Miracle-dz/UniFAR-FTUs huggingface dataset:
Generation-Guided Multi-Level Unified Network for Video Grounding

Video grounding aims to locate the timestamps best matching the query description within an untrimmed video. Prevalent methods can be divided into moment-level and clip-level frameworks. Moment-level approaches directly predict the probability of each transient moment to be the boundary in a global perspective, and they usually perform better in coarse grounding. On the other hand, clip-level ones aggregate the moments in different time windows into proposals and then deduce the most similar one, leading to its advantage in fine-grained grounding. In this paper, we propose a multi-level unified framework to enhance performance by leveraging the merits of both moment-level and clip-level methods. Moreover, a novel generation-guided paradigm in both levels is adopted. It introduces a multi-modal generator to produce the implicit boundary feature and clip feature, later regarded as queries to calculate the boundary scores by a discriminator. The generation-guided solution enhances video grounding from a two-unique-modals' match task to a cross-modal attention task, which steps out of the previous framework and obtains notable gains. The proposed Generation-guided Multi-level Unified network (GMU) surpasses previous methods and reaches State-Of-The-Art on various benchmarks with disparate features, e.g., Charades-STA, ActivityNet captions.
Reduced resonance schemes and Chen ranks

The resonance varieties are cohomological invariants that are studied in a variety of topological, combinatorial, and geometric contexts. We discuss their scheme structure in a general algebraic setting and introduce various properties that ensure the reducedness of the associated projective resonance scheme. We prove an asymptotic formula for the Hilbert series of the associated Koszul module, then discuss applications to vector bundles on algebraic curves and to Chen ranks formulas for finitely generated groups, with special emphasis on K\"ahler and right-angled Artin groups.
Style Feature Extraction Using Contrastive Conditioned Variational Autoencoders with Mutual Information Constraints

Extracting fine-grained features such as styles from unlabeled data is crucial for data analysis. Unsupervised methods such as variational autoencoders (VAEs) can extract styles that are usually mixed with other features. Conditional VAEs (CVAEs) can isolate styles using class labels; however, there are no established methods to extract only styles using unlabeled data. In this paper, we propose a CVAE-based method that extracts style features using only unlabeled data. The proposed model consists of a contrastive learning (CL) part that extracts style-independent features and a CVAE part that extracts style features. The CL model learns representations independent of data augmentation, which can be viewed as a perturbation in styles, in a self-supervised manner. Considering the style-independent features from the pretrained CL model as a condition, the CVAE learns to extract only styles. Additionally, we introduce a constraint based on mutual information between the CL and VAE features to prevent the CVAE from ignoring the condition. Experiments conducted using two simple datasets, MNIST and an original dataset based on Google Fonts, demonstrate that the proposed method can efficiently extract style features. Further experiments using real-world natural image datasets were also conducted to illustrate the method's extendability.
Analysis of Wave Height Variability Using Altimeter Measurements: Application to the Mediterranean Sea

Abstract Altimeter significant wave height (SWH) measurement data from six satellite missions covering 14 yr were analyzed over the Mediterranean Sea. First, data correction and screening were performed using the same method for the six altimeters [European Remote Sensing Satellites (ERS-1 and ERS-2), Ocean Topography Experiment (TOPEX), Geosat Follow-On, Jason, and Environmental Satellite (Envisat)]. The data from the TOPEX and Jason missions enabled the construction of seasonal maps of along-track SWH mean values and standard deviations. These reveal the regional short-scale sea state features associated with the specific meteorological patterns of the various geographical basins. Time series of monthly SWH mean values and standard deviations from each satellite and over the whole Mediterranean Sea were calculated and seen to be in good agreement, thus demonstrating interannual variability. The six altimeter missions used together enable the investigation of the monthly annual cycle at the short scales .
Ultrasonic performance of the PVDF thin film sensors under thermal fatigue

In the present work, the ultrasonic strain sensing performance of the large area PVDF thin film subjected to the thermal fatigue is studied. The PVDF thin film is prepared using hot press and the piezoelectric phase ( β-phase) has been achieved by thermo-mechanical treatment and poling under DC field. The sensors used in aircrafts for structural health monitoring applications are likely to be subjected to a wide range of temperature fluctuations which may create thermal fatigue in both aircraft structures and in the sensors. Thus, the sensitivity of the PVDF sensors for thermal fatigue needs to be studied for its effective implementation in the structural health monitoring applications. In present work, the fabricated films have been subjected to certain number of thermal cycles which serve as thermal fatigue and are further tested for ultrasonic strain sensitivity at various different frequencies. The PVDF sensor is bonded on the beam specimen at one end and the ultrasonic guided waves are launched with a piezoelectric wafer bonded on another end of the beam. Sensitivity of PVDF sensor in terms of voltage is obtained for increasing number of thermal cycles. Sensitivity variation is studied at various different extent of thermal fatigue. The variation of the sensor sensitivity with frequency due to thermal fatigue at different temperatures is also investigated. The present investigation shows an appropriate temperature range for the application of the PVDF sensors in structural health monitoring.
Identifying vascular features with orientation specific filters and B-spline snakes

The authors present several new techniques for determining vessel boundaries, or enhancing their centerline features. A bank of S-Gabor filter pairs are used to create an energy field. Vessels are represented by B-spline snakes, and depending on whether vascular boundaries or centerlines are to be determined, they are optimized on appropriate filter outputs. The optimization engine is a new dynamic programming algorithm which determines the parametric curve of least energy, and can be used to optimize B-spline curves of any order. Results are demonstrated on a number of common angiograms including preand post-angioplasty coronary angiograms, and a carotid arteriogram. <<ETX>>
A simulation-based OSCE with case presentation and remote rating – development of a prototype

Simulation-based examination formats improve the possibility to assess medical students’ competences during their performance. Additionally, videotaping of simulations allows for remote rating, providing advantages for raters, students, and exam organizers. We describe a simulation-based OSCE prototype with remote rating of students’ competences, developed to replace a conventional OSCE at Hamburg Medical Faculty. The assessment consists of two phases: a consultation phase with four simulated patient encounters and a case presentation phase where four students present two cases each. All encounters from the consultation and the presentation phase are to be videotaped and remotely rated by clinical raters. Advanced medical students (year 4) are to be assessed regarding their clinical knowledge as well as physician-patient-communication, clinical reasoning competence, and patient management competence. We provide detailed schedules for the simulation-based OSCE procedure and a roster for organization. When piloting the assessment, we encountered two major obstacles with respect to legal obligations regarding examination time and videotaping which allowed us to provide tips on how to successfully implement this assessment prototype. Remote rating will, when successfully implemented, help students to concentrate on their consultation or presentation tasks, reduce raters’ time constraints and also allow for randomized rating. Using established instruments for competence-rating rather than OSCE checklists provides an additional feature for this simulation-based OSCE prototype. Legal issues can be avoided by using the prototype for formative assessment but should be addressed in advance when it is planned to be used as summative assessment.
Localizing Spatial Information in Neural Spatiospectral Filters

Beamforming for multichannel speech enhancement relies on the estimation of spatial characteristics of the acoustic scene. In its simplest form, the delay-and-sum beamformer (DSB) introduces a time delay to all channels to align the desired signal components for constructive superposition. Recent investigations of neural spatiospectral filtering revealed that these filters can be characterized by a beampattern similar to one of traditional beamformers, which shows that artificial neural networks can learn and explicitly represent spatial structure. Using the Complex-valued Spatial Autoencoder (COSPA) as an exemplary neural spatiospectral filter for multichannel speech enhancement, we investigate where and how such networks represent spatial information. We show via clustering that for COSPA the spatial information is represented by the features generated by a gated recurrent unit (GRU) layer that has access to all channels simultaneously and that these features are not source- but only direction of arrival-dependent.
Fast exploration and learning of latent graphs with aliased observations

We consider the problem of recovering a latent graph where the observations at each node are \emph{aliased}, and transitions are stochastic. Observations are gathered by an agent traversing the graph. Aliasing means that multiple nodes emit the same observation, so the agent can not know in which node it is located. The agent needs to uncover the hidden topology as accurately as possible and in as few steps as possible. This is equivalent to efficient recovery of the transition probabilities of a partially observable Markov decision process (POMDP) in which the observation probabilities are known. An algorithm for efficiently exploring (and ultimately recovering) the latent graph is provided. Our approach is exponentially faster than naive exploration in a variety of challenging topologies with aliased observations while remaining competitive with existing baselines in the unaliased regime.
Poisson-Nernst-Planck charging dynamics of an electric double layer capacitor: symmetric and asymmetric binary electrolytes

A parallel plate capacitor containing an electrolytic solution is the simplest model of a supercapacitor, or electric double layer capacitor. Using both analytical and numerical techniques, we solve the Poisson-Nernst-Planck equations for such a system, describing the mean-field charging dynamics of the capacitor, when a constant potential difference is abruptly applied to its plates. Working at constant total number of ions, we focus on the physical processes involved in the relaxation and, whenever possible, give its functional shape and exact time constants. We first review and study the case of a symmetric binary electrolyte, where we assume the two ionic species to have the same charges and diffusivities. We then relax these assumptions and present results for a generic strong (i.e. fully dissociated) binary electrolyte. At low electrolyte concentration, the relaxation is simple to understand, as the dynamics of positive and negative ions appear decoupled. At higher electrolyte concentration, we distinguish several regimes. In the linear regime (low voltages), relaxation is multi-exponential, it starts by the build-up of the equilibrium charge profile and continues with neutral mass diffusion, and the relevant time scales feature both the average and the Nernst-Hartley diffusion coefficients. In the purely nonlinear regime (intermediate voltages), the initial relaxation is slowed down exponentially due to increased capacitance, while bulk effects become more and more evident. In the fully nonlinear regime (high voltages), the dynamics of charge and mass are completely entangled and, asymptotically, the relaxation is linear in time.
The Newton-Raphson method for solving non-linear and anisotropic time-harmonic problems

This paper discusses the use of a complex‐valued reluctivity tensor for modelling non‐linear, anisotropic and hysteretic materials in a time‐harmonic finite element context. It is shown how these problems can be solved by the Newton‐Raphson method. The method is applied for the simulation of the magnetic field distribution in a three‐phase transformer.
Enhancing Measurements of the CMB Blackbody Temperature Power Spectrum by Removing CIB and Thermal Sunyaev-Zel'dovich Contamination Using External Galaxy Catalogs

Extracting the CMB blackbody temperature power spectrum -- which is dominated by the primary CMB signal and the kinematic Sunyaev-Zel'dovich (kSZ) effect -- from mm-wave sky maps requires cleaning other sky components. In this work, we develop new methods to use large-scale structure (LSS) tracers to remove cosmic infrared background (CIB) and thermal Sunyaev-Zel'dovich (tSZ) contamination in such measurements. Our methods rely on the fact that LSS tracers are correlated with the CIB and tSZ signals, but their two-point correlations with the CMB and kSZ signals vanish on small scales, thus leaving the CMB blackbody power spectrum unbiased after cleaning. We develop methods analogous to delensing ($\textit{de-CIB}$ or $\textit{de-(CIB+tSZ)}$) to clean CIB and tSZ contaminants using these tracers. We compare these methods to internal linear combination (ILC) methods, including novel approaches that incorporate the tracer maps in the ILC procedure itself, without requiring exact assumptions about the CIB SED. As a concrete example, we use the $\textit{unWISE}$ galaxy samples as tracers. We provide calculations for a combined Simons Observatory and $\textit{Planck}$-like experiment, with our simulated sky model comprising eight frequencies from 93 to 353 GHz. Using $\textit{unWISE}$ tracers, improvements with our methods over current approaches are already non-negligible: we find improvements up to 20% in the kSZ power spectrum signal-to-noise ratio (SNR) when applying the de-CIB method to a tSZ-deprojected ILC map. These gains could be more significant when using additional LSS tracers from current surveys, and will become even larger with future LSS surveys, with improvements in the kSZ power spectrum SNR up to 50%. For the total CMB blackbody power spectrum, these improvements stand at 4% and 7%, respectively. Our code is publicly available at https://github.com/olakusiak/deCIBing.
Verifying the Robustness of Automatic Credibility Assessment

Text classification methods have been widely investigated as a way to detect content of low credibility: fake news, social media bots, propaganda, etc. Quite accurate models (likely based on deep neural networks) help in moderating public electronic platforms and often cause content creators to face rejection of their submissions or removal of already published texts. Having the incentive to evade further detection, content creators try to come up with a slightly modified version of the text (known as an attack with an adversarial example) that exploit the weaknesses of classifiers and result in a different output. Here we systematically test the robustness of popular text classifiers against available attacking techniques and discover that, indeed, in some cases insignificant changes in input text can mislead the models. We also introduce BODEGA: a benchmark for testing both victim models and attack methods on four misinformation detection tasks in an evaluation framework designed to simulate real use-cases of content moderation. Finally, we manually analyse a subset adversarial examples and check what kinds of modifications are used in successful attacks. The BODEGA code and data is openly shared in hope of enhancing the comparability and replicability of further research in this area
Modeling and Analysis of Piezoelectric Energy Harvesting From Aeroelastic Vibrations Using the Doublet-Lattice Method

Multifunctional structures are pointed out as an important technology for the design of aircraft with volume, mass, and energy source limitations such as unmanned air vehicles (UAVs) and micro air vehicles (MAVs). In addition to its primary function of bearing aerodynamic loads, the wing/spar structure of an UAV or a MAV with embedded piezoceramics can provide an extra electrical energy source based on the concept of vibration energy harvesting to power small and wireless electronic components. Aeroelastic vibrations of a lifting surface can be converted into electricity using piezoelectric transduction. In this paper, frequency-domain piezoaeroelastic modeling and analysis of a cantilevered platelike wing with embedded piezoceramics is presented for energy harvesting. The electromechanical finite-element plate model is based on the thin-plate (Kirchhoff) assumptions while the unsteady aerodynamic model uses the doublet-lattice method. The electromechanical and aerodynamic models are combined to obtain the piezoaeroelastic equations, which are solved using a p-k scheme that accounts for the electromechanical coupling. The evolution of the aerodynamic damping and the frequency of each mode are obtained with changing airflow speed for a given electrical circuit. Expressions for piezoaeroelastically coupled frequency response functions (voltage, current, and electrical power as well the vibratory motion) are also defined by combining flow excitation with harmonic base excitation. Hence, piezoaeroelastic evolution can be investigated in frequency domain for different airflow speeds and electrical boundary conditions.
A Processing Framework to Access Large Quantities of Whispered Speech Found in ASMR

Whispering is a ubiquitous mode of communication that humans use daily. Despite this, whispered speech has been poorly served by existing speech technology due to a shortage of resources and processing methodology. To remedy this, this paper provides a processing framework that enables access to large and unique data of high-quality whispered speech. We obtain the data from recordings submitted to online platforms as part of the ASMR media-cultural phenomenon. We describe our processing pipeline and a method for improved whispered activity detection (WAD) in the ASMR data. To efficiently obtain labelled, clean whispered speech, we complement the automatic WAD by using Edyson, a bulk audio-annotation tool with human-in-the-loop. We also tackle a problem particular to ASMR: separation of whisper from other acoustic triggers present in the genre. We show that the proposed WAD and the efficient labelling allows to build extensively augmented data and train a classifier that extracts clean whisper segments from ASMR audio.Our large and growing dataset enables whisper-capable, data-driven speech technology and linguistic analysis. It also opens opportunities in e.g. HCI as a resource that may elicit emotional, psychological and neuro-physiological responses in the listener.
CoMeta: Enhancing Meta Embeddings with Collaborative Information in Cold-start Problem of Recommendation

The cold-start problem is quite challenging for existing recommendation models. Specifically, for the new items with only a few interactions, their ID embeddings are trained inadequately, leading to poor recommendation performance. Some recent studies introduce meta learning to solve the cold-start problem by generating meta embeddings for new items as their initial ID embeddings. However, we argue that the capability of these methods is limited, because they mainly utilize item attribute features which only contain little information, but ignore the useful collaborative information contained in the ID embeddings of users and old items. To tackle this issue, we propose CoMeta to enhance the meta embeddings with the collaborative information. CoMeta consists of two submodules: B-EG and S-EG. Specifically, for a new item: B-EG calculates the similarity-based weighted sum of the ID embeddings of old items as its base embedding; S-EG generates its shift embedding not only with its attribute features but also with the average ID embedding of the users who interacted with it. The final meta embedding is obtained by adding up the base embedding and the shift embedding. We conduct extensive experiments on two public datasets. The experimental results demonstrate both the effectiveness and the compatibility of CoMeta.
Physical defintion of randomness

Ability to generate random numbers is an important resource for many applications ranging from scientific research to practical cryptography and quantum technologies. However, a widely accepted definition of random numbers, or randomness, has eluded researchers thus far. Without a definition, it is impossible to complete security proofs or make new industrial standards. Here, we propose an information-theory-based definition of randomness which, unlike state of the art, does not try to find desirable properties of generated numbers, but rather focus on the physical process of random number generation. We explain its intuitiveness, demonstrate its verifiability and further define randomness deviation as a measure of quality of the random number generating process or device.
Efficient Image-Text Retrieval via Keyword-Guided Pre-Screening

Under the flourishing development in performance, current image-text retrieval methods suffer from $N$-related time complexity, which hinders their application in practice. Targeting at efficiency improvement, this paper presents a simple and effective keyword-guided pre-screening framework for the image-text retrieval. Specifically, we convert the image and text data into the keywords and perform the keyword matching across modalities to exclude a large number of irrelevant gallery samples prior to the retrieval network. For the keyword prediction, we transfer it into a multi-label classification problem and propose a multi-task learning scheme by appending the multi-label classifiers to the image-text retrieval network to achieve a lightweight and high-performance keyword prediction. For the keyword matching, we introduce the inverted index in the search engine and create a win-win situation on both time and space complexities for the pre-screening. Extensive experiments on two widely-used datasets, i.e., Flickr30K and MS-COCO, verify the effectiveness of the proposed framework. The proposed framework equipped with only two embedding layers achieves $O(1)$ querying time complexity, while improving the retrieval efficiency and keeping its performance, when applied prior to the common image-text retrieval methods. Our code will be released.