Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing drug repurposing on graphs by integrating drug molecular structure as feature

Drug repurposing has become increasingly important, particularly in light of the COVID-19 pandemic. This process involves identifying new therapeutic uses for existing drugs, which can significantly reduce the cost, risk, and time associated with developing new drugs, de novo development. A previous conducted study proved that Deep Learning can be used to streamline this process by identifying drug repurposing hypotheses. The study presented a model called REDIRECTION, which utilized the rich biomedical information available in graph form and combined it with Geometric Deep Learning to find new indications for existing drugs. The reported metrics for this model were 0.87 for AUROC and 0.83 for AUPRC. In this current study, the importance of node features in GNNs is explored. Specifically, the study used GNNs to embed two-dimensional drug molecular structures and obtain corresponding features. These features were incorporated into the drug repurposing graph, along with some other enhancements, resulting in an improved model called DMSR. Performance score for the reported metrics values raised by 0.0448 in AUROC and 0.0919 in AUPRC. Based on these findings, we believe that the method used for embedding drug molecular structures is interesting and captures valuable information about drugs. Its incorporation in the graph for drug repurposing can significantly benefit the process, leading to improved performance evaluation metrics.
Bialgebraic description of generalized binary methods and other topics in the semantics of object-oriented languages

Object-Oriented Languages support a very flexible programming paradigm for implementing data abstraction. However, there does not exist as yet an established approach to a formal semantics for such languages. This is unsatisfactory, since a formal semantics is crucial for developing full fledged formal methods for any programming language. The present thesis provides contribution towards a robust development of a coalgebraic semantics for Object-Oriented Languages. In particular, it addresses the critical issue of dealing with binary methods. These are methods that take more than one parameter of a class type. Although this problem has been dealt with before in the literature, we feel that it has not yet been solved in a completely satisfactory way. The appeal of coalgebraic methods is their considerably low mathematical over head and their operational nature. The solution we propose, we think fits naturally in this spirit. More specially: we extend the H.Reichel and B.Jacobs coalgebraic account of specification and refinement of objects and classes in Object Oriented Programming to (generalized) binary methods. Generalized binary methods are methods whose type parameters are built over constants and class variables, using products, sums and powerset type constructors. In order to take care of class constructors, we model classes as bialgebras. We study and compare two solutions for modelling generalized binary methods, which use, somewhat surprisingly, only, purely covariant functors. In the first solution, which applies when we already have a class implementation, we reduce the behaviour of a generalized binary method to that of a bunch of unary methods. These are obtained by freezing the types of the extra class parameters to constant types. The bisimulation behavioural equivalence induced on objects by this model amounts to the greatest congruence with respect to method application. Alternatively, in the second solution, we treat binary methods as graphs instead of functions, thus turning contravariant occurrences in the functor into covariant ones. We investigate a coalgebraic semantics for the class-based object oriented imperative language, Fickle, introduced and studied by Drossopoulou et al. Fickle is a Java-like language, extending Java with object re-classification. First we investigate notions of observational equivalences over Fickle programs. Then, in order to study them, we define a coalgebraic model for Fickle programs. However, in order to deal with the store, we need to extend the original approach of H.Reichel and B.Jacobs, which is purely functional. Finally, we address the problem of typing binary methods when subclasses are considered as subtypes. We propose yet another solution, inspired by a view of functions as graphs, based on a new typing system, where one can annotate in the type of an object whether a method is never called on that object.
Effect of added TiO 2 on the mechanical properties of sintered Al 2 O 3 insulator

: The effect of TiO 2 additive concentration and sintering temperatures on the densification of alumina (Al 2 O 3 ) ceramics were investigated. Densified alumina specimens were prepared by uniaxial pressing at a pressure of 60 MPa followed by isothermal heating at either 1400, 1500, or 1600 o C for 2 h. The relative density, microstructure, crystallinity, and hardness were investigated, and the correlation between properties and structure was discussed in relation to TiO 2 addition and sintering temperature. The densities of the 0.05wt%-TiO 2 specimens sintered at 1500 and 1600 o C had higher values than the pure alumina specimens. Adding more than 5.0wt% TiO 2 lowered the relative density. The maximum achieved density was 99.7% at 0.05wt% TiO 2 addition. Shrinkage and hardness analyses confirmed that the sintering temperature for the Al 2 O 3 insulator could be lowered by adding TiO 2. The optimal concentration to obtain a dense alumina with high hardness for insulator use was determined to be in the range of 0.05 to 1.0 wt% of TiO 2 . These results provide fundamental composition and process optimization data for the development of high-dielectric alumina insulators.
Materials for terahertz science and technology

Terahertz spectroscopy systems use far\|infrared radiation to extract molecular spectral information in an otherwise inaccessible portion of the electromagnetic spectrum. Materials research is an essential component of modern terahertz systems; novel, higher\|power terahertz sources rely heavily on new materials such as quantum cascade structures. At the same time, terahertz spectroscopy and imaging provide a powerful tool for the characterization of a broad range of materials, including semiconductors and biomolecules.
A discrete-time storage process with a general release rule

A discrete-time storage system with a general release rule and stationary nonnegative inflows is examined. A simple condition is found for the existence of a stationary storage and outflow for a general possibly non-monotone release function. It is also shown that in the Markov case (i.e. independent inflows) these distributions are unique under certain conditions. It is demonstrated that under these conditions the stationary behaviour in the Markov case varies continuously with parametric changes in the release rule. This result is used to prove convergence of a finite state space approximation for the Markov storage system.
Speech and Noise Dual-Stream Spectrogram Refine Network With Speech Distortion Loss For Robust Speech Recognition

In recent years, the joint training of speech enhancement front-end and automatic speech recognition (ASR) back-end has been widely used to improve the robustness of ASR systems. Traditional joint training methods only use enhanced speech as input for the backend. However, it is difficult for speech enhancement systems to directly separate speech from input due to the diverse types of noise with different intensities. Furthermore, speech distortion and residual noise are often observed in enhanced speech, and the distortion of speech and noise is different. Most existing methods focus on fusing enhanced and noisy features to address this issue. In this paper, we propose a dual-stream spectrogram refine network to simultaneously refine the speech and noise and decouple the noise from the noisy input. Our proposed method can achieve better performance with a relative 8.6% CER reduction.
D-3DLD: Depth-Aware Voxel Space Mapping for Monocular 3D Lane Detection with Uncertainty

The estimation of 3D lanes from monocular RGB images is a fundamentally ill-posed problem. Previous studies have assumed that all lanes are on a flat ground plane. However, we argue that the algorithms based on this assumption have difficulty in detecting various lanes in actual driving environments. Contrary to previous approaches, we expand rich contextual features from an image domain to a 3D space by utilizing depth-aware voxel mapping. In addition, we determine 3D lanes based on voxelized features. We design a new lane representation combined with uncertainties and predict the confidence intervals of 3D lane points using Laplace loss. Experimental results show that the proposed method achieves state-of-the-art detection accuracy on three challenging datasets, including two real-world datasets, and significantly outperforms existing methods with reasonable computation load.
Beyond Rate Coding: Signal Coding and Reconstruction Using Lean Spike Trains

Recent years have seen a growing interest in spike based encoding of continuous time signals–a hallmark of biological computation. In this context, we present a mathematical framework for signal representation, leveraging a simple but robust mechanistic model of a biologically plausible spiking neuron. The framework considers encoding of a signal through spike trains generated by an ensemble of neurons via a standard convolve-then-threshold mechanism, albeit with a wide variety of convolution kernels. Reconstruction is posited as a convex optimization minimizing energy. Formal conditions under which perfect and approximate reconstruction of the signal from the spike trains is possible are then identified. The strength of the framework is shown in experiments on a large audio dataset, demonstrating good reconstruction at a spike rate of one fifth the Nyquist rate. Comparison against a benchmark sparse coding technique, viz convolutional orthogonal matching pursuit, shows competitive results in reconstruction with orders of magnitude improvement in runtime efficiency.
Structured-Anchor Projected Clustering for Hyperspectral Images

Hyperspectral image (HSI) clustering seeks to assign each pixel to a specific class without trained labels. This is a challenging task owing to the spatial and spectral complexity. Recently, anchor graph-based clustering has attracted considerable attention due to its flexibility in handling large-scale HSI data. However, these methods typically disregard noisy bands and require post-processing. To tackle these issues, we propose a structured-anchor projected clustering (SAPC) model for HSIs. In SAPC, the projection clustering is introduced into anchor graph learning to suppress noise, and the Laplacian rank constraints can quickly obtain the structure of anchors. Thus, we can directly obtain the clustering results through the anchor graph and the structured anchors. Moreover, we propose an iterative optimization method to efficiently solve the SAPC model. Extensive experiments show that our model achieves superior results.
Improved Mask-Based Neural Beamforming for Multichannel Speech Enhancement by Snapshot Matching Masking

In multichannel speech enhancement (SE), time-frequency (T-F) mask-based neural beamforming algorithms take advantage of deep neural networks to predict T-F masks that represent speech and noise dominance. The predicted masks are subsequently leveraged to estimate the speech and noise power spectral density (PSD) matrices for computing the beamformer filter weights based on signal statistics. However, in the literature most networks are trained to estimate some pre-defined masks, e.g., the ideal binary mask (IBM) and ideal ratio mask (IRM) that lack direct connection to the PSD estimation. In this paper, we propose a new masking strategy to predict the Snapshot Matching Mask (SMM) that aims to minimize the distance between the predicted and the true signal snapshots, thereby estimating the PSD matrices in a more systematic way. Performance of SMM compared with existing IBM- and IRM-based PSD estimation for mask-based neural beamforming is presented on several datasets to demonstrate its effectiveness for the SE task.
Elastic Graph Transformer Networks for EEG-Based Emotion Recognition

Electroencephalogram (EEG) has been applied in emotion recognition due to excellent temporal resolution with less competitive spatial resolution. This leads to the consequence that the majority of EEG-based emotion recognition models emphasize on exploiting temporal features while ignoring the efficient information provided by spatial resolution. To extract more informative representations, we propose an elastic Graph Transformer network for emotion recognition (EmoGT) inspired by the advantages of Transformer in time-series analysis and the superior performance of graph convolutional networks in topological analysis. Moreover, it is able to be flexibly expanded to cope with multimodal inputs by employing specially designed structures. Experimental results on 3 public datasets demonstrate that our models outperform the state-of-the-art results by 3% on average in both single and multimodal cases, indicating the effectiveness of utilizing temporal and spatial information simultaneously.
SADI: A Self-Adaptive Decomposed Interpretable Framework for Electric Load Forecasting Under Extreme Events

Accurate prediction of electric load is crucial in power grid planning and management. In this paper, we solve the electric load forecasting problem under extreme events such as scorching heats. One challenge for accurate forecasting is the lack of training samples under extreme conditions. Also load usually changes dramatically in these extreme conditions, which calls for interpretable model to make better decisions. In this paper, we propose a novel forecasting framework, named Self-adaptive Decomposed Interpretable framework (SaDI), which ensembles long-term trend, short-term trend, and period modelings to capture temporal characteristics in different components. The external variable triggered loss is proposed for the imbalanced learning under extreme events. Furthermore, Generalized Additive Model (GAM) is employed in the framework for desirable interpretability. The experiments on both Central China electric load and public energy meters from buildings show that the proposed SaDI framework achieves average 22.14% improvement compared with the current state-of- the-art algorithms in forecasting under extreme events in terms of daily mean of normalized RMSE. Code, Public datasets, and Appendix are available at: https://doi.org/10.24433/CO.9696980. v1.
Rapid Eye Localization Based on Projection Peak

An eye localization method based on projection peak is presented.Firstly,according to the proportional relationships of face features,the eye region from an face image is segmented by setting appropriate candidate window.Then,by means of histogram analysis of the eye region image,a threshold is obtained and binary transform is performed to segment the eyes out of the eye region.Next,a series of projection peak is derived from vertical and horizontal gray projection curves of the binary image.Through comparison and analysis,the exact coordinates of the eyes is finally confirmed.The presented method has no need of any previous knowledge and training process.Experimental results on three face databases show that the proposed method is effective,accurate and rapid in eye localization and it meets the requirement of real-time face recognition system entirely.
Insights into Chemical Interactions and Related Toxicities of Deep Eutectic Solvents with Mammalian Cells Observed Using Synchrotron Macro–ATR–FTIR Microspectroscopy

Deep eutectic solvents (DESs) and ionic liquids (ILs) are highly tailorable solvents that have shown a lot of promise for a variety of applications including cryopreservation, drug delivery, and protein stabilisation. However, to date, there is very limited information on the detailed interactions of these solvents with mammalian cells. In this work, we studied six DESs and one IL that show promise as cryoprotective agents, applying synchrotron macro–ATR–FTIR to examine their effects on key biochemical components of HaCat mammalian cells. These data were paired with resazurin metabolic assays and neutron reflectivity experiments to correlate cellular interactions with cellular toxicity. Stark differences were observed even between solvents that shared similar components. In particular, it was found that solvents that are effective cryoprotective agents consistently showed interactions with cellular membranes, while high toxicity correlated with strong interactions of the DES/IL with nucleic acids and proteins. This work sheds new light on the interactions between novel solvents and cells that may underpin future biomedical applications.
Distributed Signal Processing for Out-of-System Interference Suppression in Cell-Free Massive MIMO

Cell-free massive multiple-input-multiple-output (CF-mMIMO) is a next-generation wireless access technology that offers superior coverage and spectral efficiency compared to conventional MIMO. With many future applications in unlicensed spectrum bands, networks will likely experience and may even be limited by out-of-system (OoS) interference. The OoS interference differs from the in-system interference from other serving users in that for OoS interference, the associated pilot signals are unknown or non-existent, which makes estimation of the OoS interferer channel difficult.In this paper, we propose a novel sequential algorithm for the suppression of OoS interference for uplink CF-mMIMO with a stripe (daisy-chain) topology. The proposed method has comparable performance to that of a fully centralized interference rejection combining algorithm but has substantially less fronthaul load requirements.
Transient Dictionary Learning for Compressed Time-of-Flight Imaging

Time-of-Flight imaging aims to retrieve the 3D geometry of a scene from the delay that a modulated light waveform experiences when interacting with the former. Multi-path interference, arising from translucent objects or concave geometries, poses a challenge when the problem is to be solved from few measurements. In this work, we step aside from mainstream deep learning methods to invert the problem and propose exploiting underlying sparsity in an appropriate basis, in combination with compressive sampling schemes. More specifically, we show that the temporal response functions of real-life scenes are of bounded complexity and can be sparsely represented in a learned dictionary. A variety of sparse dictionary learning techniques are considered to find appropriate time-domain bases. Uniform frequency-domain sampling is compared to random sampling schemes and sparse rulers. Simulations acknowledge the superiority of non-uniform sampling and confirm that both transient profiles and millimeter-accurate depth images can be successfully reconstructed from few measurements.
Convergence Analysis of Graphical Game-Based Nash Q−Learning using the Interaction Detection Signal of N−Step Return

The graphical game provides an effective method for modeling different kinds of sparse interactions in multi-agent reinforcement learning. Most previous work on game abstraction lacks theoretical guarantees of convergence. In this paper, we adopt the ${\mathcal{N}}$-step return signal to detect interactions between agents and build the Markov graphical game based on it. We analyze that the solution of the Markov graphical game is an ϵ-Nash equilibrium which guarantees the convergence of the proposed NSR-G2NashQ algorithm theoretically. Also, we have done experiments in different multi-agent reinforcement learning tasks with both tabular and function approximation solutions. The results show the NSR-G2NashQ algorithm accelerates the convergence of agents to the optimal policy.
On The Primal and Dual Formulations Of The Discrete Mumford-Shah Functional

This work focuses on the discrete Mumford-Shah (D-MS) functional which aims to perform jointly image reconstruction and contour detection but at the price of minimizing a non-convex objective function. This functional was of main interest during the 90’s but was then forsaken in order to focus on the unique restoration task relying on non-smooth convex minimization. Recent advances about D-MS were dedicated to alternative objective functions for which efficient numerical solution based on proximal iterations can be designed. In the 90’s literature about D-MS, equivalences between primal and dual formulations were derived. However, in the framework obtained by more recent developments dedicated to DMS such an equivalence was not yet derived and it is the goal of this work. By providing both a primal and dual formulation, a large panel of algorithms can be employed including recent proximal-based algorithms benefiting of good convergence behavior, especially due to KL properties and also most standard methods such as BFGS.
A New Personalized Efficacy Atlas for Pallidal Deep Brain Stimulation

Neurostimulation is to implant implants electrodes into deep brain structures to treat drug-resistant motor disorder in Parkinson’s disease. Unfortunately, it remains challenging to find the optimal electrode implanted and activated location at deep anatomical regions and achieve the optimal surgical function or performance on patients. This paper proposes to create a novel personalized efficacy atlas that warps functional scales and estimates activation volume by modeling electric field to characterize the link between electrode location and neurosurgical performance. We used a population of 32 globus pallidus stimulated patient data to construct such an atlas. The experimental results demonstrate that modeling electric field centered at actually electrode implanted positions outperforms Gaussian kernel modeling to predict activation volume for functional atlas creation. In particular, our new atlas provides an automatic and accurate electrode implantation method with a guidance accuracy 1.26 mm, which is better than other approaches. Additionally, two phases of the functional scales obtained from 3 and 6 months after neurostimulation were compared to create the new atlas. The 6-month phase gives a better efficacy map.
Robust Network Topologies for Distributed Learning

The robustness of networks against malicious agents is a critical issue for their reliability in distributed learning. While a significant number of works in recent years have investigated the development of robust algorithms for distributed learning, few have examined the influence and design of the underlying network topology on robustness. Robust schemes for distributed learning typically require certain conditions on the arrangement of malicious agents in the network. In particular, the majority of neighbors of any benign agent must be benign, and the subgraph of benign agents must be connected. In this work, we propose a scheme for the design of such topologies based on prior information of the risk profile of participating agents. We show that the resulting topology is asymptotically almost surely connected and benign agents have majority benign neighborhoods. At the same time, the proposed design asymptotically tolerates a fraction of malicious agents arbitrarily close to one, while risk agnostic designs, such as complete graphs, break down as soon as the majority of agents is malicious.