Miracle-dz/UniFAR-FTUs huggingface dataset:
Caching in Heterogeneous Networks With Per-File Rate Constraints

We study the problem of caching optimization in heterogeneous networks with mutual interference and per-file rate constraints from an energy efficiency perspective. A setup is considered in which two cache-enabled transmitter nodes and a coordinator node serve two users. We analyze and compare two approaches: 1) a cooperative approach where each of the transmitters might serve either of the users and 2) a non-cooperative approach in which each transmitter serves only the respective user. We formulate the cache allocation optimization problem so that the overall system power consumption is minimized while the use of the link from the master node to the end users is spared whenever possible. We also propose a low-complexity optimization algorithm and show that it outperforms the considered benchmark strategies. Our results indicate that significant gains both in terms of power saving and sparing of master node’s resources can be obtained when full cooperation between the transmitters is in place. Interestingly, we show that in some cases storing the most popular files is not the best solution from a power efficiency perspective.
An approach to repair Petri net-based process models with choice structures

ABSTRACT Model repair is regarded as a new process mining technique. It can repair process models of information systems according to the generated event logs. The existing approaches focus on eliminating deviations between the activities performed by a process model and those observed in event logs. However, they cannot efficiently enhance other conformance dimensions, such as precision and simplicity. In order to obtain a repaired model with higher conformance, this work designs a new model repair approach. It builds process models via Petri nets (PNs) and investigates how to repair a PN-based process model with choice structures. An extended alignment is defined to be added to reachable markings of a Petri net. Then we can compute the deviations in the Petri net and repair the process model accordingly. The correctness and effectiveness of the proposed approach is illustrated by some examples.
Responses of respiration in the light to warming in field-grown trees: a comparison of the thermal sensitivity of the Kok and Laisk methods.

The Kok and Laisk techniques can both be used to estimate light respiration Rlight . We investigated whether responses of Rlight to short- and long-term changes in leaf temperature depend on the technique used to estimate Rlight . We grew Eucalyptus tereticornis in whole-tree chambers under ambient temperature (AT) or AT + 3°C (elevated temperature, ET). We assessed dark respiration Rdark and light respiration with the Kok (RKok ) and Laisk (RLaisk ) methods at four temperatures to determine the degree of light suppression of respiration using both methods in AT and ET trees. The ET treatment had little impact on Rdark , RKok or RLaisk . Although the thermal sensitivities of RKok or RLaisk were similar, RKok was higher than RLaisk . We found negative values of RLaisk at the lowest measurement temperatures, indicating positive net CO2 uptake, which we propose may be related to phosphoenolpyruvate carboxylase activity. Light suppression of Rdark decreased with increasing leaf temperature, but the degree of suppression depended on the method used. The Kok and Laisk methods do not generate the same estimates of Rlight or light suppression of Rdark between 20 and 35°C. Negative rates of RLaisk imply that this method may become less reliable at low temperatures.
Relative compression of trajectories

We present RCT, a new compact data structure to represent trajectories of objects. It is based on a relative compression technique called Relative Lempel-Ziv (RLZ), which compresses sequences by applying an LZ77 encoding with respect to an artificial reference. Combined with $O(z)$-sized data structures on the sequence of phrases that allows to solve trajectory and spatio-temporal queries efficiently. We plan that RCT improves in compression and time performance the previous compressed representations in the state of the art.
Alternative implementations of two-level adaptive branch prediction

As the issue rate and depth of pipelining of high performance Superscalar processors increase, the importance of an excellent branch predictor becomes more vital to delivering the potential performance of a wide-issue, deep pipelined microarchitecture. We propose a new dynamic branch predictor (Two-Level Adaptive Branch Prediction) that achieves substantially higher accuracy than any other scheme reported in the literature. The mechanism uses two levels of branch history information to make predictions, the history of the last L branches encountered, and the branch behavior for the last s occurrences of the specific pattern of these k branches. We have identified three variations of the Two-Level Adaptive Branch Prediction, depending on how finely we resolve the history information gathered. We compute the hardware costs of implementing each of the three variations, and use these costs in evaluating their relative effectiveness. We measure the branch prediction accuracy of the three variations of Two-Level Adaptive Branch Prediction, along with several other popular proposed dynamic and static prediction schemes, on the SPEC benchmarks. We show that the average prediction accuracy for TwoLevel Adaptive Branch Prediction is 97 percent, while the other known schemes achieve at most 94.4 percent average prediction accuracy. We measure the effectiveness of different prediction algorithms and different amounts of history and pattern information. We measure the costs of each variation to obtain the same prediction accuracy.
DeepTwist: Learning Model Compression via Occasional Weight Distortion

Model compression has been introduced to reduce the required hardware resources while maintaining the model accuracy. Lots of techniques for model compression, such as pruning, quantization, and low-rank approximation, have been suggested along with different inference implementation characteristics. Adopting model compression is, however, still challenging because the design complexity of model compression is rapidly increasing due to additional hyper-parameters and computation overhead in order to achieve a high compression ratio. In this paper, we propose a simple and efficient model compression framework called DeepTwist which distorts weights in an occasional manner without modifying the underlying training algorithms. The ideas of designing weight distortion functions are intuitive and straightforward given formats of compressed weights. We show that our proposed framework improves compression rate significantly for pruning, quantization, and low-rank approximation techniques while the efforts of additional retraining and/or hyper-parameter search are highly reduced. Regularization effects of DeepTwist are also reported.
Comparison Detector: A novel object detection method for small dataset

Though the object detection has shown great success when the training set is sufficient, there is a serious shortage of generalization in the small dataset scenario. However, we inevitably just get a small one in some application scenarios, especially medicine. In this paper, we propose Comparison detector which still maintains the end-to-end fashion in training and testing, surpassing the state-of-the-art two-stage object detection model on the small dataset. Inspired by one/few-shot learning, we replace the parameter classifier in feature pyramid network(FPN) with the comparison classifier in no-parameters or semi-parameters manner. In fact, a stronger inductive bias is added to the model to simplify the problem and reduce the dependence of data. The performance of our model is evaluated on the cervical cancer pathology test set. When training on the small dataset, it achieves a mAP 26.3% and an AR 35.7%, both improving about 20 points compared to baseline model. Moreover, Comparison detector achieves same mAP performance as the current state-of-the-art model when training on the medium dataset, and improves AR by 4 points. Our method is promising for the development of object detection in small dataset scenario.
Collaborative ring trial of two real-time PCR assays for the detection of porcine- and chicken-derived material in meat products

In this study, we describe an inter-laboratory collaborative ring trial validation of species-specific TaqMan real-time PCR assays for the detection of porcine- and chicken-derived materials in meat products. We comprehensively evaluated the performance of these assays in different environments and situations. This validation included the participation of thirteen laboratories across Europe and Asia. The results from the thirteen participating laboratories were analyzed to determine the specificity, accuracy, false positive rate, limit of detection (LOD), and probability of detection (POD) of the developed methods. These results indicated that the methods developed to detect porcine- and chicken-derived materials in meat products are robust and repeatable. The false positive and false negative rates were both 0%, and the LOD was determined to be five copies/reaction. The laboratory standard deviation (σL) was 0.30 for both detection methods, indicating that the developed methods are suitable for detection and identification of the porcine- and chicken-derived materials in meat products.
Relating Experience Goals With Visual User Interface Design

This paper examines the cognitive process of visually experiencing user interfaces. It contributes to a theory-and methodology-grounded understanding of how UIs are experienced with regard to various aesthetic criteria. This aids in considering the targeted experience goals in relation to visual design choices – a problem that designers usually have to tackle intuitively. The issue in explicitly relating designs to experiences stems from the complexity of the process in which visual stimuli are processed and turned into experiences. The authors present a cognitive top-down approach to this process, rooted in the appraisal theory and the theory of the predictive brain. Several predictions are derived via this approach, and an eye-tracking experiment with Web sites is presented that provides evidence of them. The experience goals and repeated exposure to stimuli are shown to aﬀect appraisal times and visual scanpaths in Web pages’ evaluation; this supports the top-down approach described. Researchers can use the ﬁndings to inform their theoretical and empirical pursuits as they strive to understand what makes design artefacts emotionally evocative, and the methodology outlined can assist designers in locating the visual regions and elements relevant for experiential design goals.
Recognizing heterogeneous sequences by rational type expression

We summarize a technique for writing functions which recognize types of heterogeneous sequences in Common Lisp. The technique employs sequence recognition functions, generated at compile time, and evaluated at run-time. The technique we demonstrate extends the Common Lisp type system, exploiting the theory of rational languages, Binary Decision Diagrams, and the Turing complete macro facility of Common Lisp. The resulting system uses meta-programming to move an Omega(2^n) complexity operation from run-time to a compile-time operation, leaving a highly optimized Theta(n) complexity operation for run-time.
Restoration and Efficiency of the Neural Processing of Continuous Speech Are Promoted by Prior Knowledge

Sufficiently noisy listening conditions can completely mask the acoustic signal of significant parts of a sentence, and yet listeners may still report the perception of hearing the masked speech. This occurs even when the speech signal is removed entirely, if the gap is filled with stationary noise, a phenomenon known as perceptual restoration. At the neural level, however, it is unclear the extent to which the neural representation of missing extended speech sequences is similar to the dynamic neural representation of ordinary continuous speech. Using auditory magnetoencephalography (MEG), we show that stimulus reconstruction, a technique developed for use with neural representations of ordinary speech, works also for the missing speech segments replaced by noise, even when spanning several phonemes and words. The reconstruction fidelity of the missing speech, up to 25% of what would be attained if present, depends however on listeners’ familiarity with the missing segment. This same familiarity also speeds up the most prominent stage of the cortical processing of ordinary speech by approximately 5 ms. Both effects disappear when listeners have no or little prior experience with the speech segment. The results are consistent with adaptive expectation mechanisms that consolidate detailed representations about speech sounds as identifiable factors assisting automatic restoration over ecologically relevant timescales.
Electronic Cigarette Themes on Twitter: Dissemination Patterns and Relations with Online News and Search Engine Queries in South Korea

ABSTRACT This study examines multiple aspects of e-cigarette mentions on different online media channels during the announcement and implementation of a cigarette tax increase policy in South Korea. It consists of three parts. First, a Naive Bayes classifier was used to sort 59,147 tweets about e-cigarettes into five pre-designated themes — promotion, health, policy, product evaluation, and price — and to determine their relative prevalence. Second, social network analysis was used to identify the themes’ dissemination patterns. Third, the themes were examined for their associations with e-cigarette mentions in two other media channels — online news articles (n = 580) and search engine queries (64 weeks of Google Trends data). Results show that the most prevalent tweet theme was product evaluation, and the theme with the largest increase during the data collection period was promotion. Promotion-themed tweets were the least connected with tweets about the other five themes, while health-themed tweets were the most connected. Finally, product evaluation-themed tweets exhibited the strongest correlation with search engine query count and had the largest explanatory power.
The Focus-Aspect-Polarity Model for Predicting Subjective Noun Attributes in Images

Subjective visual interpretation is a challenging yet important topic in computer vision. Many approaches reduce this problem to the prediction of adjective- or attribute-labels from images. However, most of these do not take attribute semantics into account, or only process the image in a holistic manner. Furthermore, there is a lack of relevant datasets with fine-grained subjective labels. In this paper, we propose the Focus-Aspect-Polarity model to structure the process of capturing subjectivity in image processing, and introduce a novel dataset following this way of modeling. We run experiments on this dataset to compare several deep learning methods and find that incorporating context information based on tensor multiplication in several cases outperforms the default way of information fusion (concatenation).
Topic-Specific Sentiment Analysis Can Help Identify Political Ideology

Ideological leanings of an individual can often be gauged by the sentiment one expresses about different issues. We propose a simple framework that represents a political ideology as a distribution of sentiment polarities towards a set of topics. This representation can then be used to detect ideological leanings of documents (speeches, news articles, etc.) based on the sentiments expressed towards different topics. Experiments performed using a widely used dataset show the promise of our proposed approach that achieves comparable performance to other methods despite being much simpler and more interpretable.
A Meta-Analysis of Vascular Endothelial Growth Factor for Nasopharyngeal Cancer Prognosis

Background: Vascular endothelial growth factor (VEGF) has been reported to serve as a promising prognostic marker in several cancers. This meta-analysis aims to assess the prognostic significance of VEGF in nasopharyngeal cancer (NPC). Methods: We conducted a systematic literature search of PubMed, Embase, and the Cochrane Library for observational studies published until June, 2018 to identify observational studies on the prognostic effect of tissue VEGF expression or serum VEGF level on the survival of NPC. The primary outcome measure assessed was overall survival (OS). The secondary outcomes included disease-free survival (DFS) or progression-free survival (PFS). Summary hazard ratio (HR) and its 95% confidence interval (95% CI) were derived using a random-effects model. Results: Out of 840 retrieved citations, 16 studies inclusive of 1,345 patients were included in the analysis of tissue VEGF expression and cancer survival. The pooled HRs for OS and DFS in patients with high VEGF expression were 2.07 (95% CI: 1.32–3.25) and 5.99 (95% CI: 2.66–13.48), respectively, with significant heterogeneity between studies (I2 = 79.1% for OS and 50.2% for DFS). Tissue high VEGF expression was not significantly associated with short RFS, PFS, or MFS. Five studies also investigated the prognostic effect between serum VEGF level and patient survival and found that high serum VEGF level was significantly associated with short OS for patients with NPC (HR 2.47, 95% CI 1.16–5.28), but not with short PFS (HR 1.47, 95% CI 0.92–2.35). Conclusions: Determination of tissue VEGF expression and serum VEGF level have the potential to serve as biomarkers and add prognostic information in NPC. Prospective analyses of associated data on VEGF expression and serum VEGF level in large NPC cohorts could be further conducted to advance our understanding of the relationship between VEGF and NPC outcomes.
Roles for Ena/VASP proteins in FMNL3-mediated filopodial assembly

ABSTRACT Filopodia are actin-dependent finger-like structures that protrude from the plasma membrane. Actin filament barbed-end-binding proteins localized to filopodial tips are key to filopodial assembly. Two classes of barbed-end-binding proteins are formins and Ena/VASP proteins, and both classes have been localized to filopodial tips in specific cellular contexts. Here, we examine the filopodial roles of the FMNL formins and Ena/VASP proteins in U2OS cells. FMNL3 suppression reduces filopodial assembly by 90%, and FMNL3 is enriched at >95% of filopodial tips. Suppression of VASP or Mena (also known as ENAH) reduces filopodial assembly by >75%. However, VASP and Mena do not display consistent filopodial tip localization, but are enriched in focal adhesions (FAs). Interestingly, >85% of FMNL3-containing filopodia are associated with FAs. Two situations increase Ena/VASP filopodial localization: (1) expression of myosin-X, and (2) actively spreading cells. In spreading cells, filopodia often mark sites of nascent adhesions. Interestingly, VASP suppression in spreading cells causes a significant increase in adhesion assembly at filopodial tips. This work demonstrates that, in U2OS cells, Ena/VASP proteins play roles in filopodia beyond those at filopodial tips. This article has an associated First Person interview with the first author of the paper. Summary: Both formins and ENA/VASP proteins mediate filopodial assembly. In U2OS cells, the formin FMNL3 functions at filopodial tips, whereas ENA/VASP proteins may influence filopodia at focal adhesions.
A systematic review on small-sided games in football players: Acute and chronic adaptations

ABSTRACT Small-sided games (SSG) are played on a small pitch, often using modified rules and involving a smaller number of players. This article aimed to critically analyse the literature to determine how small-sided games affect the performance of football players in the short- and long term. Electronic databases were searched for literature dating from January 2000 to July 2018. The methodological quality of the studies was evaluated using the modified Downs and Black Quality Index (cross-sectional studies) and the Physiotherapy Evidence Database (PEDro) scale (intervention studies). Fifty-three studies, 44 cross-sectional and 9 intervention studies, met the inclusionary criteria for review. Most of the cross-sectional studies focused on describing the differences between SSG protocols, whereas 4 studies focused on making a comparison between “interval” and “continuous” SSG training regimes. On the other hand, intervention studies focused on making a comparison between SSG-based protocols and high-intensity intermittent training (HIIT)-based running protocols, in addition to determine the effect of a SSG-based training programme alone. SSG-based football plans (2 to 4 SSG sessions per week) show athletic performance improvements in football players by improving sprint, repeated sprint ability (RSA) and change of direction (COD) along with muscular and physiological adaptation.
Hyperhomocysteinemia as a Risk Factor for Vascular Contributions to Cognitive Impairment and Dementia

Behind only Alzheimer’s disease, vascular contributions to cognitive impairment and dementia (VCID) is the second most common cause of dementia, affecting roughly 10–40% of dementia patients. While there is no cure for VCID, several risk factors for VCID, such as diabetes, hypertension, and stroke, have been identified. Elevated plasma levels of homocysteine, termed hyperhomocysteinemia (HHcy), are a major, yet underrecognized, risk factor for VCID. B vitamin deficiency, which is the most common cause of HHcy, is common in the elderly. With B vitamin supplementation being a relatively safe and inexpensive therapeutic, the treatment of HHcy-induced VCID would seem straightforward; however, preclinical and clinical data shows it is not. Clinical trials using B vitamin supplementation have shown conflicting results about the benefits of lowering homocysteine and issues have arisen over proper study design within the trials. Studies using cell culture and animal models have proposed several mechanisms for homocysteine-induced cognitive decline, providing other targets for therapeutics. For this review, we will focus on HHcy as a risk factor for VCID, specifically, the different mechanisms proposed for homocysteine-induced cognitive decline and the clinical trials aimed at lowering plasma homocysteine.
Expert Systems With Applications

Knowledge extraction and incorporation is currently considered to be beneﬁcial for eﬃcient Big Data analytics. Knowledge can take part in workﬂow design, constraint deﬁnition, parameter selection and con-ﬁguration, human interactive and decision-making strategies. This paper proposes BIGOWL, an ontology to support knowledge management in Big Data analytics. BIGOWL is designed to cover a wide vocabulary of terms concerning Big Data analytics workﬂows, including their components and how they are connected, from data sources to the analytics visualization. It also takes into consideration aspects such as parameters, restrictions and formats. This ontology deﬁnes not only the taxonomic relationships be-tween the different concepts, but also instances representing speciﬁc individuals to guide the users in the design of Big Data analytics workﬂows. For testing purposes, two case studies are developed, which consists in: ﬁrst, real-world streaming processing with Spark of traﬃc Open Data, for route optimization in urban environment of New York city; and second, data mining classiﬁcation of an academic dataset on local/cloud platforms. The analytics workﬂows resulting from the BIGOWL semantic model are validated and successfully evaluated.
Probability and Computing

The power of randomization in the computational context is one of the fundamental discoveries of computer science; furthermore, statistical and probabilistic reasoning is indispensable for modern-day data analysis and data science. In this significantly-expanded second edition, the authors (continuing to assume exposure to just basic discrete probability) present a unified treatment of probabilistic concepts useful for modern computer science and data science.