Miracle-dz/UniFAR-FTUs huggingface dataset:
CMOS Capacitive Fingerprint Sensor Based on Differential Sensing Circuit with Noise Cancellation

In this paper, we introduce a differential sensing technique for CMOS capacitive fingerprint detection. It employs a new capacitive-sensing cell structure with charge sharing detection and readout circuit. The proposed technique also can eliminate the effect of parasitic capacitances by employing parasitic insensitive switched-capacitor structure and so increases the sensitivity even under severe noisy conditions. It can also overcome the performance degradation caused by various conditions of finger surface by using a differential integrator and adjusting its number of integrations. In addition, the proposed architecture allows parallel detection of all sensing channels. It can, therefore, substantially speed up the detection process compared with conventional architectures. We implemented a prototype fingerprint sensor chip with an array of 20 × 16 sensor cells using a 130 nm CMOS process. Simulation experiments demonstrated that the proposed architecture provided an SNR gain of 54 dB, whereas a conventional single line sensing gives an SNR gain of only 13 dB.
Learning to Explain Ambiguous Headlines of Online News

With the purpose of attracting clicks, online news publishers and editors use diverse strategies to make their headlines catchy, with a sacrifice of accuracy. Specifically, a considerable portion of news headlines is ambiguous. Such headlines are unclear relative to the content of the story, and largely degrade the reading experience of the audience. In this paper, we focus on dealing with the information gap caused by the ambiguous news headlines. We define a new task of explaining ambiguous headlines with short informative texts, and build a benchmark dataset for evaluation. We address the task by selecting a proper sentence from the news body to resolve the ambiguity in an ambiguous headline. Both feature engineering methods and neural network methods are explored. For feature engineering, we improve a standard SVM classifier with elaborately designed features. For neural networks, we propose an ambiguity-aware neural matching model based on a previous model. Utilizing automatic and manual evaluation metrics, we demonstrate the efficacy and the complementarity of the two methods, and the ambiguity-aware neural matching model achieves the state-of-the-art performance on this challenging task.
Graph-Based Rate Control in Pathology Imaging With Lossless Region of Interest Coding

The increasing availability of digital pathology images has motivated the design of tools to foster multidisciplinary collaboration among researchers, pathologists, and computer scientists. Telepathology plays an important role in the development of collaborative tools, as it facilitates the transmission and access to pathology images by multiple users. However, the huge file size associated with pathology images usually prevents full exploitation of the collaborative telepathology system potential. Within this context, rate control (RC) is an important tool that allows meeting storage and bandwidth requirements by controlling the bit rate of the coded image. In this paper, we propose a novel graph-based RC algorithm with lossless region of interest (RoI) coding for pathology images. The algorithm, which is designed for block-based predictive transform coding methods, compresses the non-RoI in a lossy manner according to a target bit rate and the RoI in a lossless manner. It employs a graph where each node represents a constituent block of the image to be coded. By incorporating information about the coding cost similarities of blocks into the graph, a graph kernel is used to distribute a target bit budget among the non-RoI blocks. In order to increase RC accuracy, the algorithm uses a rate-lambda (R- $\lambda $ ) model to approximate the slope of the rate-distortion curve of the non-RoI, and a graph-based approach to guarantee that the target bit rate is accurately attained. The algorithm is implemented in the High-Efficiency Video Coding standard and tested over a wide range of pathology images with multiple RoIs. Evaluation results show that it outperforms the other state-of-the-art methods designed for single images by very accurately attaining the target bit rate of the non-RoI.
Listening in noise training in children with auditory processing disorder: exploring group and individual data

Abstract Purpose: The purpose of this study was to examine the impact of auditory training in noise on auditory behaviors and life habits in children with auditory processing disorder. Methods: Ten children with auditory processing disorder underwent an auditory training program in noise and six children with auditory processing disorder comprised a control group. Before and after training, participants were tested on sentence identification in noise and auditory evoked late latency responses. Participants teachers completed two questionnaires on children’s auditory behaviors and life habits. Results: Participants were more tolerant to noise as the training sessions progressed. Significant between-group differences were found in P1 and N2 latency measures, independent of measurement time. The observed data trends suggest that some participants improved their performance on the sentence identification task in noise as well as on some electrophysiological parameters. No significant differences in questionnaire scores were found between groups or measurement times. However, one questionnaire showed significant between-group differences for certain questions. Conclusions: Listening in noise can improve with training for children with auditory processing disorder. However, this training program might be beneficial for some, but not all, children with auditory processing disorder. More data are needed to verify individual data trends. Implication for rehabilitation A structured program was developed to improve the ability of children with auditory processing disorder to listen in noise. Intervention can be beneficial for improving auditory behaviors in some children with auditory processing disorder. A limited number of questions on children’s auditory behaviors asked to teachers appears to be more sensitive to intervention-related improvement compared to questions on life habits.
A Parametric Targetability Evaluation Approach for Vitiligo Proteome Extracted through Integration of Gene Ontologies and Protein Interaction Topologies

Vitiligo is a well-known skin disorder with complex etiology. Vitiligo pathogenesis is multifaceted with many ramifications. A computational systemic path was designed to first propose candidate disease proteins by merging properties from protein interaction networks and gene ontology terms. All in all, 109 proteins were identified and suggested to be involved in the onset of disease or its progression. Later, a composite approach was employed to prioritize vitiligo disease proteins by comparing and benchmarking the properties against standard target identification criteria. This includes sequence-based, structural, functional, essentiality, protein-protein interaction, vulnerability, secretability, assayability, and druggability information. The existing information was seamlessly integrated into efficient pipelines to propose a novel protocol for assessment of targetability of disease proteins. Using the online data resources and the scripting, an illustrative list of 68 potential drug targets was generated for vitiligo. While this list is broadly consistent with the research community's current interest in certain specific proteins, and suggests novel target candidates that may merit further study, it can still be modified to correspond to a user—specific environment, either by adjusting the weights for chosen criteria (i.e., a quantitative approach) or by changing the considered criteria (i.e., a qualitative approach).
Computer Simulation and Generation of Moving Sand Pictures

Moving sand pictures are interesting devices that can be used to generate an infinite number of unique scenes when repeatedly being flipped over. However, little work has been done on attempting to simulate the process of picture formulation. In this paper, we present an approach capable of generating images in the style of moving sand pictures. Our system defines moving sand pictures in a few steps, such as initialization, segmentation and physical simulation, so that a variety of moving sand pictures including mountain ridges, desert, clouds and even regular patterns can be generated by either automatic or semi-automatic via interaction during initialization and segmentation. Potential applications of our approach range from advertisements, posters, post cards, packaging, to digital arts.
Solving Partial Least Squares Regression via Manifold Optimization Approaches

Partial least squares regression (PLSR) has been a popular technique to explore the linear relationship between two data sets. However, all existing approaches often optimize a PLSR model in Euclidean space and take a successive strategy to calculate all the factors one by one for keeping the mutually orthogonal PLSR factors. Thus, a suboptimal solution is often generated. To overcome the shortcoming, this paper takes statistically inspired modification of PLSR (SIMPLSR) as a representative of PLSR, proposes a novel approach to transform SIMPLSR into optimization problems on Riemannian manifolds, and develops corresponding optimization algorithms. These algorithms can calculate all the PLSR factors simultaneously to avoid any suboptimal solutions. Moreover, we propose sparse SIMPLSR on Riemannian manifolds, which is simple and intuitive. A number of experiments on classification problems have demonstrated that the proposed models and algorithms can get lower classification error rates compared with other linear regression methods in Euclidean space. We have made the experimental code public at https://github.com/Haoran2014.
Cross-Frequency Transfer Entropy Characterize Coupling of Interacting Nonlinear Oscillators in Complex Systems

The purpose of this study is to introduce a method in quantifying cross-frequency information transfer to characterize directional couplers between irregular oscillations in complex systems. Importantly, the method should be able to reflect the intrinsic mechanism of interacting oscillations faithfully. Six types of interacting oscillators, including phase-amplitude, amplitude–amplitude, and component-amplitude cross-frequency transfer entropy as well as their inverse transfer entropies, are within our scope in untangling the brain connectivity. Challenges with nonlinear and nonstationary patterns are designed to validate the robustness of the proposed method. We suggest this approach could be effective in identifying driving and responding elements of interacting oscillators across different time scales. Meanwhile, an atlas of interacting oscillators in sleep is constructed. High-frequency amplitude can inversely drive low-frequency phase stronger than the standard phase-amplitude coupling, and the low-frequency amplitude can be the driving force to the high-frequency amplitude in addition to the low-frequency phase. Unlike the standard phase-amplitude coupling, the proposed cross-frequency transfer entropy is applicable to quantify the interactions across phases, amplitudes, or even the components without methodological adjustments. Meanwhile, the exploration of causal relationship enables the identification of the driving force of information flow.
Visualising the Dynamics of Character Networks

The character network of a given narrative (novel, play, film, graphic novel, etc.) models the structure formed by the relations in its character-system (Woloch, 2003). A relation between two characters symbolises their co-presence in parts of the narrative; the entire set of relations between all characters constitutes a formal model of this character-system and lends itself to display and analysis. For example, Moretti (2011) used network modelling to compare the importance of protagonists from Shakespeare’s Hamlet, while Trilcke et al. (2015) created character networks for 465 German plays and used them to initiate a wider study of German Theatre. Most applications of character network analysis have disregarded temporality, possibly because of its representational complexity. Consequently, all relations in the system are considered as happening at the same time: one cannot distinguish if a given edge symbolises a relation at the start, at the end, or in several parts of the work under study. Furthermore, because temporality is not being accounted for, there is usually no way of relating the network visualisation with the unfolding of the source narrative. While prototypes such as those discussed in Roberts-Smith et al. (2013) offer sophisticate ways of dynamically visualising the text of theatre plays, they do so in a way that is unrelated to character network modelling. Based on these observations, we set out to develop an open source web application which models the character-system of theatre plays as a sequence of network states synchronised with the actual narrative content (https://github.com/maladesimaginaires/intnetviz). This paper proposes a high-level overview of our application , successively focusing on the underlying structure extraction process, the conception of the graphical interface, and the range of uses envisioned for it. In the conclusion, we evoke the ways in which we intend to develop it and reflect on the potential significance of this development at a more epistemological level.
Benefits and Limitations of the Record and Replay Approach for GNSS Receiver Performance Assessment in Harsh Scenarios

Global navigation satellite systems play a significant role in the development of intelligent transport systems, where the estimation of the vehicle’s position is a key element. However, in strongly constrained environments such as city centers, the definition of quality metrics and the assessment of positioning performances are challenges to be addressed. Due to the variability of different urban scenarios, the modeling of the dynamics as well as the architecture of the positioning platform, which might embed other sensors and aiding means to the GNSS unit, make it hard to define unambiguous positioning metrics. Performance assessment through analytical models and simulators can be ineffective in terms of cost, complexity, and general validity and scalability of the results. This paper shows how a record and replay approach can be an efficient solution to grant fidelity to a realistic scenario. This work discusses advantages and disadvantages with emphasis on the case study of harsh scenarios. Such an approach requires proper data collections that allow the replay phase to test the GNSS-based positioning terminals. This paper presents the results obtained on a set of field tests related to different scenarios, selected as representative for the key performance indicators assessment.
Metric Learning for Multi-Output Tasks

Multi-output learning with the task of simultaneously predicting multiple outputs for an input has increasingly attracted interest from researchers due to its wide application. The <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><inline-graphic xlink:href="liu-ieq1-2794976.gif"/></alternatives></inline-formula> nearest neighbor (<inline-formula><tex-math notation="LaTeX">$k \text{NN}$</tex-math><alternatives><inline-graphic xlink:href="liu-ieq2-2794976.gif"/></alternatives></inline-formula>) algorithm is one of the most popular frameworks for handling multi-output problems. The performance of <inline-formula><tex-math notation="LaTeX">$k \text{NN}$</tex-math><alternatives><inline-graphic xlink:href="liu-ieq3-2794976.gif"/></alternatives></inline-formula> depends crucially on the metric used to compute the distance between different instances. However, our experiment results show that the existing advanced metric learning technique cannot provide an appropriate distance metric for multi-output tasks. This paper systematically studies how to efficiently learn an appropriate distance metric for multi-output problems with provable guarantee. In particular, we present a novel large margin metric learning paradigm for multi-output tasks, which projects both the input and output into the same embedding space and then learns a distance metric to discover output dependency such that instances with very different multiple outputs will be moved far away. Several strategies are then proposed to speed up the training and testing time. Moreover, we study the generalization error bound of our method for three learning tasks, which shows that our method converges to the optimal solutions. Experiments on three multi-output learning tasks (multi-label classification, multi-target regression, and multi-concept retrieval) validate the effectiveness and scalability of the proposed method.
Passive Acoustic Mapping Using Data-Adaptive Beamforming Based on Higher Order Statistics

Sources of nonlinear acoustic emissions, particularly those associated with cavitation activity, play a key role in the safety and efficacy of current and emerging therapeutic ultrasound applications, such as oncological drug delivery, blood-brain barrier opening, and histotripsy. Passive acoustic mapping (PAM) is the first technique to enable real-time and non-invasive imaging of cavitation activity during therapeutic ultrasound exposure, through the recording and passive beamforming of broadband acoustic emissions using an array of ultrasound detectors. Initial limitations in PAM spatial resolution led to the adoption of optimal data-adaptive beamforming algorithms, such as the robust capon beamformer (RCB), that provide improved interference suppression and calibration error mitigation compared to non-adaptive beamformers. However, such approaches are restricted by the assumption that the recorded signals have a Gaussian distribution. To overcome this limitation and further improve the source resolvability of PAM, we propose a new beamforming approach termed robust beamforming by linear programming (RLPB). Along with the variance, this optimization-based method uses higher-order-statistics of the recorded signals, making no prior assumption on the statistical distribution of the acoustic signals. The RLPB is found via numerical simulations to improve resolvability over time exposure acoustics and RCB. In vitro experimentation yielded improved resolvability with respect to the source-to-array distance on the order of 22% axially and 13% transversely relative to RCB, whilst successfully accounting for array calibration errors. The improved resolution and decreased dependence on accurate calibration of RLPB is expected to facilitate the clinical translation of PAM for diagnostic, including super-resolution, and therapeutic ultrasound applications.
Measuring Boundedness for Protein Complex Identification in PPI Networks

The problem of identifying protein complexes in Protein-Protein Interaction (PPI) networks is usually formulated as the problem of identifying dense regions in such networks. In this paper, we present a novel approach, called TBPCI, to identify protein complexes based instead on the concept of a measure of boundedness. Such a measure is defined as an objective function of a Jaccard Index-based connectedness measure which takes into consideration how much two proteins within a network are connected to each other, and an association measure which takes into consideration how much two connecting proteins are associated based on their attributes found in the Gene Ontology database. Based on the above two measures, the objective function is derived to capture how strong the proteins can be considered as bounded together and the objective value is therefore referred as the aggregated degree of boundedness. To identify protein complexes, TBPCI computes the degree of boundedness between all possible pairwise proteins. Then, TBPCI uses a Breadth-First-Search method to determine whether a protein-pair should be incorporated into the same complex. TBPCI has been tested with several real data sets and the experimental results show it is an effective approach for identifying protein complexes in PPI networks.
Multiview Clustering via Unified and View-Specific Embeddings Learning

Multiview clustering, which aims at using multiple distinct feature sets to boost clustering performance, has a wide range of applications. A subspace-based approach, a type of widely used methods, learns unified embedding from multiple sources of information and gives a relatively good performance. However, these methods usually ignore data similarity rankings; for example, example A may be more similar to B than C, and such similarity triplets may be more effective in revealing the data cluster structure. Motivated by recent embedding methods for modeling knowledge graph in natural-language processing, this paper proposes to mimic different views as different relations in a knowledge graph for unified and view-specific embedding learning. Moreover, in real applications, it happens so often that some views suffer from missing information, leading to incomplete multiview data. Under such a scenario, the performance of conventional multiview clustering degenerates notably, whereas the method we propose here can be naturally extended for incomplete multiview clustering, which enables full use of examples with incomplete feature sets for model promotion. Finally, we demonstrate through extensive experiments that our method performs better than the state-of-the-art clustering methods.
Modelling and analysis of multi-agent systems using UPPAAL SMC

This paper proposes a novel approach to modelling and analysis of complex multi-agent systems. The approach is based on actors and asynchronous message passing, and exploits the UPPAAL statistical model checker (SMC) for the experiments. UPPAAL SMC is interesting because it automates simulations by predicting the number of executions capable of ensuring a required output accuracy, it uses statistical techniques (Monte Carlo-like simulations and sequential hypothesis testing) for extracting quantitative measures from the simulation runs, and it offers a temporal logic query language to express property queries tailored to the application needs. The paper describes the approach, clarifies its structural translation on top of UPPAAL SMC, and demonstrates its practical usefulness through modelling and analysis of a large scale and adaptive version of the Iterated Prisoner's Dilemma (IPD) problem. The case study confirms known properties, namely the emergence of cooperation under context preservation, that is .
Visible Illumination Enhanced Nonenzymatic Glucose Photobiosensor Based on TiO2 Nanorods Decorated With Au Nanoparticles

A nonenzymatic glucose photobiosensor was developed based on Au-nanoparticle-decorated TiO2 nanorods (NRs) under visible illumination. Au nanoparticles (NPs) absorbed the visible illumination, resulting in surface plasmon resonance (SPR). The SPR of the Au NPs indicated that there was a strong electric field around them, which promoted the transport of more electrons to the TiO2 NRs and enhanced the glucose sensing properties. The sensing current under visible illumination was five times higher than in the dark when in 0.1 M NaOH solution at a potential of 0.17 V. Moreover, the Michaelis–Menten constant (Km) of the Au NPs/TiO2 NRs/FTO under visible illumination was 0.52 mM, which is much smaller than that reported previously. Moreover, these results indicate that the Au NPs/TiO2 NRs/FTO under visible illumination feature outstanding properties as a nonenzymatic glucose photobiosensor.