Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Active Speaker Detection in Noisy Environments

This paper addresses the issue of active speaker detection (ASD) in noisy environments and formulates a robust active speaker detection (rASD) problem. Existing ASD approaches leverage both audio and visual modalities, but non-speech sounds in the surrounding environment can negatively impact performance. To overcome this, we propose a novel framework that utilizes audio-visual speech separation as guidance to learn noise-free audio features. These features are then utilized in an ASD model, and both tasks are jointly optimized in an end-to-end framework. Our proposed framework mitigates residual noise and audio quality reduction issues that can occur in a naive cascaded two-stage framework that directly uses separated speech for ASD, and enables the two tasks to be optimized simultaneously. To further enhance the robustness of the audio features and handle inherent speech noises, we propose a dynamic weighted loss approach to train the speech separator. We also collected a real-world noise audio dataset to facilitate investigations. Experiments demonstrate that non-speech audio noises significantly impact ASD models, and our proposed approach improves ASD performance in noisy environments. The framework is general and can be applied to different ASD approaches to improve their robustness. Our code, models, and data will be released.
Insights on the dip of fault zones in Southern California from modeling of seismicity with anisotropic point processes

Accurate models of fault zone geometry are important for scientific and hazard applications. While seismicity can provide high-resolution point measurements of fault geometry, extrapolating these measurements to volumes may involve making strong assumptions. This is particularly problematic in distributed fault zones, which are commonly observed in immature faulting regions. In this study, we focus on characterizing the dip of fault zones in Southern California with the goal of improving fault models. We introduce a novel technique from spatial point process theory to quantify the orientation of persistent surficial features in seismicity, even when embedded in wide shear zones. The technique makes relatively mild assumptions about fault geometry and is formulated with the goal of determining the dip of a fault zone at depth. The method is applied to 11 prominent seismicity regions in Southern California. Overall, the results compare favorably with the geometry models provided by the SCEC Community Fault Model and other focused regional studies. More specifically, we find evidence that the Southern San Andreas and San Jacinto fault zones are both northeast dipping at seismogenic depths at the length scales of 1.0-4.0 km. In addition, we find more limited evidence for some depth dependent variations in dip that suggest a listric geometry. The developed technique can provide an independent source of information from seismicity to augment existing fault geometry models.
MOSQUITO LARVAE (CULICIDAE) AND OTHER DIPTERA ASSOCIATED WITH CONTAINERS, STORM DRAINS, AND SEWAGE TREATMENT PLANTS IN THE FLORIDA KEYS, MONROE COUNTY, FLORIDA

Abstract We investigated the larval dipteran fauna of artificial and natural containers, sewage treatment plants, and storm drains in the Florida Keys. Mosquitoes collected were Aedes aegypti, Ae. albopictus, Anopheles crucians, Culex atratus, Cx. nigripalpus, Cx. peccator, Cx. quinquefasciatus, Deinocerites cancer, Ochlerotatus taeniorhynchus, and Wyeomyia vanduzeei, as well as an unidentified Culex (Melanoconion) species and an unidentified Anopheles species. Other Diptera collected included a chironomid species in the Chironomus decorus Johannsen group; the filter fly, Clogmia albipunctata; an undescribed psychodid in the genus Austropericoma; a ceratopogonid midge, Dasyhelea pseudoincisurata; and a phorid fly, Megaselia scalaris.
Spying on Android users through targeted ads

Targeted ads are a vital source of revenue for ad networks, and ad networks compile detailed profiles of users for targeting purposes. This wealth of private information can motivate an attacker to spy on a victim by sampling the victim's information available at the ad networks. In this paper, we explore the possibility to spy on an Android user by requesting ads targeted at that user. During our controlled experiments, an attacker was able to remotely request and receive mobile ads using a leaked identifier belonging to their victim. The lack of targeted ads from some of the studied ad networks, which we believe to be partly caused by our geographical location, restricts us from drawing strong conclusions on the success of such an attack. However, we believe that our methodology can be used to perform similar experiments to explore the success in spying on Android users through targeted ads.
Alteration of G1/S transition regulators influences recurrences in head and neck squamous carcinomas

Head‐Neck Squamous Cell Carcinoma (HN‐SCC) is a clinically challenging disease associated with a high mortality rate. The chemo‐radiotherapy treatments that aim to preserve the organ represent the current gold standard therapy for advanced laryngeal disease, reserving surgery only for non‐responsive or relapsed cases. Despite these aggressive approaches, local persistent or recurrent disease remains the primary cause of treatment failure but we still do not have known factors and/or markers able to predict the outcome of the disease and in particular the risk of local relapse. Here we address this point on a series of 54 cases of HN‐SCC for whom the presence of local relapse was known. Using immunohistochemistry (IHC) analysis to evaluate protein expression and localization in the recurrence free and recurrence positive samples, we studied the expression of key cell cycle regulators including p53, p16, p27, pRB, Cyclin D1, Cyclin D3, and Stathmin. Overall by analyzing seven different cell cycle regulators we can hypothesize that the alteration of G1/S regulation represents a fundamental event in the onset/progression of HN‐SCC cancers and that the associate use of Cyclin D1/p16 expression should be considered as a possible biomarker toward the identification of those patients that will probably develop a recurrent disease and thus should benefit of a more aggressive treatment. J. Cell. Physiol. 227: 233–238, 2012. © 2011 Wiley Periodicals, Inc.
Clustering MOOC Programming Solutions to Diversify Their Presentation to Students

In many MOOCs, whenever a student completes a programming task, they can see previous solutions of other students to find potentially different ways of solving the problem and learn new coding constructs. However, a lot of MOOCs simply show the most recent solutions, disregarding their diversity or quality. To solve this novel problem, we adapted the existing plagiarism detection tool JPlag to Python submissions on Hyperskill, a popular MOOC platform. However, due to the tool's inner algorithm, it fully processed only 46 out of 867 studied tasks. Therefore, we developed our own tool called Rhubarb. This tool first standardizes solutions that are algorithmically the same, then calculates the structure-aware edit distance between them, and then applies clustering. Finally, it selects one example from each of the largest clusters, taking into account their code quality. Rhubarb was able to handle all 867 tasks successfully. We compared approaches on a set of 59 tasks that both tools could process. Eight experts rated the selected solutions based on diversity, code quality, and usefulness. The default platform approach of selecting recent submissions received on average 3.12 out of 5, JPlag - 3.77, Rhubarb - 3.50. Since in the real MOOC, it is imperative to process everything, we created a system that uses JPlag on the 5.3% of tasks it fully processes and Rhubarb on the remaining 94.7%.
RTracker: Recoverable Tracking via PN Tree Structured Memory

Existing tracking methods mainly focus on learning better target representation or developing more robust prediction models to improve tracking performance. While tracking performance has significantly improved, the target loss issue occurs frequently due to tracking failures, complete occlusion, or out-of-view situations. However, considerably less attention is paid to the self-recovery issue of tracking methods, which is crucial for practical applications. To this end, we propose a recoverable tracking framework, RTracker, that uses a tree-structured memory to dynamically associate a tracker and a detector to enable self-recovery ability. Specifically, we propose a Positive-Negative Tree-structured memory to chronologically store and maintain positive and negative target samples. Upon the PN tree memory, we develop corresponding walking rules for determining the state of the target and define a set of control flows to unite the tracker and the detector in different tracking scenarios. Our core idea is to use the support samples of positive and negative target categories to establish a relative distance-based criterion for a reliable assessment of target loss. The favorable performance in comparison against the state-of-the-art methods on numerous challenging benchmarks demonstrates the effectiveness of the proposed algorithm.
Effectiveness and Efficiency as Conflicting Requirements in Designing Emergency Mission Reporting

Aspects of usability, such as effectiveness and efficiency, are critical for users' overall experience of an interactive system. In response to the on-going debate on the relationship between different aspects of usability in usability studies, we present an example of a User Interface (UI) design case where the relationship between effectiveness and efficiency should be considered as a requirement or design issue, rather than as variables in usability studies. In the presented case status reporting from an in-vehicle support system for emergency missions these aspects of usability were perceived as conflicting rather than as positively correlated. We present various design solutions to the task of status reporting and show how the solutions support effectiveness and efficiency in different ways. Finally, we point out some characteristics of the case that could explain our findings and we suggest how future research may obtain more insight into which types of applications that may possess similar properties. Author
Exploiting SPMD Horizontal Locality

In this paper, we analyze a particular spatial locality case (called horizontal locality) inherent to manycore accelerator architectures employing barrel execution of SPMD kernels, such as GPUs. We then propose an adaptive memory access granularity framework to exploit and enforce the horizontal locality in order to reduce the interferences among accelerator cores memory accesses and hence improve DRAM efficiency. With the proposed technique, DRAM efficiency grows by 1.42X on average, resulting in 12.3% overall performance gain, for a set of representative memory intensive GPGPU applications.
Developing generative AI chatbots conceptual framework for higher education

This research explores the quickly changing field of generative artificial intelligence (GAI) chatbots in higher education, an industry that is undergoing major technological changes. AI chatbots, such as ChatGPT, HuggingChat, and Google Bard, are becoming more and more common in a variety of sectors, including education. Their acceptance is still in its early phases, with a variety of prospects and obstacles. However, their potential in higher education is particularly noteworthy, providing lecturers and students with affordable, individualized support. Creating a comprehensive framework to aid the usage of generative AI chatbots in higher education institutions (HEIs) is the aim of this project. The Chukwuere Generative AI Chatbots Acceptance Model (CGAICAM) is the result of this study's synthesis of elements from well-known frameworks, including the TAM, UTAUT2, TPB, and others along with variables like optimism, innovativeness, discomfort, insecurity, and others. Using a research method that encompasses a comprehensive analysis of extant literature from databases such as IEEE, ACM, ScienceDirect, and Google Scholar, the study aims to comprehend the implications of AI Chatbots on higher education and pinpoint critical elements for their efficacious implementation. Peer-reviewed English-language publications published between 2020 and 2023 with a focus on the use of AI chatbots in higher education were the main focus of the search criteria. The results demonstrate how much AI chatbots can do to improve student engagement, streamline the educational process, and support administrative and research duties. But there are also clear difficulties, such as unfavorable student sentiments, doubts about the veracity of material produced by AI, and unease and nervousness with new technologies.
An efficient multiscale multigrid preconditioner for Darcy flow in high-contrast media

In this paper, we develop a multigrid preconditioner to solve Darcy flow in highly heterogeneous porous media. The key component of the preconditioner is to construct a sequence of nested subspaces $W_{\mathcal{L}}\subset W_{\mathcal{L}-1}\subset\cdots\subset W_1=W_h$. An appropriate spectral problem is defined in the space of $W_{i-1}$, then the eigenfunctions of the spectral problems are utilized to form $W_i$. The preconditioner is applied to solve a positive semidefinite linear system which results from discretizing the Darcy flow equation with the lowest order Raviart-Thomas spaces and adopting a trapezoidal quadrature rule. Theoretical analysis and numerical investigations of this preconditioner will be presented. In particular, we will consider several typical highly heterogeneous permeability fields whose resolutions are up to $1024^3$ and examine the computational performance of the preconditioner in several aspects, such as strong scalability, weak scalability, and robustness against the contrast of the media. We also demonstrate an application of this preconditioner for solving a two-phase flow benchmark problem.
Random Multi-Type Spanning Forests for Synchronization on Sparse Graphs

Random diffusions are a popular tool in Monte-Carlo estimations, with well established algorithms such as Walk-on-Spheres (WoS) going back several decades. In this work, we introduce diffusion estimators for the problems of angular synchronization and smoothing on graphs, in the presence of a rotation associated to each edge. Unlike classical WoS algorithms, these estimators allow for global estimations by propagating along the branches of multi-type spanning forests, and we show that they can outperform standard numerical-linear-algebra solvers in challenging instances, depending on the topology and density of the graph.
Sister Celine's polynomials in the quantum theory of angular momentum

The polynomials introduced by Sister Celine cover different usual orthogonal polynomials as special cases. Among them, the Jacobi and discrete Hahn polynomials are of particular interest for the quantum theory of angular momentum. In this note, we show that characters of irreducible representations of the rotation group as well as Wigner rotation"d"matrices, can be expressed as Sister Celine's polynomials. Since many relations were proposed for the latter polynomials, such connections could lead to new identities for quantities important in quantum mechanics and atomic physics.
Distinguishing Polynomials of Graphs

For a graph $G$, a $k$-coloring $c:V(G)\to \{1,2,\ldots, k\}$ is called distinguishing, if the only automorphism $f$ of $G$ with the property $c(v)=c(f(v))$ for every vertex $v\in G$ (color-preserving automorphism), is the identity. In this paper, we show that the number of distinguishing $k$-colorings of $G$ is a monic polynomial in $k$, calling it the distinguishing polynomial of $G$. Furthermore, we compute the distinguishing polynomials of cycles and complete multipartite graphs. We also show that the multiplicity of zero as a root of the distinguishing polynomial of $G$ is at least the number of orbits of $G$.
Integrative review of nurse-led follow-up after discharge from the ICU.

AIM AND OBJECTIVES To analyse and synthesise the structure, content, types of outcome variables and advantages of nurse-led follow-up of adult patients after discharge from intensive care units. BACKGROUND Follow-up service after discharge from the intensive care unit has been suggested as a way of supporting recovery of patients. Nevertheless, varieties exist in the understanding and content of nurse-led follow-up. DESIGN An integrative review of nurse-led follow-up inspired by the framework of Whittemore and Knafl. METHODS An integrative method merged with the recommendations of the PRISMA statement was used to structure the review and findings. Online databases PubMed, CINAHL, ScienceDirect and Scopus were searched from the years 2003-2014. The retrieved articles were independently assessed by two reviewers. Critical appraisal was conducted using check lists from Johanna Briggs Institute. Emerging patterns were validated by all the authors throughout the entire process of analysis. RESULTS Seventeen papers were included. Three patterns of nurse-led follow-up were identified: (1) Ward visits--in the immediate time after discharge from intensive care unit, (2) Ward visits and appointment(s) to an intensive care unit follow-up clinic and (3) follow-up visit to an intensive care unit and phone call(s) after discharge. Content of short-term nurse-led follow-up (1) ranged from clinical assessment to supporting patients in articulating their subjective health concerns. Long-term nurse-led follow-up (2, 3) included appointments, phone call(s) or information on where advice could be sought. Types of outcome variables were primarily descriptive. There were strong implications for patients' satisfaction with nurse-led follow-up up to six months after discharge. CONCLUSION Nurse-led follow-up might promote patients' health and enable use of adequate resources. RELEVANCE TO CLINICAL PRACTICE The findings of this review could be used to design, and test, future interventions and their implementation.
Transfer matrix analysis of non-hermitian Hamiltonians: asymptotic spectra and topological eigenvalues

Transfer matrix techniques are used to {provide} a new proof of Widom's results on the asymptotic spectral theory of finite block Toeplitz matrices. Furthermore, a rigorous treatment of {the} skin effect, spectral outliers, the generalized Brillouin zone and the bulk-boundary correspondence in such systems is given. This covers chiral Hamiltonians with topological eigenvalues close to zero, but no line-gap.
Positive Span of Force and Torque Components of Four-Fingered Three-Dimensional Force-Closure Grasps

We consider a 3D grasping problem. We propose a test of Ropf3-positive span of force cones and a test of Ropf3-positive span of their torque set. Our methods consider directly the quadratic force cone without pyramidal linearization of a cone. The conditions can be computed in a constant time and they are used as a heuristic for 3D force closure test. We also show that our proposed heuristic greatly improve the performance of existing grasp testing algorithms.
Blind Identification of Binaural Room Impulse Responses from Smart Glasses

Smart glasses are increasingly recognized as a key medium for augmented reality, offering a hands-free platform with integrated microphones and non-ear-occluding loudspeakers to seamlessly mix virtual sound sources into the real-world acoustic scene. To convincingly integrate virtual sound sources, the room acoustic rendering of the virtual sources must match the real-world acoustics. Information about a user's acoustic environment however is typically not available. This work uses a microphone array in a pair of smart glasses to blindly identify binaural room impulse responses (BRIRs) from a few seconds of speech in the real-world environment. The proposed method uses dereverberation and beamforming to generate a pseudo reference signal that is used by a multichannel Wiener filter to estimate room impulse responses which are then converted to BRIRs. The multichannel room impulse responses can be used to estimate room acoustic parameters which is shown to outperform baseline algorithms in the estimation of reverberation time and direct-to-reverberant energy ratio. Results from a listening experiment further indicate that the estimated BRIRs often reproduce the real-world room acoustics perceptually more convincingly than measured BRIRs from other rooms with similar geometry.
Build to Win: Electric Machines

Electric machinery can serve as a powerful teaching tool in engineering curricula of all kinds. We have designed kits of flexibly configurable components that students use to quickly construct electromagnetic actuators and sensors. They can use these machines to verify design calculations, test their understanding of the machine operating principles, and to test power electronic drives that they also design and construct. In class, the properties of different motor designs and sizing can be related to commercial products, and commercial products can be used as inspiration for design contests in the teaching laboratory.
Towards Temporally Consistent Referring Video Object Segmentation

Referring Video Object Segmentation (R-VOS) methods face challenges in maintaining consistent object segmentation due to temporal context variability and the presence of other visually similar objects. We propose an end-to-end R-VOS paradigm that explicitly models temporal instance consistency alongside the referring segmentation. Specifically, we introduce a novel hybrid memory that facilitates inter-frame collaboration for robust spatio-temporal matching and propagation. Features of frames with automatically generated high-quality reference masks are propagated to segment the remaining frames based on multi-granularity association to achieve temporally consistent R-VOS. Furthermore, we propose a new Mask Consistency Score (MCS) metric to evaluate the temporal consistency of video segmentation. Extensive experiments demonstrate that our approach enhances temporal consistency by a significant margin, leading to top-ranked performance on popular R-VOS benchmarks, i.e., Ref-YouTube-VOS (67.1%) and Ref-DAVIS17 (65.6%).
Extreme value theory and applications in environment

In the first two chapters, we try to answer two questions that are critical in climatology. The first one is to know whether a change in the behaviour of the temperature extremes occured between the beginning of the century and today. We suggest to use a version of the Kullback Leibler divergence tailored for the extreme value context. We provide some theoretical and simulation results to justify our approach. The second question is to decide where to remove stations from a network to lose the least information about the behaviour of the extremes. An algorithm called the Query By Committee is developed and applied to real data. The last chapter of the thesis deals with a more theoretical subject which is the robust estimation of a Weibull type tail index in presence of random covariates. We propose a robust estimator based on a criterion ofminimization of the divergence between two densities and study its properties.