Model: vaios-stergio/all-mpnet-base-v2-dblp-aminer-50k-triplets-cachedgistembedloss
Author: vaios-stergio
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, mpnet, sentence-similarity, feature-extraction, dense, generated_from_trainer, dataset_size:38864, loss:CachedGISTEmbedLoss, arxiv:1908.10084, base_model:sentence-transformers/all-mpnet-base-v2, base_model:finetune:sentence-transformers/all-mpnet-base-v2, endpoints_compatible, region:us
Base models (finetune): id: sentence-transformers/all-mpnet-base-v2
Card metadata: tags: sentence-transformers, sentence-similarity, feature-extraction, dense, generated_from_trainer, dataset_size:38864, loss:CachedGISTEmbedLoss
base_model: sentence-transformers/all-mpnet-base-v2
widget: source_sentence: Modelbased Dynamic Pose Graph SLAM in Unstructured Dynamic Environments Navigation in dynamic environments is a challenge for autonomous vehicles operating without prior maps or global position references. This poses high risk to vehicles that perform scientific studies and monitoring missions in marine Arctic environments characterized by slowly moving sea ice with few truly static landmarks. Whereas mature simultaneous localization and mapping (SLAM) approaches assume a static environment, this work extends pose graph SLAM to spatiotemporally evolving environments. A novel modelbased dynamic factor is proposed to capture a landmarku0027s state transition model  whether the state be kinematic, appearance or otherwise. The structure of the state transition model is assumed to be known a priori, while the parameters are estimated online. Expectation maximization is used to avoid adding variables to the graph. Proofofconcept results are shown in small and mediumscale simulation, and smallscale laboratory environments for a small quadrotor. Preliminary laboratory validation results shows the effect of mechanical limitations of the quadrotor platform and increased uncertainties associated with the modelbased dynamic factors on the SLAM estimate. Simulation results are encouraging for the application of modelbased dynamic factors to dynamic landmarks with a constantvelocity kinematic model.
sentences: Effective Distance based Low Rank Representation for Image Classification Lowrank representation (LRR) has recently attracted a great deal of attention due to its pleasing efficacy in exploring lowdimensional subspace structures embedded in data. However, conventional LRR methods simply use Euclidean distance to measure the similarity of samples, ignoring the dynamic structure information of data. Meanwhile, recent studies have shown that a probabilistically motivated distance measurement (called effective distance) can model the dynamic structure information of data. In this paper, we propose an effective distance based LRR (EDLRR)method for representation learning. The proposed EDLRR method can not only represent the dynamic structure of data, but also capture the geometric information in the inherent nonlinear data. Our method mainly uses Effective Distance Computation and Effective Distance based LowRank Representation. We evaluate our method on datasets in the task of image classification, with results demonstrating the effectiveness of the method., Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups., LongTerm Visual Inertial SLAM based on Time Series Map Prediction With the advance in the field of mobile robots, autonomous robots are required for longterm deployment in dynamic and complex environments. However, the performance of Visual Inertial SLAM systems in longterm operation is not satisfactory, and most longterm SLAM systems assumes periodic changes in the environment. This paper presents a novel solution for longterm monocular VI SLAM system in dynamic environment based on autoregression(AR) modeling and map prediction. Map points are first classified into static and semistatic map points according to a memory model. Modeling and prediction of the different states of semistatic map points are performed that are derived from time series models. The predicted map is then fused with the current map to achieve a better forecast for the next frame if the prediction is not satisfactory enough. Experiments are carried out on an embedded system. The results indicate that the map prediction is reliable and the proposed approach improves the performance of longterm localization and mapping in dynamic environments., source_sentence: Spectral Method and Spectral Element Method for Three Dimensional Linear Elliptic System: Analysis and Application In this paper, a leastsquares spectral method and a nonconforming leastsquares spectral element method for three dimensional linear elliptic system will be presented. Differentiability estimates and the main stability theorem for the proposed method are proven. Using the regularity estimate and the proposed stability estimates, we introduce a suitable preconditioner and show that the condition number of the preconditioned system is (O((ln W)2)) (where W is degree of polynomials). Then we establish the error estimates of the proposed method. Specific numerical results based on linear elasticity problem, fourth order problem and sixth order problem are discussed to reflect the efficiency of the proposed method.
sentences: A Critical Look at the 2019 College Admissions Scandal Discusses the 2019 College admissions scandal. Let me begin with a disclaimer: I am making no legal excuses for the participants in the current scandal. I am only offering contextual background that places it in the broader academic, cultural, and political perspective required for understanding. It is only the most recent installment of a wellworn narrative: the controlling elite make their own rules and live by them, if they can get away with it. Unfortunately, some of the participants, who are either serving or facing jail time, didnxe2x80x99t know to not go into a gunfight with a sharp stick. Money alone is not enough to avoid prosecution for fraud: you need political clout. The best protection a defendant can have is a prosecutor who fears political reprisal. Compare how the Koch brothers escaped prosecution for stealing millions of oil dollars from Native American tribes1,2 with the fate of actresses Lori Loughlin and Felicity Huffman, who, at the time of this writing, face jail time for paying bribes to get their children into good universities.3,4 In the former case, the federal prosecutor who dared to empanel a grand jury to get at the truth was fired for cause, which put a quick end to the prosecution. In the latter case, the prosecutors pushed for jail terms and public admonishment with the zeal of Oliver Cromwell. There you have it: stealing oil from Native Americans versus trying to bribe your kids into a great university. Where is the greater crime? Admittedly, these actresses and their, Highorder Gegenbauer integral spectral element method integrated with an adaptive Chebyshev optimization strategy for solving linear singularly perturbed differential equations Abstract   We propose a highorder adaptive spectral element method based on Gegenbauer polynomial basis for solving singularly perturbed differential equations with Dirichlet boundary conditions and a boundary layer at one end. The method reduces the singularly perturbed boundary value problem into a single system of linear algebraic equations when the solution is smooth on the solution domain, or two systems of linear algebraic equations when the solution has a thin boundary layer. The core of the new adaptive strategy lies in constructing a mathematical model of the last few Gegenbauer coefficients in the Gegenbauer truncated series approximating the secondorder derivative of the solution, which can be readily converted into a minimization problem in a leastsquares sense and solved using fast optimization techniques. An error and convergence analysis of the method for sufficiently smooth solutions is presented. A numerical test example with various parameter settings is presented to verify the accuracy, effectiveness, and applicability of the proposed method. Numerical comparisons with other rival methods in the literature are included to demonstrate further the power of the proposed method., Predicting LncRNAdisease Association by Autoencoder and Rotation Forest In the past few years, most diseaserelated lncRNAs have been identified, but the experimental identification is costconsuming and timeconsuming. It is therefore very important to develop a reliable computational model to predict lncRNAdisease association. In this paper, we propose a method based on similarity, combining autoencoder and rotation forest to predict lncRNAdisease association (SARLDA). This method not only makes use of disease and lncRNA similarities, but also extracts latent lowdimension features and expand the gap between samples to make it easier to predict the associations. To evaluate our method, we conducted several experiments. Sufficient validations show that this method has significantly improved the prediction performance., source_sentence: Fine Time Resolution TDC Architectures Integral and DeltaSigma Types This paper describes two timetodigital converter (TDC) architectures for fine time resolution and good linearity: integraltype and deltasigma TDC architectures. By investigating them, the similarity and difference between TDC and ADC architectures for fine resolution but slow measurement are clarified. Our integraltype TDC architectures are inspired by the integraltype ADC, and also the timebases of the equivalent time sampling in the sampling oscilloscope: sequential sampling and random sampling. One TDC uses a sequential sampling timebase, and the other uses a random sampling timebase. Each of them consists of two trigger circuits, a sampling flipflop, a binary counter and additional small logic circuit. Their circuit topologies, operation principles and simulation results as well as their comparison are presented. Also our deltasigma TDC architecture is mentioned, and comparison between the integral and deltasigma TDC architectures is discussed.
sentences: Simultaneous Multiple Features Tracking of Beats: A Representation Learning Approach to Reduce False Alarm Rate in ICUs The high rate of false alarms is a key challenge related to patient care in intensive care units (ICUs) that can result in delayed responses of the medical staff. Several rulebased and machine learningbased techniques have been developed to address this problem. However, the majority of these methods rely on the availability of different physiological signals such as different electrocardiogram (ECG) leads, arterial blood pressure (ABP), and photoplethysmogram (PPG), where each signal is analyzed by an independent processing unit and the results are fed to an algorithm to determine an alarm. That calls for novel methods that can accurately detect the cardiac events by only accessing one signal (e.g., ECG) with a low level of computation and sensors requirement. We propose a novel and robust representation learning framework for ECG analysis that only rely on a single lead ECG signal and yet achieves considerably better performance compared to the stateoftheart works in this domain, without relying on an expert knowledge. We evaluate the performance of this method using the xe2x80x9c2015 Physionet computing in cardiology challengexe2x80x9d dataset. To the best of our knowledge, the best previously reported performance is based on both expert knowledge and machine learning where all available signals of ECG, ABP and PPG are utilized. Our proposed method reaches the performance of 97.3%, 95.5%, and 90.8% in terms of sensitivity, specificity, and the challengeu0027s score, respectively for the detection of five arrhythmias when only one single ECG lead signals is used without any expert knowledge11This material is based upon work supported by the National Science Foundation under Grant Number 1657260. Research reported in this publication was also supported by the National Institute On Minority Health and Health Disparities of the National Institutes of Health under Award Number U54MD012388. 22The copyright notice: 97817281186731931.00 xc2xa92019 IEEE., A MultiPhase TimetoDigital Converter Differential Vernier Ring Oscillator This paper reports the development of an adjustable, TimetoDigital Converter (TDC) based on two vernier Ring Oscillators (RO). The TDC aims to measure timing in Resistive Plate Chamber (RPC) detector for CMS experiment. Considering previous designs, the contribution from power supply noise and intrinsic transistor noise had been minimizing with differential stages and proper transistor sizing. To reduce the timing resolution and deadtime inherent to Vernier TDC architecture, as many Phase Detector (PD) as possible had been implemented. Such functionality permits to choose whether reducing the dead time or measuring redundantly the startstop time difference for an improved precision. The prototype TDC fabricated in a 130nm technology consumes 8.5 mW power under 1.2V supply. The measurement of this chip shown a timing accuracy of 5.48 ps at a timing resolution of 8 ps for the first data allowed by the first phase detection., Managing Information From the  :2,Information highlights the increasing value of information and IT within organizations and shows how organizations use it. It also deals with the crucial relationship between information and personal effectiveness. The use of computer software and communications in a management context are discussed in detail, including how to mould an information system to your needs. The book explains the basics using reallife examples and brings managers uptodate with the latest developments in electronic commerce and the Internet. The book is based on the Management Charter Initiativeu0027s Occupational Standards for Management NVQs and SVQs at level 4. It is particularly suitable for managers on the Certificate in Management, or Part I of the Diploma, especially those accredited by the IM and BTEC., source_sentence: Lung Disease Classification using Deep Convolutional Neural Network The advanced technologies are essential to achieving the improvement of medicine. More specifically, an extensive investigation in a partnership among researchers, health care providers, and patients is integral to bringing precise and customized treatment strategies in taking care of various diseases. This paper aims to assess the degree of accuracy acceptable in the medical field by utilizing deep learning to publicly available data. First, we extracted spectrogram features and labels of the annotated lung sound samples and used them as an input to our 2D Convolutional Neural Network (CNN) model. Secondly, we normalized the lung sounds to remove the peak values and noise from them. For deep learning classification, publicly available data was not sufficient to conduct the learning process. Finally, we have created a deep learning model called Lung Disease Classification (LDC), combined with advanced data normalization and data augmentation techniques, for highperformance classification in lung disease diagnosis. The final accuracy obtained after the normalization and augmentation was approximately 97%. The proposed model paves the way for adequate assessment of the degree of accuracy acceptable in the medical field and guarantees better performance than other previously reported approaches.
sentences: Extremal Problems for tPartite and tColorable Hypergraphs Fix integers t ge r ge 2 and an runiform hypergraph F. We prove that the maximum number of edges in a tpartite runiform hypergraph on n vertices that contains no copy of F is c_t, Fn choose r o(nr), where c_t, F can be determined by a finite computation.   We explicitly define a sequence F_1, F_2, ldots of runiform hypergraphs, and prove that the maximum number of edges in a tchromatic runiform hypergraph on n vertices containing no copy of F_i is alpha_t,r,in choose r o(nr), where alpha_t,r,i can be determined by a finite computation for each ige 1. In several cases, alpha_t,r,i is irrational. The main tool used in the proofs is the Lagrangian of a hypergraph., GNDD: A Graph Neural NetworkBased Method for DrugDisease Association Prediction Potential drugdisease association prediction is important to facilitate drug discovery. However, most of existing drugdisease association prediction approaches rely on assembling multiple drug (disease)related biological information, which is usually not comprehensively available, and they always fail to explore the latent information in drugdisease network. To tackle these challenges, we propose a graph neural networkbased method for drugdisease association prediction, dubbed GNDD, with capturing the complex information between drugs and diseases dispense with any side information. Specifically, GNDD introduces the idea of collaborative filtering in recommendation system to avoid the dependency on multidata. Furthermore, an embedding propagation strategy is exploited to model the highorder relationships in drugdisease network. We conduct experiments on the Comparative Toxicogenomics Database, demonstrating the effectiveness of our method in drugdisease association prediction., Advanced and Minor Lung Disease Severity Classification Using Deep Features A key component of Computer Aided Diagnosis (CADx) systems is classification. This stage involves feature extraction. Deep features have shown to be an emerging area of research in various fields including medical imaging. Most recent deep works in the lung area focused using Convolution Neural Networks (CNN). However, these works have drawbacks such as overclassifying and are not reflective of the real world. Therefore this studyu0027s aim is to develop a lung disease severity classification (LDSC) system. Information on lung disease severity can be beneficial to clinicians to plan treatments. This study used high resolution computed tomography (HRCT) scans from Hospital Kuala Lumpur where there were 81 diseased (Interstitial Lung Disease) and 15 normal patients. Deep features were obtained using a pretrained network (Alexnet). This studied used four classifiers Support Vector Machine (SVM), Naive Bayes, Random Forest and KNN. A high accuracy of 100% was achieved using SVM., source_sentence: Metabolite Identification Using Artificial Neural Network Metabolite identification is a major bottleneck in mass spectrometrybased metabolomic studies. In a typical untargeted metabolomic study that uses tandem MS (MSMS), the identities of metabolites are determined by matching the experimental MSMS data against those in spectral libraries. However, existing spectral libraries cover only a small fraction of known compounds. We introduce a neural network model to predict the identity of analytes whose reference measurements are not available in spectral libraries. Specifically, an artificial neural network model is trained to predict the fingerprints of compounds based on their experimental MSMS spectra. Candidates are retrieved from metabolite databases based on molecular formula or precursor mass. The candidate with the most similar fingerprint is chosen as the predicted one. The method was evaluated via an independent dataset. The results show that our neural network model improves MSMSbased metabolite identification accuracy by a considerable margin compared to previous computational methods, such as Metfrag and CSI:FingerID.
sentences: Survivability in optical networks: a solution for the wavelength continuity constraint case Survivability of internet services is a significant and crucial challenge in designing future optical networks. A robust infrastructure and transmission protocols are needed to handle such a situation so that the network users can maintain communication despite the existence of one or more failed components on the network. For this reason, in this work, we present a generalized approach to tolerate any set of failure scenarios, to the extent the user can still communicate with the remaining components, where a scenario corresponds to an arbitrary set of links in a nonoperational state. To assess the survivability problem, we propose a joint solution to the problems listed next. We show how to find: a set of primary routes, a set of alternate routes associated to each failure scenario, and the capacity required on the network to allow communication between all users, in spite of any failure scenario, while satisfying for each user a specific predefined quality of service threshold, defined in the Service Level Agreement (SLA). Numerical results show that the proposed approach not only enjoys the advantages of low complexity and ease of implementation but is also able to achieve significant resource savings compared to existing methods. The savings are higher than 30% on single link failures and more than a 100% on two simultaneous link failures scenarios and in more complex failure scenarios., Rickshaw Buddy RICKSHAW BUDDY is a lowcost automated assistance system for threewheeler auto rickshaws to reduce the high rate of accidents in the streets of developing countries like Bangladesh. It is a given fact that the lack of over speed alert, back camera, detection of rear obstacle and delay of maintenance are causes behind fatal accidents. These systems are absent not only in auto rickshaws but also most public transports. For this system, surveys have been done in different phases among the passengers, drivers and even the conductors for a useful and successful result. Since the system is very cheap, the lowincome drivers and owners of vehicles will be able to afford it easily making road safety the first and foremost priority., Efficient Shared Peak Counting in Database Peptide Search Using Compact Data Structure for FragmentIon Index Database search is the most commonly employed method for identification of peptides from MSMS spectra data. The search involves comparing experimentally obtained MSMS spectra against a set of theoretical spectra predicted from a protein sequence database. One of the most commonly employed similarity metrics for spectral comparison is the sharedpeak count between a pair of MSMS spectra. Most modern methods index all generated fragmention data from theoretical spectra to speed up the shared peak count computations between a given experimental spectrum and all theoretical spectra. However, the bottleneck for this method is the gigantic memory footprint of fragmention index that leads to nonscalable solutions. In this paper, we present a novel data structure, called Compact FragmentIon Index Representation (CFIR), that efficiently compresses highly redundant ionmass information in the data to reduce the index size. Our proposed data structure outperforms all existing fragmention indexing data structures by at least 2xc3x97 in memory consumption while exhibiting the same time complexity for index construction and peptide search. The results also show comparable indexing speed, search speed and speedup scalability for CFIRindex and the stateoftheart algorithms.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
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Created: 2025-11-23 17:46:33