Miracle-dz/UniFAR-FTUs huggingface dataset:
Physics-Driven Diffusion Models for Impact Sound Synthesis from Videos

Modeling sounds emitted from physical object interactions is critical for immersive perceptual experiences in real and virtual worlds. Traditional methods of impact sound synthesis use physics simulation to obtain a set of physics parameters that could represent and synthesize the sound. However, they require fine details of both the object geometries and impact locations, which are rarely available in the real world and can not be applied to synthesize impact sounds from common videos. On the other hand, existing video-driven deep learning-based approaches could only capture the weak correspondence between visual content and impact sounds since they lack of physics knowledge. In this work, we propose a physics-driven diffusion model that can synthesize high-fidelity impact sound for a silent video clip. In addition to the video content, we propose to use additional physics priors to guide the impact sound synthesis procedure. The physics priors include both physics parameters that are directly estimated from noisy real-world impact sound examples without sophisticated setup and learned residual parameters that interpret the sound environment via neural networks. We further implement a novel diffusion model with specific training and inference strategies to combine physics priors and visual information for impact sound synthesis. Experimental results show that our model outperforms several existing systems in generating realistic impact sounds. Lastly, the physics-based representations are fully interpretable and transparent, thus allowing us to perform sound editing flexibly. We encourage the readers visit our project page11https://sukun1045.github. io/video-physics-sound-diffusion/ to watch demo videos with the audio turned on to experience the result.
Control of temporal mapping and harmonic intensity modulation using two-color orthogonally polarized fields

We demonstrate theoretically control over the time-frequency mapping in two-color high-order harmonic generation. By changing the temporal delay between a driving field and its orthogonally polarized second harmonic we are able to preferentially select between the short- and long-orbit contributions to the harmonic emission along both polarization axes. This trajectory selection is interpreted through use of classical and quantum orbit models. We further observe, and explain, different modulations in intensity of odd and even order harmonic emission modeled via a full macroscopic propagation model coupled to a single-atom quantum orbit treatment.
A Byzantine-Resilient Aggregation Scheme for Federated Learning via Matrix Autoregression on Client Updates

In this work, we propose FLANDERS, a novel federated learning (FL) aggregation scheme robust to Byzantine attacks. FLANDERS considers the local model updates sent by clients at each FL round as a matrix-valued time series. Then, it identifies malicious clients as outliers of this time series by comparing actual observations with those estimated by a matrix autoregressive forecasting model. Experiments conducted on several datasets under different FL settings demonstrate that FLANDERS matches the robustness of the most powerful baselines against Byzantine clients. Furthermore, FLANDERS remains highly effective even under extremely severe attack scenarios, as opposed to existing defense strategies.
Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions

Predicting crime using machine learning and deep learning techniques has gained considerable attention from researchers in recent years, focusing on identifying patterns and trends in crime occurrences. This review paper examines over 150 articles to explore the various machine learning and deep learning algorithms applied to predict crime. The study provides access to the datasets used for crime prediction by researchers and analyzes prominent approaches applied in machine learning and deep learning algorithms to predict crime, offering insights into different trends and factors related to criminal activities. Additionally, the paper highlights potential gaps and future directions that can enhance the accuracy of crime prediction. Finally, the comprehensive overview of research discussed in this paper on crime prediction using machine learning and deep learning approaches serves as a valuable reference for researchers in this field. By gaining a deeper understanding of crime prediction techniques, law enforcement agencies can develop strategies to prevent and respond to criminal activities more effectively.
Attractor-Like Dynamics Extracted from Human Electrocorticographic Recordings Underlie Computational Principles of Auditory Bistable Perception

In bistable perception, observers experience alternations between two interpretations of an unchanging stimulus. Neurophysiological studies of bistable perception typically partition neural measurements into stimulus-based epochs and assess neuronal differences between epochs based on subjects' perceptual reports. Computational studies replicate statistical properties of percept durations with modeling principles like competitive attractors or Bayesian inference. However, bridging neuro-behavioral findings with modeling theory requires the analysis of single-trial dynamic data. Here, we propose an algorithm for extracting nonstationary timeseries features from single-trial electrocorticography (ECoG) data. We applied the proposed algorithm to 5-min ECoG recordings from human primary auditory cortex obtained during perceptual alternations in an auditory triplet streaming task (six subjects: four male, two female). We report two ensembles of emergent neuronal features in all trial blocks. One ensemble consists of periodic functions that encode a stereotypical response to the stimulus. The other comprises more transient features and encodes dynamics associated with bistable perception at multiple time scales: minutes (within-trial alternations), seconds (duration of individual percepts), and milliseconds (switches between percepts). Within the second ensemble, we identified a slowly drifting rhythm that correlates with the perceptual states and several oscillators with phase shifts near perceptual switches. Projections of single-trial ECoG data onto these features establish low-dimensional attractor-like geometric structures invariant across subjects and stimulus types. These findings provide supporting neural evidence for computational models with oscillatory-driven attractor-based principles. The feature extraction techniques described here generalize across recording modality and are appropriate when hypothesized low-dimensional dynamics characterize an underlying neural system. SIGNIFICANCE STATEMENT Irrespective of the sensory modality, neurophysiological studies of multistable perception have typically investigated events time-locked to the perceptual switching rather than the time course of the perceptual states per se. Here, we propose an algorithm that extracts neuronal features of bistable auditory perception from largescale single-trial data while remaining agnostic to the subject's perceptual reports. The algorithm captures the dynamics of perception at multiple timescales, minutes (within-trial alternations), seconds (durations of individual percepts), and milliseconds (timing of switches), and distinguishes attributes of neural encoding of the stimulus from those encoding the perceptual states. Finally, our analysis identifies a set of latent variables that exhibit alternating dynamics along a low-dimensional manifold, similar to trajectories in attractor-based models for perceptual bistability.
Healthier diets for all? A systematic review and meta‐analysis examining socioeconomic equity of the effect of increasing availability of healthier foods on food choice and energy intake

Widespread availability of unhealthy food and drink products may contribute to socioeconomic patterning in obesity. Therefore, increasing the availability of healthier foods may be one approach to reducing obesity without widening existing inequalities. This systematic review and meta‐analysis examined the impact of increasing the availability of healthier food and drink on consumer behavior among individuals with higher and lower socioeconomic position (SEP). Eligible studies were required to use experimental designs to compare conditions of higher vs lower availability of healthier vs less healthy options on food choice‐related outcomes and measure SEP. Thirteen eligible studies were included. Odds of choosing a healthy item were higher when availability was increased for higher (OR = 5.0, 95% CI: 3.3, 7.7) and lower (OR = 4.9, CI: 3.0, 8.0) SEP. Increased availability of healthier foods was also associated with a decrease in energy content of selections for higher (−131 kcal; CI: −76, −187) and lower (−109 kcal; CI: −73, −147) SEP. There was no SEP moderation. Increasing the relative availability of healthier foods may be an equitable and effective approach to improve population‐level diet and address obesity, though more research is required testing this in real‐world environments.
Identification of quasi-periodically varying systems with quasi-linear frequency changes

Abstract The problem of identification of linear quasi-periodically varying systems is considered. This problem can be solved using generalized adaptive notch filtering (GANF) algorithms. It is shown that accuracy of system parameter estimation can be increased if the results obtained from GANF are further processed using a cascade of appropriately designed filters. The resulting generalized adaptive notch smoothing (GANS) algorithms can be employed in offline applications where causality constraints do not apply. When the instantaneous frequency of parameter changes varies in a sufficiently smooth manner, the proposed GANS algorithm, based on a new, quasi-linear model of frequency drift, outperforms the existing solutions.
Pitfalls of the concordance index for survival outcomes

Prognostic models are useful tools for assessing a patient's risk of experiencing adverse health events. In practice, these models must be validated before implementation to ensure that they are clinically useful. The concordance index (C‐Index) is a popular statistic that is used for model validation, and it is often applied to models with binary or survival outcome variables. In this paper, we summarize existing criticism of the C‐Index and show that many limitations are accentuated when applied to survival outcomes, and to continuous outcomes more generally. We present several examples that show the challenges in achieving high concordance with survival outcomes, and we argue that the C‐Index is often not clinically meaningful in this setting. We derive a relationship between the concordance probability and the coefficient of determination under an ordinary least squares model with normally distributed predictors, which highlights the limitations of the C‐Index for continuous outcomes. Finally, we recommend existing alternatives that more closely align with common uses of survival models.
Synchronization of Coupled Phase Oscillators with Stochastic Disturbances and the Cycle Space of the Graph

The synchronization stability of a complex network system of coupled phase oscillators is discussed. In case the network is affected by disturbances, a stochastic linearized system of the coupled phase oscillators may be used to determine the fluctuations of phase differences in the lines between the nodes and to identify the vulnerable lines that may lead to desynchronization. The main result is the derivation of the asymptotic variance matrix of the phase differences which characterizes the severity of the fluctuations. It is found that the cycle space of the graph of the system plays a role in this characterization. With theory of the cycle space, the effect of forming small cycles on the fluctuations are evaluated. It is proven that adding a new line or increasing the coupling strength of a line affect the fluctuations in the lines in any cycle including this line while it does not affect the fluctuations in the other lines. In particular, if the phase differences at the synchronous state are not changed by these actions, then the affected fluctuations reduce.
Clonal hematopoiesis detection in patients with cancer using cell-free DNA sequencing

In the context of cancer, clonal hematopoiesis of indeterminate potential (CHIP) is associated with the development of therapy-related myeloid neoplasms and shorter overall survival. Cell-free DNA (cfDNA) sequencing is becoming widely adopted for genomic screening of patients with cancer but has not been used extensively to determine CHIP status because of a requirement for matched blood and tumor sequencing. We present an accurate classification approach to determine the CH status from cfDNA sequencing alone, applying our model to 4324 oncology clinical cfDNA samples. Using this method, we determined that 30.3% of patients in this cohort have evidence of CH, and the incidence of CH varies by tumor type. Matched RNA sequencing data show evidence of increased inflammation, especially neutrophil activation, within the tumors and tumor microenvironments of patients with CH. In addition, patients with CH had evidence of neutrophil activation systemically, pointing to a potential mechanism of action for the worse outcomes associated with CH status. Neutrophil activation may be one of many mechanisms, however, because patients with estrogen receptor–positive breast cancer harboring TET2 frameshift mutations had worse outcomes but similar neutrophil frequencies to patients without CH. Together, these data show the feasibility of detecting CH through cfDNA sequencing alone and an application of this method, demonstrating increased inflammation in patients with CH both systemically and in the tumor microenvironment. Description Clonal hematopoiesis in patients with cancer can be accurately detected using cell-free DNA sequencing. Cell-free detection Clonal hematopoiesis (CH) commonly occurs in older adults and is also associated with poorer outcomes in patients with cancer. The use of cell-free DNA (cfDNA) sequencing to noninvasively detect tumor-derived variants is becoming more common, but a challenge in diagnosing CH from cfDNA sequencing data in patients with cancer is the need for matched blood and tumor tissue. Here, Fairchild et al. developed a classification approach to identify CH in cfDNA samples without the need for matched blood or tumor tissue. Using this classifier, the authors found, in a cohort of 4324 oncology cfDNA samples, that almost one-third of the samples had evidence of CH. In these samples, CH was associated with inflammation and neutrophil activation both local to the tumor and systemically, suggesting a potential mechanism by which CH could be pathogenic. Together, these data support the use of cfDNA to more easily identify CH. —CM
Consistent integration and propagation of disparate sensor observations

To operate efficiently in an unknown or uncertain environ ment, robots must take observations from many different sensors to provide information with which to build a robust world model. We describe a methodfor integrating partial, uncertain, geometric sensor observations into a robust, con sistent estimate of the state of the environment. The integra tion process uses a Bayes procedure for comparing disparate observations of geometric features, rejecting spurious mea surements, and providing partial updates of object locations to a world model. This integration mechanism can combine any number of observations from sensors that provide mea surements of different geometric features. The invariant topology of relations between uncertain geometric features is used to develop a method for propagating observations through the world model. This propagation mechanism forces a consistent interpretation of the environment to be main tained and makes maximum use of sensor information. The method described has been .
Scattering properties of Sturm-Liouville equations with sign-alternating weight and transmission condition at turning point

Abstract In this article, we focus on the scattering analysis of Sturm-Liouville type singular operator including an impulsive condition and turning point. In the classical literature, there are plenty of papers considering the positive values of the weight function in both continuous and discontinuous cases. However, this article differs from the others in terms of the impulsive condition appearing at the turning point. We generate the scattering function, resolvent operator, and discrete spectrum of the operator using the hyperbolic representations of the fundamental solutions. Finally, we create an example to show the article’s primary conclusions.
CNN-based Segmentation and Classification of Sound Streams under realistic conditions

Audio datasets support the training and validation of Machine Learning algorithms in audio classification problems. Such datasets include different, arbitrarily chosen audio classes. We initially investigate a unifying approach, based on the mapping of audio classes according to the Audioset ontology. Using the ESC-10 audio dataset, a tree-like representation of its classes is created. In addition, we employ an audio similarity calculation tool based on the values of extracted features (spectrum centroid, the spectrum flux and the spectral roll-off). This way the audio classes are connected both semantically and in feature-based manner. Employing the same dataset, ESC-10, we perform sound classification using CNN-based algorithms, after transforming the sound excerpts into images (based on their Mel spectrograms). The YAMNet and VGGish networks are used for audio classification and the accuracy reaches 90%. We extend the classification algorithm with segmentation logic, so that it can be applied into more complex sound excerpts, where multiple sound types are included in a sequential and/or overlapping manner. Quantitative metrics are defined on the behavior of the combined segmentation and segmentation functionality, including two key parameters for the merging operation, the minimum duration of the identified sounds and the intervals. The qualitative metrics are related to the number of sound identification events for a concatenated sound excerpt of the dataset and per each sound class. This way the segmentation logic can operate in a fine- and coarse-grained manner while the dataset and the individual sound classes are characterized in terms of clearness and distinguishability.
A Survey on Vulnerability Prediction using GNNs

The massive release of software products has led to critical incidents in the software industry due to low-quality software. Software engineers lack security knowledge which causes the development of insecure software. Traditional solutions for analysing code for vulnerabilities suffer from high false positives and negative rates. Researchers over the last decade have proposed mechanisms for analysing code for vulnerabilities using machine learning. The results are promising and could replace traditional static analysis tools or accompany them in the foreseeable future to produce more reliable results. This survey presents the work done so far in vulnerability detection using Graph Neural Networks (GNNs). Presents the GNNs architectures, the graph representations, the datasets, and the results of these studies.
Educational Data Mining-based visualization and early prediction of student performance: a synergistic approach

Educational Data Mining tools exploit the data automatically logged in e-learning platforms such as moodle to address a variety of education-related questions including the (early) prediction and visualization of student participation and performance data. Efficient tools are available to these ends; they offer a wide spectrum of functionalities at the cost of certain limitations or constraints. A synergistic approach that uses more than one tool is therefore proposed in this paper to answer the double need of the teacher for visualization of current and prediction of future student participation and performance outcomes. Moreover, the feasibility of early and accurate prediction is investigated through a case study on a postgraduate course semester module, aiming at an early warning system for students at risk of failure. Case study results indicate that timely and accurate prediction is possible early enough in the semester to allow enough time for supportive measures within the same term. The combined use of tools can also offer a multi-faceted visualization that dynamically monitors variables of interest at the individual and the class level, including current performance data that feed prediction. More research is certainly necessary, however, to offer to the teachers – end users tools with truly user-friendly interfaces.
An Indicative Demanding Teaching Scenario for Primary Education with the Support of a Multi-layer Fog Computing Architecture

Fog computing is a new, modern type of computing that distributes some of the storage, estimation, and processing constraints of cloud computing closer to the edge of the network. The impact of Cloud and Fog Computing in an educational context is now more important than ever. This paper presents innovative designed services of fog computing techniques in the educational sector. An original educational scenario is described, based on innovative fog computing techniques and the appropriate use of online collaborative learning tools. The aim of the paper is to propose an appropriate fog-based architecture to support education in Primary and Secondary Education as well as to provide appropriate recommendations and present some indicative education scenarios implemented based on the use of cloud and fog computing. The proposed scenario is properly documented, while specific implementation issues are presented and analyzed.
Towards open source software licenses compatibility check

The use of free and open source software is increasing and there is currently a tendency towards more openness in the provision of open source software. However, libraries that are used in conjunction with the software may affect the final license selection of the open source software and special caution is needed by software developers. Existing tools provide the means to extract license information from software projects, but this information has not been utilized towards recommending licenses that do not cause license violations. In this paper, we present our work towards the recommendation of licenses that satisfy the license compatibility requirement taking into consideration the licenses of third party libraries used in the software project. We have employed a dataset of 160 open source software projects to compare license compatibility using license extraction techniques implemented in different tools, i.e. Nomos and Ninka, whereas we have integrated the license extraction process in the findOSSLicense open source license recommender system, in order to recommend licenses that do not cause violations. The evaluation results and a small scale user study demonstrate the added value of the approach for the software developers in being better informed about license compatibility.