks, including directional jump, twisting jump, step jump, and somersaults.

cs.RO↗

arXiv2023

Valley-dependent topological edge states in plasma photonic crystals

Plasma photonic crystals designed in this paper are composed of gas discharge tubes to control the flow of electromagnetic waves. The band structures calculated by the finite element method are consistent with the experimental results in that two distinct attenuation peaks appear in the ranges of 1 GHz to 2.5 GHz and 5 GHz to 6 GHz. Electromagnetic parameters of the plasma are extracted by the Nicolson-Ross-Weir method and effective medium theory. The measured electron density is between 1e11 cm-3 and 1e12 cm-3, which verifies the correctness of the parameter used in the simulation, and the collision frequency is near 1.5e10 Hz. As the band structures are corroborated by the measured scattering parameters, we introduce the concept of photonic topological insulator based on the quantum Valley Hall effect into the plasma photonic crystal. A valley-dependent plasma photonic crystal with hexagonal lattice is constructed, and the phase transition of the valley K(K') occurs by breaking the spatial inversion symmetry. Valley-spin locked topological edge states are generated and excited by chiral sources. The frequency of the non-bulk state can be dynamically regulated by the electron density. This concept paves the way for novel, tunable topological edge states. More interestingly, the Dirac cone is broken when the electron density increases to 3.1e12 cm-3, which distinguishes from the methods of applying a magnetic field and changing the symmetry of the point group.

physics.plasm-ph↗

arXiv2023

Causal Bandits: Online Decision-Making in Endogenous Settings

The deployment of Multi-Armed Bandits (MAB) has become commonplace in many economic applications. However, regret guarantees for even state-of-the-art linear bandit algorithms (such as Optimism in the Face of Uncertainty Linear bandit (OFUL)) make strong exogeneity assumptions w.r.t. arm covariates. This assumption is very often violated in many economic contexts and using such algorithms can lead to sub-optimal decisions. Further, in social science analysis, it is also important to understand the asymptotic distribution of estimated parameters. To this end, in this paper, we consider the problem of online learning in linear stochastic contextual bandit problems with endogenous covariates. We propose an algorithm we term $ε$-BanditIV, that uses instrumental variables to correct for this bias, and prove an $\tilde{\mathcal{O}}(k\sqrt{T})$ upper bound for the expected regret of the algorithm. Further, we demonstrate the asymptotic consistency and normality of the $ε$-BanditIV estimator. We carry out extensive Monte Carlo simulations to demonstrate the performance of our algorithms compared to other methods. We show that $ε$-BanditIV significantly outperforms other existing methods in endogenous settings. Finally, we use data from real-time bidding (RTB) system to demonstrate how $ε$-BanditIV can be used to estimate the causal impact of advertising in such settings and compare its performance with other existing methods.

econ.EM↗

arXiv2023

Revising Properties of Planet-Host Binary Systems. III. There is No Observed Radius Gap For Kepler Planets in Binary Star Systems

Binary stars are ubiquitous; the majority of solar-type stars exist in binaries. Exoplanet occurrence rate is suppressed in binaries, but some multiples do still host planets. Binaries cause observational biases in planet parameters, with undetected multiplicity causing transiting planets to appear smaller than they truly are. We have analyzed the properties of a sample of 119 planet-host binary stars from the Kepler mission to study the underlying population of planets in binaries that fall in and around the radius valley, which is a demographic feature in period-radius space that marks the transition from predominantly rocky to predominantly gaseous planets. We found no statistically significant evidence for a radius gap for our sample of 122 planets in binaries when assuming the primary stars are the planet hosts, with a low probability ($p < 0.05$) of the binary planet sample radius distribution being consistent with the single-star small planet population via an Anderson-Darling test. These results reveal demographic differences in the planet size distribution between planets in binary and single stars for the first time, showing that stellar multiplicity may fundamentally alter the planet formation process. A larger sample and further assessment of circumprimary versus circumsecondary transits is needed to either validate this non-detection or explore other scenarios, such as a radius gap with a location that is dependent on binary separation.

astro-ph.EP↗

arXiv2023

Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Tasks

Dialogue systems capable of social influence such as persuasion, negotiation, and therapy, are essential for extending the use of technology to numerous realistic scenarios. However, existing research primarily focuses on either task-oriented or open-domain scenarios, a categorization that has been inadequate for capturing influence skills systematically. There exists no formal definition or category for dialogue systems with these skills and data-driven efforts in this direction are highly limited. In this work, we formally define and introduce the category of social influence dialogue systems that influence users' cognitive and emotional responses, leading to changes in thoughts, opinions, and behaviors through natural conversations. We present a survey of various tasks, datasets, and methods, compiling the progress across seven diverse domains. We discuss the commonalities and differences between the examined systems, identify limitations, and recommend future directions. This study serves as a comprehensive reference for social influence dialogue systems to inspire more dedicated research and discussion in this emerging area.

cs.CL↗