Miracle-dz/UniFAR-FTUs huggingface dataset:
Community-enhanced Network Representation Learning for Network Analysis

Network representation learning (NRL) aims to build low-dimensional vectors for vertices in a network. Most existing NRL methods focus on learning representations from local context of vertices (such as their neighbors). Nevertheless, vertices in many complex networks also exhibit significant global patterns widely known as communities. It's a common sense that vertices in the same community tend to connect densely, and usually share common attributes. These patterns are expected to improve NRL and benefit relevant evaluation tasks, such as link prediction and vertex classification. In this work, we propose a novel NRL model by introducing community information of vertices to learn more discriminative network representations, named as Community-enhanced Network Representation Learning (CNRL). CNRL simultaneously detects community distribution of each vertex and learns embeddings of both vertices and communities. In this way, we can obtain more informative representation of a vertex accompanying with its community information. In experiments, we evaluate the proposed CNRL model on vertex classification, link prediction, and community detection using several real-world datasets. The results demonstrate that CNRL significantly and consistently outperforms other state-of-the-art methods. Meanwhile, the detected meaningful communities verify our assumptions on the correlations among vertices, sequences, and communities.
Source Selection and Content Dissemination for Preference-Aware Traffic Offloading

As mobile devices become more ubiquitous, the amount of cellular traffic for multimedia content grows explosively. Therefore, data dissemination through proximity-based opportunistic communications attracts the attention of service providers who are eager for solutions of traffic offloading. In this paper, we propose PrefCast, a preference-aware opportunistic content dissemination protocol that uses as few cellular bandwidth as possible to maximally satisfy user preferences for content objects. The efficiency of PrefCast depends on 1) how does the base-station select initial sources, and 2) how does each user forward objects within a limited contact duration. Since mobile users typically form communities and have heterogeneous preferences, PrefCast's base-station selects sources that efficiently produce the maximal utility to their communities. We then derive a model to predict how much utility a forwarder can contribute to future contacts. PrefCast's users can hence use such prediction to find their optimal forwarding schedule, which maximizes the utility contribution, in a distributed way. Our trace-based evaluation shows that, without explicit source selection, PrefCast produces a 15.7 and 22.6 percent higher average utility than the protocols that only consider contact frequency or preference of local contacts, respectively. Enabling source selection in PrefCast further improves the utility by 49.3 percent.
CCP11 Group Meeting—Towards the Functional Analysis of Microarrays

The CCP11 project [2] aims to foster bioinformatics in the UK through conferences, workshops and the provision of Web resources. In March 2002, CCP11 held a meeting in Manchester, UK, on the functional analysis of microarrays. This was part of Manchester BioinformaticsWeek—three consecutive short bioinformatics meetings held in the attractive setting of the Chancellor's Conference Centre at the University of Manchester. The other meetings in the series were a workshop on ontologies and the 12th Annual MASAMB (Mathematical and Statistical Aspects of Molecular Biology) Conference. Many delegates were able to attend more than one meeting, which led to a useful cross-fertilization of ideas across the bioinformatics community. The CCP11 meeting shared with MASAMB a strong emphasis on the statistical analysis and interpretation of data—most often image intensity data.
If No Media Were Allowed inside the Venue, Was Anybody Allowed?

This paper presents a framework to understand negation in positive terms. Specifically, we extract positive meaning from negation when the negation cue syntactically modifies a noun or adjective. Our approach is grounded on generating potential positive interpretations automatically, and then scoring them. Experimental results show that interpretations scored high can be reliably identified.
The medium helps the message: Early sensitivity to auditory fluency in children’s endorsement of statements

Recently, a growing number of studies have investigated the cues used by children to selectively accept testimony. In parallel, several studies with adults have shown that the fluency with which information is provided influences message evaluation: adults evaluate fluent information as more credible than dysfluent information. It is therefore plausible that the fluency of a message could also influence children’s endorsement of statements. Three experiments were designed to test this hypothesis with 3- to 5-year-olds where the auditory fluency of a message was manipulated by adding different levels of noise to recorded statements. The results show that 4 and 5-year-old children, but not 3-year-olds, are more likely to endorse a fluent statement than a dysfluent one. The present study constitutes a first attempt to show that fluency, i.e., ease of processing, is recruited as a cue to guide epistemic decision in children. An interpretation of the age difference based on the way cues are processed by younger children is suggested.
Early Recanalization Rates and Clinical Outcomes in Patients With Tandem Internal Carotid Artery/Middle Cerebral Artery Occlusion and Isolated Middle Cerebral Artery Occlusion

Background— Patients with isolated middle cerebral artery (MCA) and tandem MCA/internal carotid artery (ICA) obstruction have similar presenting symptoms and stroke severity. We aimed to investigate early recanalization of MCA and clinical outcomes in patients with tandem ICA/MCA obstructions and isolated MCA occlusion. Methods— Patients with MCA occlusion on pretreatment transcranial Doppler were treated with intravenous tissue plasminogen activator. ICA lesions were detected with carotid duplex. Early neurological improvement (ENI) was defined by reduction of National Institutes of Health Stroke Scale (NIHSS) ≥10 points or total NIHSS ≤3 points. Good outcome at 3 months was a modified Rankin score of ≤2. Results— Among 104 patients, 31% had tandem lesions and 69% had isolated MCA occlusions. Complete recanalization rate was 39% in isolated MCA occlusion group and 9% in tandem group (P=0.002). ENI at 24 hours occurred in 46% of the isolated MCA occlusion group and in 25% of the tandem group (P=0.045). Good outcome was achieved by 30% and 39% respective patients (NS). Conclusion— The tandem lesion group showed lower early recanalization rate and ENI rate than the isolated MCA occlusion group. Despite this, good outcomes were similar in both groups.
Context as a Dynamic Construct

Context is a dynamic construct. Although some contextual situations are fairly stable, discernable, and predictable, there are many others that are not. Similar looking contextual situations may actually differ dramatically, due perhaps to people's previous episodes of use, the state of their social interactions, their changing internal goals, and the nuances of local influences. The consequence is that, for all but simple cases, the designer of a context-aware application may find it difficult or even impossible to (a) enumerate the set of contextual states that may exist, (b) know what information could accurately determine a contextual state within that set, and (c) state what appropriate action should be taken from a particular state.
Improving trace-based JIT optimisation using whole-program information

Trace-based just-in-time compilers use program analyses to optimise execution traces. These analyses are limited in scope to the parts of the program that have been traced. In this position paper, we conjecture that trace optimisations can benefit from extending the scope of their enabling analyses beyond these traces to the set of possible future execution states of the program. This by incorporating a static analysis which, however, necessarily over-approximates this set. We therefore propose to continuously refine the set of future states computed by an initial, ahead-of-time analysis using run-time information about the current execution state of the program. Additional static analyses launched at run time could further refine the information about the future of the current and all possible states. We expect that the resulting, hybrid program view of static and dynamic information may enable additional optimisations on collected traces and that these optimisations may overcome the computational overhead of keeping the view up-to-date.
Pulsed Nd:YAG laser assisted fabrication of graphene nanosheets in water

Graphene nanosheets were prepared by pulsed Nd:YAG laser ablation of graphite target in H_2O under ambient conditions. The synthesized graphene nanosheets were characterized by high resolution transmission electron microscope (HRTEM), X-ray diffraction (XRD), Raman spectroscopy and Selected Area Electron Diffraction (SAED). The obtained structural and morphological analysis confirmed that the graphene nanosheets could be formed in an aqueous medium via one step method where a nanosecond pulsed near-infrared (NIR) laser (λ = 1064 nm) is used to ablate the surface of a pure graphite target. Compared to other used chemical methods to synthesis graphene nanosheets, laser ablation is an easy, versatile, environmental friendly and rapidly growing method for the synthesis of nanostructured materials such as graphene nanosheets. This technique showed normal operation in liquid medium (i.e. water or organic) under ambient conditions. Our study confirmed the great potential of laser ablation in liquid method for the fabrication of graphene nanosheets based nanofluids wich has a potential applicatiuon as a heat transfer fluid.
Fair-Priority-Expression-Based burst scheduling to enhance performance and fairness of shared dram systems

Many algorithms ofmemory access scheduling have been studied to attack the well-known memory-wall. However, some of them introduce unfairness problem in Chip-Multi-Processor (CMP) systems. Therefore a new approach called Fair-Priority-Expression-Based (FairPEB) burst scheduling is proposed in this paper to address both performance and unfairness problems. Experiment results show that this method improves data bus utilization of memory system by 15.4% and 31.87% over conventional First-Ready First-Come-First-Service (FR-FCFS) and original burst scheduling. Furthermore it keeps memory accesses of different applications fair.
Equivalent system model and equalization of differential impulse radio UWB systems

A discrete-time equivalent system model is derived for differential and transmitted reference (TR) ultra-wideband (UWB) impulse radio (IR) systems, operating under heavy intersymbol-interference (ISI) caused by multipath propagation. In the systems discussed, data is transmitted using differential modulation on a frame-level, i.e., among UWB pulses. Multiple pulses (frames) are used to convey a single bit. Time hopping and amplitude codes are applied for multi user communications, employing a receiver front-end that consists of a bank of pulse-pair correlators. It is shown that these UWB systems are accurately modeled by second-order discrete-time Volterra systems. This proposed nonlinear equivalent system model is the basis for developing optimal and suboptimal receivers for differential UWB communications systems under ISI. As an example, we describe a maximum likelihood sequence detector with decision feedback, to be applied at the output of the receiver front-end sampled at symbol rate, and an adaptive inverse modeling equalizer. Both methods significantly increase the robustness in presence of multipath interference at tractable complexity.
Habenula Circuit Development: Past, Present, and Future

The habenular neural circuit is attracting increasing attention from researchers in fields as diverse as neuroscience, medicine, behavior, development, and evolution. Recent studies have revealed that this part of the limbic system in the dorsal diencephalon is involved in reward, addiction, and other behaviors and its impairment is associated with various neurological conditions and diseases. Since the initial description of the dorsal diencephalic conduction system (DDC) with the habenulae in its center at the end of the nineteenth century, increasingly sophisticated techniques have resolved much of its anatomy and have shown that these pathways relay information from different parts of the forebrain to the tegmentum, midbrain, and hindbrain. The first part of this review gives a brief historical overview on how the improving experimental approaches have allowed the stepwise uncovering much of the architecture of the habenula circuit as we know it today. Our brain distributes tasks differentially between left and right and it has become a paradigm that this functional lateralization is a universal feature of vertebrates. Moreover, task dependent differential brain activities have been linked to anatomical differences across the left–right axis in humans. A good way to further explore this fundamental issue will be to study the functional consequences of subtle changes in neural network formation, which requires that we fully understand DDC system development. As the habenular circuit is evolutionarily highly conserved, researchers have the option to perform such difficult experiments in more experimentally amenable vertebrate systems. Indeed, research in the last decade has shown that the zebrafish is well suited for the study of DDC system development and the phenomenon of functional lateralization. We will critically discuss the advantages of the zebrafish model, available techniques, and others that are needed to fully understand habenular circuit development.
GOME Fast delivery and value-Added Products ( GOFAP )

A Fast Delivery processor has been developed to provide GOME ozone products within 3 hours after observation. This service meets the growing demand for ozone products for purposes like assimilation in numerical weather prediction models, UV radiation forecasts, and planning of experiments related to atmospheric chemistry research. The ozone products consist of total ozone columns, the global assimilated ozone field, global stratospheric ozone profiles, cloud fraction, cloud top pressure and the aerosol absorption index. The validation and current developments of the near-real time processor and the delivered ozone products are discussed.
CLUE-Aligner : An Alignment Tool to Annotate Pairs of Paraphrastic and Translation Units

Currently available alignment tools and procedures for marking-up alignments overlook non-contiguous multiword units for being too complex within the bounds of the proposed alignment methodologies. This paper presents the CLUE-Aligner (Cross-Language Unit Elicitation Aligner), a web alignment tool designed for manual annotation of pairs of paraphrastic and translation units, representing both contiguous and non-contiguous multiwords and phrasal expressions found in monolingual or bilingual parallel sentences. Non-contiguous block alignments are necessary to express alignments between multiwords or phrases, which contain insertions, i.e., words that are not part of the multiword unit or phrase. CLUE-Aligner also allows the alignment of smaller individual or multiword units inside non-contiguous multiword units. The interactive web application was developed under the scope of the eSPERTo project, which aims to build a linguistically enhanced paraphrasing system. However, a tool for manual annotation of alignment and for visualization of automatic phrase alignment can prove useful in human and machine translation evaluation.
Imperative program optimization by partial evaluation

We implement strength reduction and loop-invariant code motion by specializing instrumented interpreters; we define a novel program transformation that uses bisimulation to identify and remove code duplication in residual programs; and we discover that some simple classical optimizations, notably constant-propagation, seemingly do not lend themselves to implementation by specialization of instrumented interpreters.
Privately detecting correlations in distributed time series

In this paper, we consider privacy preservation in the context of independently owned, distributed time series data. Specifically, we are interested in discovering correlations even though we cannot share the raw time series values. We propose developing a generic framework for identifying similarities or correlations of a particular behavior or statistic across participants. Our generic framework makes use of the additive combining property of certain statistics. It also allows for sharing of scaled bin values instead of raw data or statistical values to improve levels of privacy. We find that while there is a natural trade off between privacy and accuracy, we can maintain reasonable correlation accuracy for different levels of privacy.
Efficient Quantitative Information Extraction from PCR-RFLP Gel Electrophoresis Images

For the purpose of PCR-RFLP analysis, as in the case of human papillomavirus (HPV) typing, quantitative information needs to be extracted from images resulting from one-dimensional gel electrophoresis by associating the image intensity with the concentration of biological material at the corresponding position on a gel matrix. However, the background intensity of the image stands in the way of quantifying this association. We propose a novel, efficient methodology for modeling the image background with a polynomial function and prove that this can benefit the extraction of accurate information from the lane intensity profile when modeled by a superposition of properly shaped parametric functions.
New dimensionless parameter construction using genetic programming for fault classifying of rotating machinery

In this paper, an approach to apply genetic programming in combination with dimensionless parameter of time domain is proposed. According to this approach, new dimensionless parameter is constructed. The effectiveness of the new dimensionless parameter is validated by an illustrative experiment. Experimental result shows that the classification ability of new dimensionless parameter is better than that of existing ones.
Contextualizing Semantic Representations Using Syntactically Enriched Vector Models

We present a syntactically enriched vector model that supports the computation of contextualized semantic representations in a quasi compositional fashion. It employs a systematic combination of first- and second-order context vectors. We apply our model to two different tasks and show that (i) it substantially outperforms previous work on a paraphrase ranking task, and (ii) achieves promising results on a wordsense similarity task; to our knowledge, it is the first time that an unsupervised method has been applied to this task.
Error feedback based lexical entity extraction for Chinese language modeling

Chinese, which is quite different from western languages, has no standard definition of word. Therefore, choosing suitable lexicon plays an important role in Chinese language modeling. This paper proposes a novel method of constructing the lexicon automatically. Other than depending on statistical measures of text features, this method is directly based on the feedback of errors from the corresponding task, such as phoneme-to-grapheme conversion in this paper. The whole process consists of two iterative phases: selection of individual words from a large manual lexicon and further extraction of compound words based on Phase One. Experiments implemented on phoneme-to-grapheme conversion show that this method can achieve 1.09% and 0.38% absolute reduction in character error rate respectively for Phase One and Phase Two compared with baseline lexicons in the same size generated by the conventional method based on word frequency.
Next-generation High Efficiency and Low Cost GaAs/Si Multijunction Solar Cells with Smart Stack Technology

Multi-junction (MJ) solar cells have a practical solution to consist with high efficiency and low cost. This paper shows the demonstrations of a GaAs/Si MJ solar cells with mechanical stacking method. Our key technology is the direct bonding using conductive nanoparticle alignment, which is named “Smart Stack” technology. Using this technology, we fabricated an InGaP/GaAs/Si 3-junction solar cell and observed the efficiency of 24.71% (AM1.5g). According to our theoretical prediction, these efficiencies can be improved over 30% under the optimized structure design. In addition, we examined the cost analysis of the GaAs/Si MJ module. Under the low concentration, the cost attains the competitive level (module cost with lens_ <0.4$/W). The obtained results show the possibility of GaAs/ Si MJ solar cells as next generation solar cell.
Metal Nanoparticles: Synthesis and Applications in Pharmaceutical Sciences

A much-needed summary of the importance, synthesis and applications of metal nanoparticles in pharmaceutical sciences, with a focus on gold, silver, copper and platinum nanoparticles. After a brief introduction to the history of metal complexes in medicine and fundamentals of nanotechnology, the chapters continue to describe different methods for preparation of metal nanoparticles. This section is followed by representative presentations of current biomedical applications, such as drug delivery, chemotherapy, and diagnostic imaging.
Capacitive micromachined ultrasonic transducer (CMUT) with graphene membrane

This paper presents fabrication methods using graphene as a membrane that was thermally bonded on top of the CMUT cavity formed by patterned photoresist. The impedance measurement result showed graphene CMUT exhibit high resonance frequency when membrane size remains large. Previously we had also reported such a graphene CMUT with stiction problem caused by a graphene transfer technique. In the newly fabricated CMUT, different methods are used to protect the graphene membrane and stiction problem was solved by bonding in air. The resonant frequencies of the fabricated graphene CMUT with membrane sizes of 500μm and 90μm are found to be 45MHz and 63MHz respectively in air.
Stationary solutions, intermediate asymptotics and large-time behaviour of type II Streater's models

In this paper, we study type II Streater’s models. These models describe the coupled evolution of the density of a cloud of particles in an external potential and a temperature, preserving the energy, with eventually a nonlocal Poisson coupling. We introduce an entropy and consider in a bounded domain, or in an unbounded domain with a confining external potential, the stationary solutions (with given mass and energy), for which we have existence and uniqueness results. The entropy is reinterpreted as a relative entropy which controls the convergence to the stationary solutions. We consider also the whole IR d space problems without exterior potential using time-dependent rescalings and show the existence of intermediate asymptotics in special cases.
Correspondence between the 3-point BMN correlators and the 3-string vertex on the pp-wave

The PP-wave/SYM proposal in its original form emphasizes a duality relation between the masses of the string states and the anomalous dimensions of the corresponding BMN operators in gauge theory, the mass-dimension type duality. In this paper, we give evidence in favour of another duality relation of the vertex-correlator type, which relates the coefficients of 3-point correlators of BMN operators in gauge theory to 3-string vertices in lightcone string field theory in the pp-wave background. We verify that all the available field theory results in the literature, as well as the newly obtained ones, for the 3-point functions are successfully reproduced from our proposal.