Miracle-dz/UniFAR-FTUs huggingface dataset:
EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous Variables

While exogenous variables have a major impact on performance improvement in time series analysis, interseries correlation and time dependence among them are rarely considered in the present continuous methods. The dynamical systems of multivariate time series could be modeled with complex unknown partial differential equations (PDEs) which play a prominent role in many disciplines of science and engineering. In this article, we propose a continuous-time model for arbitrary-step prediction to learn an unknown PDE system in multivariate time series whose governing equations are parameterized by self-attention and gated recurrent neural networks. The proposed model, exogenous-guided PDE network (EgPDE-Net), takes account of the relationships among the exogenous variables and their effects on the target series. Importantly, the model can be reduced into a regularized ordinary differential equation (ODE) problem with specially designed regularization guidance, which makes the PDE problem tractable to obtain numerical solutions and feasible to predict multiple future values of the target series at arbitrary time points. Extensive experiments demonstrate that our proposed model could achieve competitive accuracy over strong baselines: on average, it outperforms the best baseline by reducing 9.85% on RMSE and 13.98% on MAE for arbitrary-step prediction.
Quantum fluctuations, quanta of electromagnetic interaction, quantum electronic bound states

Based on the concepts of the quantum field theory of virtual photons as quanta of electromagnetic interaction, we discuss the physical content of the phenomena underlying the principle of quantum uncertainties. We consider the features of the uncertainty relations and the properties of the elementary particles (electrons, protons, etc.) under the conditions of the formation of quantum bound states at atomic and subatomic distances.
A Feature-space Multimodal Data Augmentation Technique for Text-video Retrieval

Every hour, huge amounts of visual contents are posted on social media and user-generated content platforms. To find relevant videos by means of a natural language query, text-video retrieval methods have received increased attention over the past few years. Data augmentation techniques were introduced to increase the performance on unseen test examples by creating new training samples with the application of semantics-preserving techniques, such as color space or geometric transformations on images. Yet, these techniques are usually applied on raw data, leading to more resource-demanding solutions and also requiring the shareability of the raw data, which may not always be true, e.g. copyright issues with clips from movies or TV series. To address this shortcoming, we propose a multimodal data augmentation technique which works in the feature space and creates new videos and captions by mixing semantically similar samples. We experiment our solution on a large scale public dataset, EPIC-Kitchens-100, and achieve considerable improvements over a baseline method, improved state-of-the-art performance, while at the same time performing multiple ablation studies. We release code and pretrained models on Github at https://github.com/aranciokov/FSMMDA\_VideoRetrieval.
Distributed Event-Triggered Nonlinear Fusion Estimation Under Resource Constraints

This article studies the event-triggered distributed fusion estimation problems for a class of nonlinear networked multisensor fusion systems without noise statistical properties. In practice, the sensor-to-remote estimator channel and the smart sensor-to-fusion center channel of the communication network will be faced with some resource constraint problems. To meet the finite communication resources during the information transmission, an event-triggered strategy and a dimensionality reduction strategy are introduced in a unified network framework to reduce the communication traffic. Since the reduction of communication information will inevitably degenerate the estimation performance, two kinds of compensation strategies in terms of a unified model are proposed to restructure the untransmitted information. Then, the local/fusion estimators are designed based on the compensation information. Moreover, the linearization errors caused by the Taylor expansion are modeled by the state-dependent matrices with uncertain parameters when establishing estimation error systems, and then, different robust recursive optimization problems are established to determine the estimator gains and the fusion criteria. Meanwhile, the stability conditions are also presented such that the square errors of the designed nonlinear estimators are asymptotically bounded. Finally, a vehicle localization system is employed to demonstrate the effectiveness and advantages of the proposed methods.
Abusing Commodity DRAMs in IoT Devices to Remotely Spy on Temperature

The ubiquity and pervasiveness of modern Internet of Things (IoT) devices opens up vast possibilities for novel applications, but simultaneously also allows spying on, and collecting data from, unsuspecting users to a previously unseen extent. This paper details a new attack form in this vein, in which the decay properties of widespread, off-the-shelf DRAM modules are exploited to accurately spy on the temperature in the vicinity of the DRAM-carrying device. Among others, this enables adversaries to remotely and purely digitally spy on personal behavior in users’ private homes, or to collect security-critical data in server farms, cloud storage centers, or commercial production lines. We demonstrate that our attack can be performed by merely compromising the software of an IoT device and does not require hardware modifications or physical access at attack time. It can achieve temperature resolutions of up to <inline-formula> <tex-math notation="LaTeX">$\mathbf {0.5^{\circ} }\text{C}$ </tex-math></inline-formula> over a range of <inline-formula> <tex-math notation="LaTeX">$\mathbf {0^{\circ} }\text{C}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$\mathbf {70^{\circ} }\text{C}$ </tex-math></inline-formula> in practice. The presented attack works in devices that do not have a dedicated temperature sensor on board; as the DRAM modules already present in the device are abused to spy on the temperature. To complete the work, the paper discusses practical attack scenarios as well as possible countermeasures against the new temperature-spying attacks.
Synthetic Next Generation Very Large Array line observations of a massive star-forming cloud

Study of pre-stellar cloud evolution requires observations with high sensitivity and resolution, and regions of high-mass star formation are particularly challenging. We wish to quantify, to what accuracy the physical conditions within a massive star-forming cloud can be determined from observations. We are particularly interested in the possibilities offered by the Next Generation VLA (ngVLA) interferometer. We use data from a magnetohydrodynamic simulation of star-formation and concentrate on a filamentary structure that has physical properties similar to an infrared-dark cloud. We produce synthetic ngVLA observations of spectral lines and analyse the column density, gas temperature, and kinematics. The results are compared to ideal observations and the actual 3D model. For a distance of 4 kpc, ngVLA provides a resolution of 0.01 pc even in its most compact configuration. For abundant molecules, such as HCO+, NH3, N2H+, and CO isotopomers, cloud kinematics and structure can be mapped down to sub-arcsec scales. For NH3, a reliable column density map could be obtained for the entire 15 * 40 arcsec cloud, even without additional single-dish data, and kinetic temperatures are recovered to a precision of 1 K. At higher frequencies, the loss of large-scale emission is noticeable. The line observations accurately trace the cloud kinematics, except for the largest scales. The line-of-sight confusion complicates the interpretation of the kinematics, and limits the usefulness of collapse indicators based on the blue asymmetry of optically thick lines. The ngVLA will provide accurate data on the small-scale structure and the physical and chemical state of clouds, even in high-mass star-forming regions at kiloparsec distances. Complementary single-dish data are still essential for estimates of the total column density and the large-scale kinematics.
Introducing BEREL: BERT Embeddings for Rabbinic-Encoded Language

We present a new pre-trained language model (PLM) for Rabbinic Hebrew, termed Berel (BERT Embeddings for Rabbinic-Encoded Language). Whilst other PLMs exist for processing Hebrew texts (e.g., HeBERT, Aleph-Bert), they are all trained on modern Hebrew texts, which diverges substantially from Rabbinic Hebrew in terms of its lexicographi cal, morphological, syntactic and orthographic norms. We demonstrate the superiority of Berel on Rabbinic texts via a challenge set of Hebrew homographs. We release the new model and homograph challenge set for unrestricted use.
Sharpening Your Tools: Updating bulk_extractor for the 2020s

Bulk_extractor is a high-performance digital forensics tool written in C++. Between 2018 and 2022 we updated the program from C++98 to C++17, performed a complete code refactoring, and adopted a unit test framework. The new version typically runs with 75\% more throughput than the previous version, which we attribute to improved multithreading. We provide lessons and recommendations for other digital forensics tool maintainers.
Skyrmion and vortex crystals in the Hubbard model

A mutual interplay between the charge and spin degrees of freedom in itinerant magnets leads to a plethora of topological spin textures, such as magnetic skyrmion and vortex crystals, in both centrosymmetric and noncentrosymmetric hosts. Meanwhile, their stabilization has been exten-sively studied in the system including the classical localized spins. We here study a realization of the skyrmion crystal in the centrosymmetric triangular-lattice Hubbard model, where the itinerant nature of electrons plays a more signiﬁcant role. By performing the self-consistent mean-ﬁeld calculations, we ﬁnd that two types of skyrmion crystals with spatially nonuniform charge modulations appear in the ground state at a zero magnetic ﬁeld. Moreover, we obtain another noncoplanar vortex crystal phase without a net scalar chirality in the vicinity of the skyrmion crystal phase. We show that the latter vortex crystal exhibits the topological phase transition to a diﬀerent skyrmion phase in an applied magnetic ﬁeld. Our results provide a possibility of the skyrmion and vortex crystals in itinerant magnets without the localized moments.
Link Prediction on Heterophilic Graphs via Disentangled Representation Learning

Link prediction is an important task that has wide applications in various domains. However, the majority of existing link prediction approaches assume the given graph follows homophily assumption, and designs similarity-based heuristics or representation learning approaches to predict links. However, many real-world graphs are heterophilic graphs, where the homophily assumption does not hold, which challenges existing link prediction methods. Generally, in heterophilic graphs, there are many latent factors causing the link formation, and two linked nodes tend to be similar in one or two factors but might be dissimilar in other factors, leading to low overall similarity. Thus, one way is to learn disentangled representation for each node with each vector capturing the latent representation of a node on one factor, which paves a way to model the link formation in heterophilic graphs, resulting in better node representation learning and link prediction performance. However, the work on this is rather limited. Therefore, in this paper, we study a novel problem of exploring disentangled representation learning for link prediction on heterophilic graphs. We propose a novel framework DisenLink which can learn disentangled representations by modeling the link formation and perform factor-aware message-passing to facilitate link prediction. Extensive experiments on 13 real-world datasets demonstrate the effectiveness of DisenLink for link prediction on both heterophilic and hemophiliac graphs. Our codes are available at https://github.com/sjz5202/DisenLink
OP-NDTJ200157 1..8

Background. Phosphate is absorbed in the small intestine via passive flow and active transport.NaPi-IIb, a type II sodiumdependent phosphate transporter, is considered to mediate active phosphate transport in rodents. To study the regulation of intestinal phosphate transport in chronic kidney disease (CKD), we analyzed the expression levels of NaPi-IIb, pituitaryspecific transcription factor 1 (PiT-1) and PiT-2 and the kinetics of intestinal phosphate transport using two CKD models. Methods. CKD was induced in rats via adenine orThy1 antibody injection. Phosphate uptake by intestinal brush border membrane vesicles (BBMV) and the messenger RNA (mRNA) expression of NaPi-IIb, PiT-1 and PiT-2 were analyzed. The protein expression level of NaPi-IIb was measured by mass spectrometry (e.g. liquid chromatography tandem mass spectrometry). Results. In normal rats, phosphate uptake into BBMV consisted of a single saturable component and its Michaelis constant (Km) was comparable to that of NaPi-IIb. The maximum velocity (Vmax) correlated with mRNA and protein levels of NaPi-IIb. In the CKD models, intestinal phosphate uptake consisted of two saturable components. The Vmax of the higher-affinity transport, which is thought to be responsible for NaPi-IIb, significantly decreased and the decrease correlated with reduced NaPi-IIb expression. The Km of the lower-affinity transport was comparable to that of PiT-1 and -2. PiT-1 mRNA expression was much higher than that of PiT-2, suggesting that PiT-1 was mostly responsible for phosphate transport. Conclusions. This study suggests that the contribution of NaPiIIb to intestinal phosphate absorption dramatically decreases in rats with CKD and that a low-affinity alternative to NaPi-IIb, in particular PiT-1, is upregulated in a compensatory manner in CKD.
Automatic Discrimination of Domestic Cat Sounds and Imitations

This study presents the results of automatic discrimination to distinguish the sounds of domestic cats and the sounds of domestic cats imitated by humans. In this work, our main contribution is domestic cat sound imitation data set creation and investigation of classification accuracy both between original cat sounds and imitations mutually. First, we analyzed the specific types of vocalization of cats, which are the main representatives of the cat sound categories. Then, we use Freesound to create a data set of these specific type of cat sounds vocalizations. We created an imitation data set consisting of the vocalizations of the humans precisely for the same categories. We use Librosa to extract the features of these new data sets, and Weka to distinguish cat sounds from their imitations by using machine learning techniques.
AIS Electronic Library (AISeL) Individual Relationships with Technology

In this paper, we explore the nature of the relationships that people develop with technology. In particular, we examine social exchange theory, the norm of reciprocity, and attachment theory to begin to explain how individuals develop, or do not develop, relationships with computers and technology. The theories are summarized, hypotheses are presented, proposed methods for empirical work are presented, and brief discussion is offered suggesting that the reference theories identified provide a novel way to examine the relationship between people and the technologies they interact with.
Shape optimization of a floating body

In the present thesis we consider a variational formulation of a floating body in a perfect fluid. In particular, we study the case of a stationary two-dimensional potential flow and thus we are able to replace the velocity potential by its harmonic conjugate which is called the stream function. Compared to the velocity potential the stream function satisfies Dirichlet data instead of Neumann data on the boundary and we can characterize the liquid set as the positivity set of the stream function. The resulting energy functional depends on both stream function and floating body. We realize these two minimizations by working in two separated steps. When minimizing the stream function we work with the constraint that the volume of the fluid has to be constant. However, in order to use non-volume preserving perturbations we add a penalty term and disregard the volume condition. The choice of the penalty term yields an approximation of the original functional. Using direct methods we prove the existence of a minimizer of the penalized problem. Moreover, we show that the minimizer is bounded and subharmonic. By a construction of two appropriate comparison functions we get equivalence of the original and the penalized problem provided we have chosen adequate parameters. Furthermore, we use a technique of Morrey in order to show Hölder continuity and a method of Alt and Caffarelli in order to get Lipschitz continuity of the stream function, which is the maximal regularity which can be proved. A nondegeneracy property of the stream function leads to density estimates on the free surface of the fluid which imply that the free boundary has locally finite perimeter. We use the concept of blow-up limits to deduce gradient estimates on the surface of the fluid which finally lead to the statement that the reduced boundary is locally a C1,β surface. A further result which is only valid in a two-dimensional setting extends the regularity to the whole boundary. We look for the optimal floating body in the family of all compact sets with prescribed volume and a priori bounded number of connected components. In addition, we postulate boundedness of the density perimeter of the boundary in order to avoid oscillations that may eventually appear. Again we use direct methods and work with two different notions of domain convergence, namely Hausdorff convergence and γconvergence. In both cases we are able to show the existence of an optimal floating body.
TOWARDS A HYBRID PERSONALIZED MOVIE RECOMMENDER SYSTEM

Recommender systems represent a powerful method for enabling users to filter through wide verity of information. Research in the recommender system is moving in the direction of a richer understanding of how recommender technology may be embedded in specific domains. A recommendation system for movies is important in our social life to provide the enhanced entertainment . There are two major recommendation techniques –Collaborative and content based filtering but these filtering techniques are having some limitations thus an hybrid approach is often adopted. The proposed movie recommender system has an ability to recommend user specific movie by using their social networking site like Facebook data. This provides the generalized framework for personalized movie recommendation.
Model-Driven Clustering of Time-Course Gene Expression Data

In the last decade, various methods for clustering the microarrays data have been developped, such as the hierachical clustering [1], k-means [5] or SOM (self-organizing maps) [3, 5]. These methods are not adapted to time-course expression data, because the time dependence of the observed data is not preserved. Here, we attempt of elaborating a novel approach combining the self organizing maps (SOM) method [2] with a model space. The parameter space is the set of the parameter vectors controlling a given class of deterministic time-course models able to approximate the expression profiles. The model space is a discrete subspace of this space, i.e., a network of parameter vectors initially defined which will be optimized by a recursive procedure similar to that used in SOM. The ”model-driven clustering” consists in locating initially the observed expression data in a model network, i.e., a subset of parameter vectors, and then in optimizing this subset for identifying the optimal parameters associated with data, but also charaterizing the clusters by making the nodes closer. By a procedure similar to that used in SOM, we displace the network nodes to improve the similarity between the time-course expression data and the possible models. We define areas of high density of models so characterizing the clusters.
A randomised controlled trial in patients undergoing arthroscopic shoulder surgery comparing interscalene block with either 10 ml or 20 ml levobupivacaine 0.25%

The interscalene brachial plexus block is recommended for analgesia after shoulder surgery but it may cause hemidiaphragmatic dysfunction. We tested whether ipsilateral hemidiaphragmatic contraction was better after a smaller dose of local anaesthetic without impairing analgesic effect. We randomly allocated 48 adults to 10 ml or 20 ml levobupivacaine 0.25% before arthroscopic shoulder surgery. The primary outcome was hemidiaphragmatic paralysis, defined as inspiratory thickness < 1.2 times expiratory thickness, measured by ultrasound 4 h after block. Hemidiaphragmatic paralysis was recorded for 6/24 vs. 23/24 supine participants after 10 ml vs. 20 ml levobupivacaine 0.25%, respectively, and for 4/24 vs. 23/24 sitting participants, respectively, p < 0.001 for both. Pain scores after 10 ml injectate were not worse than after 20 ml injectate. Median (IQR [range]) morphine doses in the first 24 postoperative hours after 10 ml and 20 ml levobupivacaine 0.25% were 2 (0–6 [0–23]) mg vs. 1 (0–2 [0–11]) mg, respectively, p = 0.12. No participant had a complication after 10 ml interscalene levobupivacaine, whereas seven had complications after 20 ml levobupivacaine, p = 0.009. Hemidiaphragmatic function was better after 10 ml vs. 20 ml interscalene levobupivacaine 0.25% without impairing analgesia for 24 postoperative hours.