Explicit gain equations for hybrid graphene-quantum-dot photodetectors

Graphene is an attractive material for broadband photodetection but suffers from weak light absorption. Coating graphene with quantum dots can significantly enhance light absorption and create extraordinarily high photo gain. This high gain is often explained by the classical gain theory which is unfortunately an implicit function and may even be questionable. In this work, we managed to derive explicit gain equations for hybrid graphene-quantum-dot photodetectors. Due to the work function mismatch, lead sulfide (PbS) quantum dots coated on graphene will form a surface depletion region near the interface of quantum dots and graphene. Light illumination narrows down the surface depletion region, creating a photovoltage that gates the graphene. As a result, high photo gain in graphene is observed. The explicit gain equations are derived from the theoretical gate transfer characteristics of graphene and the correlation of the photovoltage with the light illumination intensity. The derived explicit gain equations fit well with the experimental data, from which physical parameters are extracted.Comment: 14 pages, 6 figure

Result Diversification in Search and Recommendation: A Survey

Diversifying return results is an important research topic in retrieval systems in order to satisfy both the various interests of customers and the equal market exposure of providers. There has been growing attention on diversity-aware research during recent years, accompanied by a proliferation of literature on methods to promote diversity in search and recommendation. However, diversity-aware studies in retrieval systems lack a systematic organization and are rather fragmented. In this survey, we are the first to propose a unified taxonomy for classifying the metrics and approaches of diversification in both search and recommendation, which are two of the most extensively researched fields of retrieval systems. We begin the survey with a brief discussion of why diversity is important in retrieval systems, followed by a summary of the various diversity concerns in search and recommendation, highlighting their relationship and differences. For the survey's main body, we present a unified taxonomy of diversification metrics and approaches in retrieval systems, from both the search and recommendation perspectives. In the later part of the survey, we discuss the open research questions of diversity-aware research in search and recommendation in an effort to inspire future innovations and encourage the implementation of diversity in real-world systems.Comment: 20 page

Towards Benchmarking GUI Compatibility Testing on Mobile Applications

GUI is a bridge connecting user and application. Existing GUI testing tasks can be categorized into two groups: functionality testing and compatibility testing. While the functionality testing focuses on detecting application runtime bugs, the compatibility testing aims at detecting bugs resulting from device or platform difference. To automate testing procedures and improve testing efficiency, previous works have proposed dozens of tools. To evaluate these tools, in functionality testing, researchers have published testing benchmarks. Comparatively, in compatibility testing, the question of ``Do existing methods indeed effectively assist test cases replay?'' is not well answered. To answer this question and advance the related research in GUI compatibility testing, we propose a benchmark of GUI compatibility testing. In our experiments, we compare the replay success rate of existing tools. Based on the experimental results, we summarize causes which may lead to ineffectiveness in test case replay and propose opportunities for improving the state-of-the-art

Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network

Deep sparse networks are widely investigated as a neural network architecture for prediction tasks with high-dimensional sparse features, with which feature interaction selection is a critical component. While previous methods primarily focus on how to search feature interaction in a coarse-grained space, less attention has been given to a finer granularity. In this work, we introduce a hybrid-grained feature interaction selection approach that targets both feature field and feature value for deep sparse networks. To explore such expansive space, we propose a decomposed space which is calculated on the fly. We then develop a selection algorithm called OptFeature, which efficiently selects the feature interaction from both the feature field and the feature value simultaneously. Results from experiments on three large real-world benchmark datasets demonstrate that OptFeature performs well in terms of accuracy and efficiency. Additional studies support the feasibility of our method.Comment: NeurIPS 2023 poste

Electrochemically active organic thin films

The objective of this study is to gain an understanding of the factors which influence and control the formation of self-assembled monolayers (SAMs) chemisorbed on metal surfaces and how these SAMs modulate electron transfer kinetics. To achieve this purpose various alkyl thiol-SAMs with chain lengths from C4SH to C22SH and terminal functional groups such as -CO2H, -OH, -NH2, and -SO3- were prepared on gold surfaces.The electron transfer processes across thiol-SAMs have been extensively studied by electrochemical techniques including cyclic voltammetry and rotating disk electrode (RDE) voltammetry. RDE results indicate that a thiol monolayer greatly reduces the electron-transfer rate at the interface. From the current versus concentration relationships of several redox probes including K 3Fe(CN)6, Ru(NH3)6Cl3, (Bu 4N)3Fe(CN)6, and Co(bpy)Cl2, a partition mechanism is proposed. This mechanism accounts for the observation of saturation in current/concentration plots. Simulations of a pinhole/defects model and the partition model show that saturation in current/concentration curves occurs only in the partition model.Studies show that short-chain thiols adsorbed on gold surfaces can lower the interfacial energy and promote aniline polymerization. Long chain thiol SAMs do not block aniline polymerization. A homogeneous polyaniline film was proposed to be formed in, and on, long-chain thiol SAMs. Such a polyaniline exhibits better conductivity than that of polyaniline formed on bare gold.The voltammetric behavior of dopamine, ascorbic acid, and their mixture has been described at HSC11CO2H-modified Au electrodes. Complex redox processes involving the thiol-Au bond evident only under high sweep rate conditions were found. Depending upon such redox processes, dopamine at levels as low as 1nM can be detected. Such sensitivity is essential for the in vivo application of the o-carboxylic acid thiol SAM/Au electrode.The adsorption process of the thiol monolayer was showed to be electrochemically accessible. Control of electrode potential during the adsorption process apparently assists thiol self-assembly. Potential control allows for the formation of monolayers in a relatively short time and is more likely to be defect free than monolayers prepared under passive adsorption conditions. The potential deposition process allows one to control the ratio of components in a C 16SH/HSC15CO2H mixed monolayer

DeepGemini: Verifying Dependency Fairness for Deep Neural Network

Deep neural networks (DNNs) have been widely adopted in many decision-making industrial applications. Their fairness issues, i.e., whether there exist unintended biases in the DNN, receive much attention and become critical concerns, which can directly cause negative impacts in our daily life and potentially undermine the fairness of our society, especially with their increasing deployment at an unprecedented speed. Recently, some early attempts have been made to provide fairness assurance of DNNs, such as fairness testing, which aims at finding discriminatory samples empirically, and fairness certification, which develops sound but not complete analysis to certify the fairness of DNNs. Nevertheless, how to formally compute discriminatory samples and fairness scores (i.e., the percentage of fair input space), is still largely uninvestigated. In this paper, we propose DeepGemini, a novel fairness formal analysis technique for DNNs, which contains two key components: discriminatory sample discovery and fairness score computation. To uncover discriminatory samples, we encode the fairness of DNNs as safety properties and search for discriminatory samples by means of state-of-the-art verification techniques for DNNs. This reduction enables us to be the first to formally compute discriminatory samples. To compute the fairness score, we develop counterexample guided fairness analysis, which utilizes four heuristics to efficiently approximate a lower bound of fairness score. Extensive experimental evaluations demonstrate the effectiveness and efficiency of DeepGemini on commonly-used benchmarks, and DeepGemini outperforms state-of-the-art DNN fairness certification approaches in terms of both efficiency and scalability

An extended Levinson-Durbin algorithm and its application in mixed excitation linear prediction

Ten order all-pole model is used in the 2400 bit/second mixed excitation linear prediction to describe human vocal tract. Traditional Levinson-Durbin algorithm is one of the methods to solve the Yule-Walker equations conducted by the ten order linear prediction model. Taking the iteration step of traditional Levinson-Durbin algorithm as 1, an extended algorithm with any positive integer iteration step which is no larger than the order of Teoplitz matrix is proposed. The extended algorithm considers interaction between the adjacent subtracts. A hybrid algorithm of the extended algorithm and traditional algorithm has been applied to solve the 2400 bit/second mixed excitation linear prediction under some conditions. The perceptual evaluation of speech quality mean opinion score of nasal syllable is improved in some degree

Quantitative estimates of collective geo-tagged human activities in response to typhoon Hato using location-aware big data

Location-aware big data from social media have been widely used to quantitatively characterize natural disasters and disaster-induced losses. It is not clear how human activities collectively respond to a disaster. In this study, we examined the collective human activities in response to Typhoon Hato at multi spatial scales using aggregated location request data. We proposed a Multilevel Abrupt Changes Detection (MACD) methodological framework to detect and characterize the abrupt changes in location requests in response to Typhoon Hato. Results show that, at the grid level, most anomaly grids were located within a radius of 53 km around the typhoon trajectory. At the city level, there are significant spatial difference in terms of the human activity recovery duration (230 h on average). At the subnational level, the absolute magnitude of abrupt location request changes is strongly correlated with the typhoon-induced economic losses and the population affected