Model: vaios-stergio/all-mpnet-base-v2-dblp-aminer-10k-pair-label-cosentloss
Author: vaios-stergio
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, mpnet, sentence-similarity, feature-extraction, dense, generated_from_trainer, dataset_size:9600, loss:CoSENTLoss, 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:9600, loss:CoSENTLoss
base_model: sentence-transformers/all-mpnet-base-v2
widget: source_sentence: Ex Vivo Assessment of Multiple Parameters in High Intensity Focused Ultrasound.High Intensity Focused Ultrasound HIFU is a very promising technology for a noninvasive treatment of several pathologies especially in oncology. However optimizing the stimulation parameters for better tuning the induced lethal effects thermal andor mechanical in the targeted area is not trivial and it has not been achieved yet. The aim of this study is to present the results of a combined analysis of temperature acoustic cavitation and lesion geometry induced in ex vivo tissues during HIFU procedures by varying power sonication time and duty cycle. Temperature rise was analyzed using a thin wire thermocouple embedded in the sonicated tissue stable and inertial cavitation were measured using a passive cavitation detector PCD and lesion volume was assessed using both ultrasound imaging and optical visualization. The obtained results may represent an important guideline for clinical treatments providing useful nformation for better tuning HIFU operational parameters to induce a desired type of ablation i.e. thermal mechanical or a combination of both.
sentences: The Deeper the Better Analysis of Person Attributes Recognition.In person attributes recognition we describe a person in terms of their appearance. Typically this includes a wide range of traits including age gender clothing and footwear. Although this could be used in a wide variety of scenarios it generally is applied to video surveillance where attribute recognition is impacted by low resolution and other issues such as variable pose occlusion and shadow. Recent approaches have used deep convolutional neural networks CNNs to improve the accuracy in person attribute recognition. However many of these networks are relatively shallow and it is unclear to what extent they use contextual cues to improve classification accuracy. In this paper we propose deeper methods for person attribute recognition. Interpreting the reasons behind the classification is highly important as it can provide insight into how the classifier is making decisions. Interpretation suggests that deeper networks generally take more contextual information into consideration which helps improve classification accuracy and generalizability. We present experimental analysis and results for whole body attributes using the PA100K and PETA datasets and facial attributes using the CelebA dataset., A Practical Deep Online Ranking System in Ecommerce Recommendation.User online shopping experience in modern ecommerce websites critically relies on realtime personalized recommendations. However building a productionized recommender system still remains challenging due to a massive collection of items a huge number of online users and requirements for recommendations to be responsive to user actions. In this work we present our relevant responsive and scalable deep online ranking system DORS that we developed and deployed in our company. DORS is implemented in a threelevel architecture which includes 1 candidate retrieval that retrieves a board set of candidates with various business rules enforced 2 deep neural network ranking model that takes advantage of available user and item specific features and their interactions 3 multiarm bandits based online reranking that dynamically takes user realtime feedback and reranks the final recommended items in scale. Given a user as a query DORS is able to precisely capture users’ realtime purchasing intents and help users reach to product purchases. Both offline and online experimental results show that DORS provides more personalized online ranking results and makes more revenue., Carbon NanotubeGraphene Nanoplatelet Hybrid Film as a Flexible Multifunctional Sensor.A flexible hybrid film consisting of graphene nanoplatelets GNPs and multiwalled carbon nanotubes MWCNTs was prepared and employed as a multifunctional sensor to monitor temperature and liquid leakage based on the piezoresistive effect. The influences of the GNP content on the mechanical thermal and sensing properties were investigated. Experimental results showed that both the hardness and Young’s modulus of the hybrid film were decreased with an increasing GNP content while the thermal conductivity exhibited in an opposite trend. The electrical resistance of the hybrid film decreased was linearly with an increase in temperature. The resistance change increased linearly with an increase of the solvent adsorption. These features demonstrated the potential applications of the hybrid film in the detection of temperature and liquid leakage. The sensitivity of leakage detection is increasing with the increase of the GNP loading while temperature sensitivity is in the opposite trend., source_sentence: Toward Lung Tumor Localization based on Strain Variability of Lung Surface during VideoAssisted Thoracoscopic Surgery.Tumor localization especially in case of minimally invasive lung tumor resection surgery is extremely challenging due to the continuous motion of the organ. This motion can be troublesome as it results in spatial discrepancy corresponding to preoperative and intraoperative tumor location. In order to characterize lung tissue stiffness for the purpose of lung tumor localization in this paper we present a novel characterization approach based on variability in resistance of the healthy region vs. the tumorous region resulting from lung motion. The proposed approach is numerically validated on a Finite Element FE model of the lung with varying surface stiffnesses where higher stiffness represents tumor and lower stiffness corresponds to healthy lung tissue. The numerical simulation validates the sensitivity of our mechanism for different grades of tumors by demonstrating that the strain on the healthy tissue is 31.8 and 67.1 times higher than that on the tumor surface for a selected relative stiffness variation of 3.6x and 24.4x respectively at a pressure of 1.6 KPa. Additionally a framework is developed to validate the proposed approach in a video of a videoassisted thoracoscopic surgery VATS where multiple landmarks on the lung surface are tracked. This enables us to quantify the motion of points residing on healthy surface and tumorous surface. The motion data is further analyzed to study the relative surface strain and it is shown that the proposed approach differentiates a tumor from healthy surface.
sentences: Convolutional neural networks CNNs have attracted researchers’ increasing attention for almost three decades now achieving superior results in such domains as computer vision signal processing etc Their success can be mainly attributed to a specific network architecture which is conceived by assigning values to a large number of hyperparameters each influencing the resulting error rate Yet a search for good hyperparameter values is a challenging task being usually done manually and taking a considerable amount of work This paper is dedicated to the problem of designing automated hyperparameter search algorithms for convolutional architectures We propose two algorithms based on such metaheuristics as evolutionary computation and local search To our knowledge they have never been applied to the case of CNN architectures before Using image recognition datasets we compare the algorithms and show that they can produce CNNs with nearly state of the art performance without any user interference saving much tedious effort, Layered adaptive compression design for efficient data collection in industrial wireless sensor networks.Abstract Existing compressed sensing CSbased spatiotemporal data compression schemes can significantly decrease communication consumption for data collection however they ignore data correlation among different clusters over spatial dimensions. To explore data correlation among different clusters and satisfy the requirement of high data precision in industrial applications in this paper we propose a layered adaptive compression design for efficient data collection LACDEDC in industrial wireless sensor networks IWSNs. In the proposed scheme first we design a multilayer network architecture to support the exploration of spatiotemporal correlations especially spatial correlation among different clusters. Then we construct specific projection methods for exploring temporal correlation in sensory nodes spatial correlation intracluster in cluster heads and spatial correlation intercluster in processing nodes. In addition a detailed solution method is developed to recover the original data and achieve approximate data collection in the sink node. Subsequently sparsifying dictionaries are trained for adapting different types of data and obtaining better sparse representations which further improves the data recovery accuracy. Our simulation results indicate that the proposed layered adaptive compression scheme offers better recovery performance than conventional clustered compression schemes i.e. achieving efficient data collection with high quality., Performance Comparison of Chunk and Peer Scheduling Algorithms of PeertoPeer Streaming Systems.With increasing popularity of Peer to Peer systems for video streaming it is important that the expectations of the users regarding the quality of such systems are being met. In a P2P system the media stream is divided into small data units known as chunks. Each peer in a Peer to Peer P2P system has to take two important decisions at a given time. First which chunks are to be shared and second with which peer. This paper compares the performance of different combinations of chunkpeer schedulers in terms of chunk diffusion delay average chunk distribution delay and max chunk distribution delay. By doing so the best possible combination of the two schedulers for the given experimental setup is explored. The results obtained under the specified experimental setup show that when chunk scheduling algorithm Deadline Based Chunk Scheduler DLc is combined with different peer scheduling algorithms the best results are obtained by its combination with Chunk Earliest Free Pair Scheduler CEFp. For a constant peer scheduler CEFp combined with different chunk schedulers the best results are obtained by combining it with Latest Blind Chunk Scheduler LBc. Finally with varying neighborhood size the best results are obtained by the combination of DLC and Chunk Almost Free Peer Scheduler CAFp, source_sentence: Which method to use An assessment of data mining methods in Environmental Data Science.Abstract Data Mining DM is a fundamental component of the Data Science process. Over recent years a huge library of DM algorithms has been developed to tackle a variety of problems in fields such as medical imaging and traffic analysis. Many DM techniques are far more flexible than more classical numerial simulation or statistical modelling approaches. These could be usefully applied to datarich environmental problems. Certain techniques such as artificial neural networks clustering casebased reasoning or Bayesian networks have been applied in environmental modelling while other methods like support vector machines among others have yet to be taken up on a wide scale. There is greater scope for many lesser known techniques to be applied in environmental research with the potential to contribute to addressing some of the current open environmental challenges. However selecting the best DM technique for a given environmental problem is not a simple decision and there is a lack of guidelines and criteria that helps the data scientist and environmental scientists to ensure effective knowledge extraction from data. This paper provides a broad introduction to the use of DM in Data Science processes for environmental researchers. Data Science contains three main steps preprocessing data mining and postprocessing. This paper provides a conceptualization of Environmental Systems and a conceptualization of DM methods which are in the core step of the Data Science process. These two elements define a conceptual framework that is on the basis of a new methodology proposed for relating the characteristics of a given environmental problem with a family of Data Mining methods. The paper provides a general overview and guidelines of DM techniques to a nonexpert user who can decide with this support which is the more suitable technique to solve their problem at hand. The decision is related to the bidimensional relationship between the type of environmental system and the type of DM method. An illustrative two way table containing references for each pair Environmental SystemData Mining method is presented and discussed. Some examples of how the proposed methodology is used to support DM method selection are also presented and challenges and future trends are identified.
sentences: An iterative propagation based cosaliency framework for RGBD images.Abstract As an important topic which is emerging in the field of computer vision cosaliency detection is aimed at finding the salient target among several related images. The existent methods are usually to formulate cosaliency map by designed clues or initialized and direct forward pipeline. However these models are in lack of an improved scheme for loop scheme. At the meantime lots of methods only focus on RGB images while ignore depth cues of RGBD images. In this paper an iterative RGBD cosaliency method will be introduced. It utilizes the existing single saliency maps as initialization and another kind of refinementcycle model to generate a final RGBD cosaliency map. The proposed model adopted three schemes including addition scheme deletion scheme and iterative scheme. Besides this paper also proposed a new descriptor in addition scheme Depth Shape Prior DSP. Putting introduced depth cues into DSP can enhance recognition ability for cosaliency target which will eventually achieve the transformation from twodimension saliency detection to cosaliency detection based on RGBD images. Experiments show the effectiveness of described in this paper., Multitask learning model based on recurrent convolutional neural networks for citation sentiment and purpose classification.Abstract Automated citation analysis is a method of identifying sentiment and purpose of citations in the citing works. Most of the existing approaches use machine learning techniques to boost the performance of citation sentiment classification CSC and citation purpose classification CPC which are the main tasks of automated citation analysis. However such approaches address CPC and CSC by learning them separately which often suffer from inadequate training data and timeconsuming for feature engineering. To alleviate these problems we propose a multitask learning model based on convolutional and recurrent neural networks. The proposed model benefits from jointly learning CSC and CPC by modeling the citation context with taskspecific information and shared layers for citation sentiment and purpose classification. The network architecture of the proposed model is useful to represent the citation context and extracts the features automatically. We conduct experiments on two public datasets to evaluate the performance of the proposed model using standard metrics such as precision recall and Fscore. The results of CSC and CPC tasks show improvements relative to classical machine learning algorithms such as SVM and NB as well as singletask deep learning models., Handshake Sense Multiple Access Control for Cognitive RadioBased IoT Networks.InternetofThings IoT enabling technologies such as ZigBee WiFi 6LowPAN RFID MachinetoMachine LTEAdvanced etc. depend on the licensefree Industrial Scientific and Medical ISM bands for the Internet. The proliferation of IoT devices is not only anticipated to create a huge amount of congestion in the near future but even now the unlicensed spectrum is not enough in the ISM bands. Towards this end Cognitive Radio CR technology can resolve the spectrum shortage issue since CR users can opportunistically exploit white spaces in licensed channels of the adjacent wireless systems. In CR networks it is critical to coordinate spectrum access among Secondary Users SUs while protecting priority rights of Primary Users PUs. Therein SUs need to take good care of hidden PUs in order to avoid harmful interference. Further a densely deployed CR network can compromise spectrum sensing quality and certainty of the results when a large number of SUs contends to access the same channel. Therefore based on the vulnerable sensing results SUs can cause interference to the PUs. In this paper we first investigate the leading issues and then propose a novel Handshake Sense Multiple Access with Collision Avoidance HSMACA protocol for CRbased IoT networks. Our proposed HSMACA scheme resolves hidden primary terminal problem and maintains sufficient priority rights to PUs in a densely distributed network. In addition we optimize the spectrum sensing period to maximize the system performance by maintaining peculiarities in the sensing operation like false alarm and misdetection. To evaluate the performance of HSMACA we have analyzed the protocol through the Markov chain model in terms of throughput and verify its accuracy by simulations. Simulation results show that our scheme is suitable for noncollaborative densely deployed CRbased IoT networks., source_sentence: Metric on Nonlinear Dynamical Systems with PerronFrobenius Operators.The development of a metric for structural data is a longterm problem in pattern recognition and machine learning. In this paper we develop a general metric for comparing nonlinear dynamical systems that is defined with PerronFrobenius operators in reproducing kernel Hilbert spaces. Our metric includes the existing fundamental metrics for dynamical systems which are basically defined with principal angles between some appropriatelychosen subspaces as its special cases. We also describe the estimation of our metric from finite data. We empirically illustrate our metric with an example of rotation dynamics in a unit disk in a complex plane and evaluate the performance with realworld timeseries data.
sentences: Abstract Mobile personal cloud storage services PCSSs have become increasingly common among mobile users in recent years Prior studies largely focused on understanding usersu0027 early adoption of mobile PCSSs but attention to postadoption switching behavior is still rare Based on the pushpullmooring framework of migration theory to explore the factors that influence the voluntary switching intentions of mobile PCSS users in the Chinese market we developed a model around three effects pushing pulling and mooring The results show that two push factors privacy risk and security risk six pull factors referent network size complementarity usefulness technical compatibility lifestyle compatibility and enjoyment and two mooring factors habit and switching cost have significant effects on switching intention Moreover referent network size total network size complementarity technical compatibility lifestyle compatibility and enjoyment are significant to usefulness and usefulness mediates between them and switching intention The findings will help practitioners devise proper strategies for retaining current users and attracting new users, Capacitively Coupled Electrical Impedance Tomography for Brain Imaging.Electrical impedance tomography EIT is considered as a potential candidate for brain stroke imaging due to its compactness and potential use in bedside and emergency settings. The electrode–skin contact impedance and low conductivity of skull pose some practical challenges to the EIT head imaging. This paper studies the application of capacitively coupled electrical impedance tomography CCEIT in brain imaging for the first time. CCEIT is a new contactless EIT technique which uses voltage excitation without direct contact with the skin as oppose to directly injecting the current to the skin in EIT. Because the safety issue of a new technique should be strictly treated simulation work based on a simplified head model was carried out to investigate the safety aspects of CCEIT. By comparing with the standard EIT excited by a typical safe current level used in brain imaging the safe excitation reference of CCEIT is obtained. This is done by comparing the maximum level of internal electrical field internal current density of EIT and that of CCEIT. Simulation results provide useful knowledge of excitation signal level of CCEIT and also show a critical comparison with traditional EIT. Practical experiments were carried out with a 12electrode CCEIT phantom saline and carrot samples. Experimental results show the feasibility and potential of CCEIT for stroke imaging. In this paper the anomaly diameter resolution is 10 mm 118 of the phantom diameter which indicates that smallvolume stroke could be detected. This is achieved by a low excitation voltage of 1 V showing the possibility of even better performance when higher but yet safe level of excitation voltages is used., DeepMoCap Deep Optical Motion Capture Using Multiple Depth Sensors and RetroReflectors.In this paper a markerbased singleperson optical motion capture method DeepMoCap is proposed using multiple spatiotemporally aligned infrareddepth sensors and retroreflective straps and patches reflectors. DeepMoCap explores motion capture by automatically localizing and labeling reflectors on depth images and subsequently on 3D space. Introducing a nonparametric representation to encode the temporal correlation among pairs of colorized depthmaps and 3D optical flow frames a multistage Fully Convolutional Network FCN architecture is proposed to jointly learn reflector locations and their temporal dependency among sequential frames. The extracted reflector 2D locations are spatially mapped in 3D space resulting in robust 3D optical data extraction. The subject’s motion is efficiently captured by applying a templatebased fitting technique on the extracted optical data. Two datasets have been created and made publicly available for evaluation purposes one comprising multiview depth and 3D optical flow annotated images DMC2.5D and a second consisting of spatiotemporally aligned multiview depth images along with skeleton inertial and ground truth MoCap data DMC3D. The FCN model outperforms its competitors on the DMC2.5D dataset using 2D Percentage of Correct Keypoints PCK metric while the motion capture outcome is evaluated against RGBD and inertial data fusion approaches on DMC3D outperforming the next best method by 4.5 in total 3D PCK accuracy., source_sentence: A Multiscale RayShooting Model for Termination Detection of TreeLike Structures in Biomedical Images
sentences: Flow Prediction in SpatioTemporal Networks Based on Multitask Deep Learning.Predicting flows e.g. the traffic of vehicles crowds and bikes consisting of the inout traffic at a node and transitions between different nodes in a spatiotemporal network plays an important role in transportation systems. However this is a very challenging problem affected by multiple complex factors such as spatial correlations between different locations temporal correlations among different time intervals and external factors like events and weather. In addition the flow at a node called node flow and transitions between nodes edge flow mutually influence each other. To address these issues we propose a multitask deeplearning framework that simultaneously predicts the node flow and edge flow throughout a spatiotemporal network. Using fully convolutional networks our approach designs two sophisticated models for predicting node flow and edge flow respectively. Two models are connected by coupling their latent representations of middle layers and trained together. The external factors are also integrated into the framework through a gating fusion mechanism. In the edge flow prediction model we employ an embedding component to deal with the sparse transitions between nodes. We evaluate our method based on the taxicab data in Beijing and New York City.Experimental results show advantages of our method beyond 11 baselines such as ConvLSTM CNN and Markov Random Field., Using Containers to Execute SQL Queries in a Cloud.Emergent software container technology is available on any cloud and opens up new opportunities to execute and scale data computationally intensive applications wherever data is located. However many traditional databases hosted on clouds have not scaled well. In this paper a framework and methodology to containerize a SQL query is presented so that a single SQL query can be scaled and executed by a network of cooperating containers achieving operator parallelism and other significant performance gains. Experiments of a framework prototype are reported and compared to a realworld database control baseline. Preliminary results on a research cloud shows up to 3orders of magnitude performance gain compared to running the same query on a single VM hosted baseline control database., Digital reconstruction tracing of treelike structures such as neurons retinal blood vessels and bronchi from volumetric images and 2D images is very important to biomedical research Many existing reconstruction algorithms rely on a set of good seed points The 2D or 3D terminations are good candidates for such seed points In this paper we propose an automatic method to detect terminations for treelike structures based on a multiscale rayshooting model and a termination visual prior The multiscale rayshooting model detects 2D terminations by extracting and analyzing the multiscale intensity distribution features around a termination candidate The range of scale is adaptively determined according to the local neurite diameter estimated by the Rayburst sampling algorithm in combination with the grayweighted distance transform The termination visual prior is based on a key observation–when observing a 3D termination from three orthogonal directions without occlusion we can recognize it in at least two views Using this prior with the multiscale rayshooting model we can detect 3D terminations with high accuracies Experiments on 3D neuron image stacks 2D neuron images 3D bronchus image stacks and 2D retinal blood vessel images exhibit average precision and recall rates of 8750 and 9054 The experimental results confirm that the proposed method outperforms other the stateoftheart termination detection methods
pipeline_tag: sentence-similarity
library_name: sentence-transformers
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Created: 2025-12-24 17:20:22