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Ding Wang

Publications and source records attributed to Ding Wang.

At least 109 records · Page 6Linked to original sources
arXiv2022

How Platform-User Power Relations Shape Algorithmic Accountability: A Case Study of Instant Loan Platforms and Financially Stressed Users in India

Accountability, a requisite for responsible AI, can be facilitated through transparency mechanisms such as audits and explainability. However, prior work suggests that the success of these mechanisms may be limited to Global North contexts; understanding the limitations of current interventions in varied socio-political conditions is crucial to help policymakers facilitate wider accountability. To do so, we examined the mediation of accountability in the existing interactions between vulnerable users and a 'high-risk' AI system in a Global South setting. We report on a qualitative study with 29 financially-stressed users of instant loan platforms in India. We found that users experienced intense feelings of indebtedness for the 'boon' of instant loans, and perceived huge obligations towards loan platforms. Users fulfilled obligations by accepting harsh terms and conditions, over-sharing sensitive data, and paying high fees to unknown and unverified lenders. Users demonstrated a dependence on loan platforms by persisting with such behaviors despite risks of harms such as abuse, recurring debts, discrimination, privacy harms, and self-harm to them. Instead of being enraged with loan platforms, users assumed responsibility for their negative experiences, thus releasing the high-powered loan platforms from accountability obligations. We argue that accountability is shaped by platform-user power relations, and urge caution to policymakers in adopting a purely technical approach to fostering algorithmic accountability. Instead, we call for situated interventions that enhance agency of users, enable meaningful transparency, reconfigure designer-user relations, and prompt a critical reflection in practitioners towards wider accountability. We conclude with implications for responsibly deploying AI in FinTech applications in India and beyond.

cs.HC↗
arXiv2022

Neural Network-based Constrained Optimal Coordination for Heterogeneous Uncertain Nonlinear Multi-agent Systems

In this paper, we investigate a constrained optimal coordination problem for a class of heterogeneous nonlinear multi-agent systems described by high-order dynamics subject to both unknown nonlinearities and external disturbances. Each agent has a private objective function and a steady-state constraint about its output. We develop a composite distributed controller for each agent by a combination of internal model and neural network. All agent outputs are proven to reach the constrained minimal point of the aggregate objective function with bounded residual errors irrespective of the unknown nonlinearities and external disturbances. Two examples are finally given to demonstrate the effectiveness of the algorithm.

math.OC↗
arXiv2022

Whose AI Dream? In search of the aspiration in data annotation

This paper present the practice of data annotation from the perspective of the annotators. Data is fundamental to ML models. This paper investigates the work practices concerning data annotation as performed in the industry, in India. Previous investigations have largely focused on annotator subjectivity, bias and efficiency. We present a wider perspective of the data annotation, following a grounded approach, we conducted three sets of interviews with 25 annotators, 10 industry experts and 12 ML practitioners. Our results show that the work of annotators is dictated by the interests, priorities and values of others above their station. More than technical, we contend that data annotation is a systematic exercise of power through organizational structure and practice. We propose a set of implications for how we can cultivate and encourage better practice to balance the tension between the need for high quality data at low cost and the annotator aspiration for well being, career perspective, and active participation in building the AI dream.

cs.HC↗
arXiv2022

Growth, Electronic Structure and Superconductivity of Ultrathin Epitaxial CoSi2 Films

We report growth, electronic structure and superconductivity of ultrathin epitaxial CoSi2 films on Si(111). At low coverages, preferred islands with 2, 5 and 6 monolayers height develop, which agrees well with the surface energy calculation. We observe clear quantum well states as a result of electronic confinement and their dispersion agrees well with density functional theory calculations, indicating weak correlation effect despite strong contributions from Co 3d electrons. Ex-situ transport measurements show that superconductivity persists down to at least 10 monolayers, with reduced Tc but largely enhanced upper critical field. Our study opens up the opportunity to study the interplay between quantum confinement, interfacial symmetry breaking and superconductivity in an epitaxial silicide film, which is technologically relevant in microelectronics.

cond-mat.mes-hall↗
arXiv2022

A reduced variational approach for searching cycles in high-dimensional systems

Searching recurrent patterns in complex systems with high-dimensional phase spaces is an important task in diverse fields. In the current work, an improved scheme is proposed to accelerate the recently designed variational approach for finding periodic orbits in systems with chaotic dynamics based on the existence of inertial manifold widely observed in various spatially extended systems, especially those with high dimensions. On the premise of keeping exponential convergence of the variational method, an effective loop evolution equation is derived to greatly reduce the storage and computing time. With repeated modification of local coordinates and evolution of the guess loop being carried out alternately, the rapid convergence and the stability of the reduction scheme are effectively achieved. The dimension of local coordinate subspaces is generally larger than the number of nonnegative Lyapunov exponents to ensure the exponential convergence. The proposed scheme is successfully demonstrated on several well-known examples and expected to supply a powerful tool in the exploration of high-dimensional nonlinear systems.

nlin.CD↗
arXiv2021

Learning Deep Neural Networks under Agnostic Corrupted Supervision

Training deep neural models in the presence of corrupted supervision is challenging as the corrupted data points may significantly impact the generalization performance. To alleviate this problem, we present an efficient robust algorithm that achieves strong guarantees without any assumption on the type of corruption and provides a unified framework for both classification and regression problems. Unlike many existing approaches that quantify the quality of the data points (e.g., based on their individual loss values), and filter them accordingly, the proposed algorithm focuses on controlling the collective impact of data points on the average gradient. Even when a corrupted data point failed to be excluded by our algorithm, the data point will have a very limited impact on the overall loss, as compared with state-of-the-art filtering methods based on loss values. Extensive experiments on multiple benchmark datasets have demonstrated the robustness of our algorithm under different types of corruption.

cs.LG↗
arXiv2021

Impact of COVID-19 behavioral inertia on reopening strategies for New York City Transit

The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing. A baseline model was previously developed and calibrated for pre-COVID conditions as MATSim-NYC. A new COVID model is calibrated that represents travel behavior during the COVID-19 pandemic by recalibrating the population agendas to include work-from-home and re-estimating the mode choice model for MATSim-NYC to fit observed traffic and transit ridership data. Assuming the change in behavior exhibits inertia during reopening, we analyze the increase in car traffic due to the phased reopen plan guided by the state government of New York. Four reopening phases and two reopening scenarios (with and without transit capacity restrictions) are analyzed. A Phase 4 reopening with 100% transit capacity may only see as much as 73% of pre-COVID ridership and an increase in the number of car trips by as much as 142% of pre-pandemic levels. Limiting transit capacity to 50% would decrease transit ridership further from 73% to 64% while increasing car trips to as much as 143% of pre-pandemic levels. While the increase appears small, the impact on consumer surplus is disproportionately large due to already increased traffic congestion. Many of the trips also get shifted to other modes like micromobility. The findings imply that a transit capacity restriction policy during reopening needs to be accompanied by (1) support for micromobility modes, particularly in non-Manhattan boroughs, and (2) congestion alleviation policies that focus on reducing traffic in Manhattan, such as cordon-based pricing.

econ.GN↗
arXiv2021

"Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment

Artificial intelligence (AI) technology has been increasingly used in the implementation of advanced Clinical Decision Support Systems (CDSS). Research demonstrated the potential usefulness of AI-powered CDSS (AI-CDSS) in clinical decision making scenarios. However, post-adoption user perception and experience remain understudied, especially in developing countries. Through observations and interviews with 22 clinicians from 6 rural clinics in China, this paper reports the various tensions between the design of an AI-CDSS system ("Brilliant Doctor") and the rural clinical context, such as the misalignment with local context and workflow, the technical limitations and usability barriers, as well as issues related to transparency and trustworthiness of AI-CDSS. Despite these tensions, all participants expressed positive attitudes toward the future of AI-CDSS, especially acting as "a doctor's AI assistant" to realize a Human-AI Collaboration future in clinical settings. Finally we draw on our findings to discuss implications for designing AI-CDSS interventions for rural clinical contexts in developing countries.

cs.HC↗
arXiv2021

Photoionization-induced broadband dispersive wave generated in an Ar-filled hollow-core photonic crystal fiber

The resonance band in hollow-core photonic crystal fiber (HC-PCF), while leading to high-loss region in the fiber transmission spectrum, has been successfully used for generating phase-matched dispersive wave (DW). Here, we report that the spectral width of the resonance-induced DW can be largely broadened due to plasma-driven blueshifting soliton. In the experiment, we observed that in a short length of Ar-filled single-ring HC-PCF the soliton self-compression and photoionization effects caused a strong spectral blueshift of the pump pulse, changing the phase-matching condition of the DW emission process. Therefore, broadening of DW spectrum to the longer-wavelength side was obtained with several spectral peaks, which correspond to the generation of DW at different positions along the fiber. In the simulation, we used super-Gauss windows with different central wavelengths to filter out these DW spectral peaks, and studied the time-domain characteristics of these peaks respectively using Fourier transform method. The simulation results verified that these multiple-peaks on the DW spectrum have different delays in the time domain, agreeing well with our theoretical prediction. Remarkably, we found that the whole time-domain DW trace can be compressed to ~29 fs using proper chirp compensation. The experimental and numerical results reported here provide some insight into the resonance-induced DW generation process in gas-filled HC-PCFs, they could also pave the way to ultrafast pulse generation using DW-emission mechanism.

physics.optics↗
arXiv2020

A validated multi-agent simulation test bed to evaluate congestion pricing policies on population segments by time-of-day in New York City

Evaluation of the demand for emerging transportation technologies and policies can vary by time of day due to spillbacks on roadways, rescheduling of travelers' activity patterns, and shifting to other modes that affect the level of congestion. These effects are not well-captured with static travel demand models. We calibrate and validate the first open-source multi-agent simulation model for New York City, called MATSim-NYC, to support agencies in evaluating policies such as congestion pricing. The simulation-based virtual test bed is loaded with an 8M+ synthetic 2016 population calibrated in a prior study. The road network is calibrated to INRIX speed data and average annual daily traffic for a screenline along the East River crossings, resulting in average speed differences of 7.2% on freeways and 17.1% on arterials, leading to average difference of +1.8% from the East River screenline. Validation against transit stations shows an 8% difference from observed counts and median difference of 29% for select road link counts. The model is used to evaluate a congestion pricing plan proposed by the Regional Plan Association and suggests a much higher (127K) car trip reduction compared to their report (59K). The pricing policy would impact the population segment making trips within Manhattan differently from the population segment of trips outside Manhattan. The multiagent simulation can show that 37.3% of the Manhattan segment would be negatively impacted by the pricing compared to 39.9% of the non-Manhattan segment, which has implications for redistribution of congestion pricing revenues. The citywide travel consumer surplus decreases when the congestion pricing goes up from $9.18 to $14 both ways even as it increases for the Charging-related population segment. This implies that increasing pricing from $9.18 to $14 benefits Manhattanites at the expense of the rest of the city.

physics.soc-ph↗
arXiv2020

Accelerating the computation of quantum brachistochrone

Efficient control of qubits plays a key role in quantum information processing. In the current work, an alternative set of differential equations are derived for an optimal quantum control of single or multiple qubits with or without interaction. The new formulation enables a great reduction of the computation load by eliminating redundant complexity involved in previous formulations. A relaxation technique is designed for numerically detecting optimal paths involving entanglement. Interesting continuous symmetries are identified in the Lagrangian, which indicates the existence of physically equivalent classes of paths and may be utilized to remove neutral directions in the Jacobian of the evolution. In the 'ground state' solution among the set of optimal paths, the time-reversal symmetry of the system shows up, which is expected to be universal for the symmetry-related initial and final state.

quant-ph↗
arXiv2020

Agent-based Simulation Model and Deep Learning Techniques to Evaluate and Predict Transportation Trends around COVID-19

The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing. This edition of the white paper updates travel trends and highlights an agent-based simulation model's results to predict the impact of proposed phased reopening strategies. It also introduces a real-time video processing method to measure social distancing through cameras on city streets.

cs.MA↗
arXiv2020

Single-scan, dual-functional interferometer for fast spatiotemporal characterization of few-cycle pulses

Accurate and fast characterization of spatiotemporal information of high-intensity, ultrashort pulses is crucial in the field of strong-field laser science and technology. While conventional self-referenced interferometers were widely used to retrieve the spatial profile of the relative spectral phase of pulses, additional measurements of temporal and spectral information at a particular position of the laser beam were, however, necessary to remove the indeterminacy, which increases the system complexity. Here we report an advanced, dual-functional interferometer that is able to reconstruct the complete spatiotemporal information of ultrashort pulses with a single scan of the interferometer arm. The set-up integrates an interferometric frequency-resolved optical gating (FROG) with a radial shearing Michelson interferometer. Trough scanning one arm of the interferometer, both cross-correlated FROG trace at the central part of the laser beam and delay-dependent interferograms of the entire laser profile are simultaneously obtained, allowing a fast 3-dimensional reconstruction of few-cycle laser pulses.

physics.optics↗
arXiv2020

Photoionization-assisted, high-efficiency emission of dispersive wave in gas-filled hollow-core photonic crystal fibers

We demonstrate that the phase-matched dispersive wave (DW) emission within the resonance band of a 25-cm-long gas-filled hollow-core photonic crystal fiber (HC-PCF) can be strongly enhanced by the photoionization effect of the pump pulse. In the experiments we observe that as the pulse energy increases, the pump pulse gradually shifts to shorter wavelengths due to soliton-plasma interactions. When the central wavelength of the blueshifting soliton is close to the resonance band of the HC-PCF, high-efficiency energy transfer from the pump light to the DW in the visible region can be obtained. During this DW emission process, we also observe that the spectral center of the DW gradually shifts to longer wavelengths leading to a slightly-increased DW bandwidth, which can be well explained as the consequence of phase-matched coupling between the pump pulse and the DW. In particular, at an input pulse energy of 6 uJ, the spectral ratio of the DW at the fiber output is measured to be as high as ~53% together with a conversion efficiency of ~19%. These experimental results, explained by numerical simulations, pave the way to high-brightness light sources based on high-efficiency frequency-upconversion processes in gas-filled HC-PCFs.

physics.optics↗
arXiv2019

Handshake between Fibonacci series and pure preferential attachment mechanism on a graph-model

In order to better understand dynamical functions on amounts of natural and man-made complex systems, lots of researchers from a wide range of disciplines, covering statistic physics, mathematics, theoretical computer science, and so on, have spent much time in doing this intriguing study. In this paper, the discussed popularly topic, how to construct reasonable graph-model and then to explain many features of realistic networks using previously presented theoretical models, is still our main work. Compared with many pre-existing deterministic graph-model in single evolution way, our new graph-model can be constructed using three types of growth ways to meet preferential attachment mechanism. Meanwhile several typical indices associated with network research will be reported. In addition, some interesting findings will be shown, including the first handshake between Fibonacci series and "pure" preferential attachment mechanism, an obvious relationship connecting two well-known rules, power-law and Zipf-law, and a common but useful equation on the basis of both spanning trees number and the number of spanning trees with maximum leaves. Based on these foregoing discussions, we can demonstrate that our graph-model obeys power-law and small-world property. For the future research directions, we present some unknown problems to be studied at the end of this paper.

cs.SI↗
arXiv2017

2.6mJ/100Hz CEP stable near-single-cycle 4μm laser based on OPCPA and hollow-core-fiber compression

A carrier envelope phase stable near-single cycle mid-infrared laser based on optical parametric chirped pulse amplification and hollow-core-fiber compression is demonstrated. 4 μm laser pulses with 11.8 mJ energy are delivered from a KTA based OPCPA with 100 Hz repetition rate, and compressed to be ~105 fs by a two-grating compressor with efficiency over 50%. Subsequently, the pulse spectrum is broadened by employing a krypton gas-filled hollow-core-fiber (HCF). Then, the pulse duration is further compressed to 21.5 fs through a CaF2 bulk material with energy of 2.6 mJ and stability of 0.9% RMS, which is about 1.6 cycle for 4 μm laser pulse. The near-single cycle 4 μm laser pulse CEP is passively stabilized with ~370 mrad based on a CEP stable 4 μm OPA injection.

physics.optics↗
arXiv2017

Aztec: A Platform to Render Biomedical Software Findable, Accessible, Interoperable, and Reusable

Precision medicine and health requires the characterization and phenotyping of biological systems and patient datasets using a variety of data formats. This scenario mandates the centralization of various tools and resources in a unified platform to render them Findable, Accessible, Interoperable, and Reusable (FAIR Principles). Leveraging these principles, Aztec provides the scientific community with a new platform that promotes a long-term, sustainable ecosystem of biomedical research software. Aztec is available at https://aztec.bio and its source code is hosted at https://github.com/BD2K-Aztec.

cs.DL↗