Miracle-dz/UniFAR-FTUs huggingface dataset:
Heterogeneous Metric Learning with Joint Graph Regularization for Cross-Media Retrieval

As the major component of big data, unstructured heterogeneous multimedia content such as text, image, audio, video and 3D increasing rapidly on the Internet. User demand a new type of cross-media retrieval where user can search results across various media by submitting query of any media. Since the query and the retrieved results can be of different media, how to learn a heterogeneous metric is the key challenge. Most existing metric learning algorithms only focus on a single media where all of the media objects share the same data representation. In this paper, we propose a joint graph regularized heterogeneous metric learning (JGRHML) algorithm, which integrates the structure of different media into a joint graph regularization. In JGRHML, different media are complementary to each other and optimizing them simultaneously can make the solution smoother for both media and further improve the accuracy of the final metric. Based on the heterogeneous metric, we further learn a high-level semantic metric through label propagation. JGRHML is effective to explore the semantic relationship hidden across different modalities. The experimental results on two datasets with up to five media types show the effectiveness of our proposed approach.
Characterizing Inverse Time Dependency in Multi-class Learning

The training time of most learning algorithms increases as the size of training data increases. Yet, recent advances in linear binary SVM and LR challenge this commonsense by proposing an inverse dependency property, where the training time decreases as the size of training data increases. In this paper, we study the inverse dependency property of multi-class classification problem. We describe a general framework for multi-class classification problem with a single objective to achieve inverse dependency and extend it to three popular multi-class algorithms. We present theoretical results demonstrating its convergence and inverse dependency guarantee. We conduct experiments to empirically verify the inverse dependency of all the three algorithms on large-scale datasets as well as to ensure the accuracy.
Cell-Aware Test

This paper describes the new cell-aware test (CAT) approach, which enables a transistor-level and defect-based ATPG on full CMOS-based designs to significantly reduce the defect rate of manufactured ICs, including FinFET technologies. We present results from a defect-oriented CAT fault model generation for 1,940 standard library cells, as well as the application of CAT to several industrial designs. We present high volume production test results from a 32 nm notebook processor and from a 350 nm automotive design, including the achieved defect rate reduction in defective-parts-per-million. We also present CAT diagnosis and physical failure analysis results from one failing part and give an outlook for using the functionality for quickly ramping up the yield in advanced technology nodes.
Atomic Force Microscopy of Asymmetric Membranes from Turtle Erythrocytes

The cell membrane provides critical cellular functions that rely on its elaborate structure and organization. The structure of turtle membranes is an important part of an ongoing study of erythrocyte membranes. Using a combination of atomic force microscopy and single-molecule force spectroscopy, we characterized the turtle erythrocyte membrane structure with molecular resolution in a quasi-native state. High-resolution images both leaflets of turtle erythrocyte membranes revealed a smooth outer membrane leaflet and a protein covered inner membrane leaflet. This asymmetry was verified by single-molecule force spectroscopy, which detects numerous exposed amino groups of membrane proteins in the inner membrane leaflet but much fewer in the outer leaflet. The asymmetric membrane structure of turtle erythrocytes is consistent with the semi-mosaic model of human, chicken and fish erythrocyte membrane structure, making the semi-mosaic model more widely applicable. From the perspective of biological evolution, this result may support the universality of the semi-mosaic model.
Literature-based discovery on the World Wide Web

Previous research has shown that researchers can generate medical hypotheses by using computers to analyze several, seemingly unrelated, medical literatures. In this work we suggest broader application for the ideas of literature-based discovery. Specifically, we suggest that literature-based discovery can be fruitful in areas other than medicine; that in addition to finding "cures" for "problems," literature-based discovery offers the possibility of finding new problems for existing technologies; that the analysis of a single literature may be sufficient for literature-based discovery; and that literature-based discovery can support individuals seeking to draw together ideas from various areas of inquiry, even if such connections have been previously made by others.We describe literature-based discovery experiments conducted on the World Wide Web that support these ideas.
Comparison of MRF and CRF for Text/Non-text Classification in Japanese Ink Documents

In the paper, we compare the methods based on Markov Random fields (MRF) and Conditional Random fields (CRF) for separating text and non-text ink strokes in online handwritten Japanese documents. This paper validates the effect of context information in neighbor strokes based on graphical models of MRF and CRF. The task of separating text and non-text ink strokes in ink documents denotes classifying ink strokes into two classes (text and non-text). For classification, Support Vector Machine (SVM) classifiers are trained on the set of ink strokes. After converting the SVM's outputs to likelihood probabilities, they are assigned to the likelihood clique potentials of MRF and the feature functions of CRF. The classification based on MRF or CRF is considered as a labeling problem, which can be solved using a labeling algorithm. The experiments on Japanese ink documents in the Kondate database shows that the proposed method based on CRF achieves a classification rate of 98.02% while the method based on MRF produces the classification rate of 97.86%.
Neural Basis of Embodiment: Distinct Contributions of Temporoparietal Junction and Extrastriate Body Area

Embodiment, the sense of being localized within one's physical body, is a fundamental aspect of the self. Recently, researchers have started to show that self and body processing require distinct brain mechanisms, suggesting two posterior brain regions as key loci: the temporoparietal junction (TPJ), which is involved in self processing and multisensory integration of body-related information; and the extrastriate body area (EBA), which responds selectively to human bodies and body parts. Here we used evoked potential mapping and a distributed linear inverse solution to show that activations in EBA and TPJ code differentially for embodiment and self location, because the location and timing of brain activation depended on whether mental imagery is performed with mentally embodied (EBA) or disembodied (TPJ) self location. In a second experiment, we showed that only EBA activation, related to embodied self location, but not TPJ activation, related to disembodied self location, was modified by the subjects' body position during task performance (supine or sitting). This suggests that embodied self location and actual body location share neural mechanisms. Collectively, these data show that distributed brain activity at the EBA and TPJ as well as their timing are crucial for the coding of the self as embodied and as spatially situated within the human body.
Electrostatic internal energy using the method of images

For several configurations of charges in the presence of conductors, the method of images permits us to obtain some observables associated with such a configuration by replacing the conductors with some image charges. However, simple inspection shows that the potential energy associated with both systems does not coincide. Nevertheless, it can be shown that for a system of a grounded or neutral conductor and a distribution of charges outside, the external potential energy associated with the real charge distribution embedded in the field generated by the set of image charges is twice the value of the internal potential energy associated with the original system. This assertion is valid for any size and shape of the conductor, and regardless of the configuration of images required. In addition, even in the case in which the conductor is not grounded nor neutral, it is still possible to calculate the internal potential energy of the original configuration through the method of images. These results show that the method of images could also be useful for calculations of the internal potential energy of the original system.
Membrane Redistribution of the Escherichia coli MinD Protein Induced by MinE

ABSTRACT Escherichia coli cells contain potential division sites at midcell and adjacent to the cell poles. Selection of the correct division site at midcell is controlled by three proteins: MinC, MinD, and MinE. It has previously been shown (D. Raskin and P. de Boer, Cell 91:685–694, 1997) that MinE-Gfp localizes to the midcell site in an MinD-dependent manner. We use here Gfp-MinD to show that MinD associates with the membrane around the entire periphery of the cell in the absence of the other Min proteins and that MinE is capable of altering the membrane distribution pattern of Gfp-MinD. Studies with the isolated N-terminal and C-terminal MinE domains indicated different roles for the two MinE domains in the redistribution of membrane-associated MinD.
RANCANGAN PELATIHAN PARALEL JARINGAN SYARAF DEEP LEARNING BERBASIS MAP-REDUCE

Jaringan saraf deep learning telah menjadi model pembelajar dengan unjuk kerja yang tinggi pada beragam persoalan pengenalan pola. Meskipun demikian, pelatihan model ini seringkali terkendala oleh keterbatasan memori serta oleh kecepatan pengolahan yang rendah ketika data pelatihan yang digunakan berukuran besar. Untuk menyelesaikan persoalan tersebut, penelitian ini mengusulkan suatu rancangan pelatihan paralel jaringan saraf deep learning berdasarkan kerangka kerja map-reduce pada klaster komputer. Map-reduce diadopsi sebagai kerangka kerja pelatihan paralel karena memiliki dukungan implementasi yang kuat dan beragam.
Progressive Sequence Matching for ADL Plan Recommendation

Activities of Daily Living (ADLs) are indicatives of a person's lifestyle. In particular, daily ADL routines closely relate to a person's well-being. With the objective of promoting active lifestyles, this paper presents an agent system that provides recommendations of suitable ADL plans (i.e., selected ADL sequences) to individual users based on the more active lifestyles of the others. Specifically, we develop a set of quantitative measures, named wellness scores, spanning the evaluation across the physical, cognitive, emotion, and social aspects based on his or her ADL routines. Then we propose an ADL sequence learning model, named Recommendation ADL ART, or RADLART, which proactively recommends healthier choices of activities based on the learnt associations among the user profiles, ADL sequence, and wellness scores. For empirical evaluation, extensive simulations have been conducted to assess the improvement in wellness scores for synthetic users with different acceptance rates of the provided recommendations. Experiments on real users further show that recommendations given by RADLART are generally more acceptable by the users because it takes into considerations of both the user profiles and the performed activities.
Jupiter / SVM : a JVM-based Single System Image for Clusters of Workstations

We address the problem of providing a single system image (SSI) on clusters of workstations, based on the Java Virtual Machine (JVM). Our approach is unique in that the needed functionality is separated in two layers: a shared virtual memory (SVM) system, CableS, that is optimized for system area networks and provides a standard Pthreads API, and a multithreaded JVM, Jupiter, that was originally developed for symmetric multiprocessors (SMPs). We identify the JVM extensions that are required to deal with CableS’s more relaxed memory consistency model, to optimize memory allocation by using private memory where possible, and to deal with various dynamic resource limitations imposed by CableS. We present a preliminary evaluation of the new JVM using the Java Grande benchmark suite on a 16-processor cluster of PCs interconnected with a Myrinet network, which (to the best of our knowledge) is the largest configuration reported to the literature. We find that: (i) the overhead introduced by SVM-specific extensions is less than 7% on average and (ii) Jupiter/SVM scales well to achieve an average speedup of 14 on 16 processors—a significantly better speedup than for previous reported work. Our main contribution is the conclusion that JVM-based SSIs for clusters do not have to be based on specially designed JVMs but may use JVMs that have been developed for popular SMP platforms.
Robust suboptimal control over lossy networks using extended input schemes

We consider an optimal control problem for networked, linear systems, where the loop is closed via a lossy network with a lossy acknowledgment mechanism. So, the data send from the controller to the actuators might be lost. In these cases often specific strategies called input scheme are used to choose backup inputs. In this work we employ an extended class of such strategies. We derive the necessary optimality conditions for linear controllers. Moreover, we present heuristic methods as well as bilinear matrix inequalities to determine these controllers in a suboptimal way. Examples illustrate that these extended input schemes can improve the performance.
Hair Interpolation for Portrait Morphing

In this paper we study the problem of hair interpolation: given two 3D hair models, we want to generate a sequence of intermediate hair models that transform from one input to another both smoothly and aesthetically pleasing. We propose an automatic method that efficiently calculates a many‐to‐many strand correspondence between two or more given hair models, taking into account the multi‐scale clustering structure of hair. Experiments demonstrate that hair interpolation can be used for producing more vivid portrait morphing effects and enabling a novel example‐based hair styling methodology, where a user can interactively create new hairstyles by continuously exploring a “style space” spanning multiple input hair models.
Scaling for Multimodal 3 D Object Detection

We investigate two methods for scalable 3D object detection. We base our approach on a recently proposed template matching algorithm [8] for detecting 3D objects. First, we demonstrate that it is possible to gain significant increase in runtime performance of the algorithm at almost no cost in accuracy by quickly rejecting most regions of the image with low-resolution templates. Second, we investigate an implicit part-based model that uses fixed-sized template dictionary and a Generalized Hough Transform framework to detect objects. We present results on two separate datasets that we collected using the Kinect sensor.
Optimal path planning for planetary exploration rovers based on artificial vision system for environment reconstruction

In this paper an optimal path planning algorithm for rover navigation is presented. The algorithm is based on an artificial vision system which reconstructs the digital elevation map of the environment through the use of two stereo cameras. On the basis of this digital elevation map the optimal path is generated with a simulated annealing approach. The direct use of the information in the digital elevation map allows to design a detailed path which takes care of each detected feature of the environment and of the cinematic limitation of the rover
ACE View --- an Ontology and Rule Editor based on Attempto Controlled English

We describe the architecture of a novel ontology and rule editor ACE View. The goal of ACE View is to simplify viewing and editing expressive and syntactically complex OWL/SWRL knowledge bases by making most of the interaction with the knowledge base happen via Attempto Controlled English (ACE). This makes ACE View radically different from current OWL/SWRL editors which are based on formal logic syntaxes and general purpose graphical user interface widgets. ACE View integrates two mappings, ACE→OWL/SWRL and OWL→ACE, and is implemented as a plug-in for Protege 4.
A cochlear implant speech processing strategy based on an auditory model

The use of auditory models in cochlear implant sound processing strategies aims to improve cochlear implant users' perception of speech, particularly in noisy environments. To date, cochlear implant sound processing strategies have been designed using simple envelope extraction techniques, Our new strategy, simulates the behaviour of the cochlea and the auditory nerve to give a stimulation sequence that produces responses in the auditory nerve that are closer to those in normal hearing. The development and evaluation of the strategy may also help to answer the fundamental question of how much benefit is provided by fine-grained timing information to the perception of different sounds that make up speech.
Learning to Support Adolescent Literacy: Teacher Educator Pedagogy and Novice Teacher Take Up in Secondary English Language Arts Teacher Preparation

Disciplinary literacy scholars promote text-based instruction in the service of disciplinary inquiry, and scholars of teacher education promote practice-based preparation for teachers. This study brings these scholarly communities into conversation by investigating how practice orientations in teacher education influence novice teachers’ literacy teaching. We conducted video analyses of teacher education coursework and novice teachers’ classroom instruction in secondary English language arts. Data were collected during a summer institute for novice teachers designed through a partnership between a university and an alternative teacher education program. Analyses revealed that when learning targets for novice teacher participants were explicitly connected to teaching practice through the use of representations, decompositions, and approximations, those targets were more frequently observed in novices’ subsequent classroom instruction.
OpenSDWN: programmatic control over home and enterprise WiFi

The quickly growing demand for wireless networks and the numerous application-specific requirements stand in stark contrast to today's inflexible management and operation of WiFi networks. In this paper, we present and evaluate OpenSDWN, a novel WiFi architecture based on an SDN/NFV approach. OpenSDWN exploits datapath programmability to enable service differentiation and fine-grained transmission control, facilitating the prioritization of critical applications. OpenSDWN implements per-client virtual access points and per-client virtual middleboxes, to render network functions more flexible and support mobility and seamless migration. OpenSDWN can also be used to out-source the control over the home network to a participatory interface or to an Internet Service Provider.
PICCO: Protocol Independent Congestion Control Method Using Mobile Relays for Ad-Hoc Networks

The limited lifetime of batteries and the bandwidth limitation of channels are the major issues in Ad-hoc networks. The term Congestion is usually associated with the bandwidth issues. The bandwidth keeps varying and when there is insufficient bandwidth to satisfy the demand, we say we have a congestion problem. Congestion control methods are partially implemented in the network elements (e.g.: routers) inside the network and partially in the transport protocols running on the end hosts. All congestion control methods were developed depending on the underlying transport protocol TCP and hence cannot be used when the underlying transport protocol changes to UDP. In real time applications, where UDP is mostly used with Real time Transport Protocol (RTP), lost data packets cannot be resent. So it becomes all the more critical to have a proper congestion control method which can be used by UDP as well. PICCO method to solve the congestion problem can be implemented only in the network elements and is independent of the underlying transport protocols. Hence it can be used for TCP as well as UDP. This method reduces the number of packets being dropped in the network, thus improving the overall performance of the network.
Analysis of Square-Law Combining for Cognitive Radios over Nakagami Channels

This paper addresses the problem of energy detection of a primary signal in a cognitive radio environment over Nakagami channels. As an extension to the previous works, we start with the no diversity case, and present some alternative closed-form expressions of detection probability under Nakagami channels. Then we pay much attention to studying the square-law combining technology when wireless users experience Nakagami fading (the outputs of all square-law devices are added to yield a new decision statistic), and derive closed-form expressions of detection probability when Nakagami parameters are integral. Interestingly, it is noted that, for a diversity scheme of SLC with L diversity branches, the detection probability in a Nakagami channel with intergral Nakagami parameter m is equivalent to that in a Rayleigh channel with mL diversity branches. Finally, performance evaluation is exhibited for different numbers of diversity branches and different values of Nakagami parameters.
Changes in soil organic carbon under biofuel crops

One potentially significant impact of growing biofuel crops will be the sequestration or release of carbon (C) in soil. Soil organic carbon (SOC) represents an important C sink in the lifecycle C balances of biofuels and strongly influences soil quality. We assembled and analyzed published estimates of SOC change following conversion of natural or agricultural land to biofuel crops of corn with residue harvest, sugarcane, Miscanthus x giganteus, switchgrass, or restored prairie. We estimated SOC losses associated with land conversion and rates of change in SOC over time by regressing net change in SOC relative to a control against age since establishment year. Conversion of uncultivated land to biofuel agriculture resulted in significant SOC losses – an effect that was most pronounced when native land was converted to sugarcane agriculture. Corn residue harvest (at 25–100% removal) consistently resulted in SOC losses averaging 3–8 Mg ha 1 in the top 30 cm, whereas SOC accumulated under all four perennial grasses, with SOC accumulation rates averaging o1 Mg ha 1 yr 1 in the top 30 cm. More intensive harvests led to decreased C gains or increased C losses – an effect that was particularly clear for residue harvest in corn. Direct or indirect conversion of previously uncultivated land for biofuel agriculture will result in SOC losses that counteract the benefits of fossil fuel displacement. Additionally, SOC losses under corn residue harvest imply that its potential to offset C emissions may be overestimated, whereas SOC sequestration under perennial grasses represents an additional benefit that has rarely been accounted for in life cycle analyses of biofuels.