Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a Sweet Spot of Dataplane Programmability , Portability and Performance : On the Scalability of Multi-Architecture P 4 Pipelines

Despite having received less attention compared to the control and application plane aspects of Software-Defined Networking (SDN), the data plane is a critical piece of the puzzle. P4 takes SDN datapaths to the next level by unlocking deep programmability through a target-independent high-level programming language that can be compiled to run on a variety of targets (e.g., ASIC, FPGA, GPU). This article presents the design and evaluation of our sweet spot approach on SDN datapaths offering three contending characteristics, namely, performance, portability and scalability in multiple realistic scenarios. The focus is on our Multi-Architecture Compiler System for Abstract Dataplanes (MACSAD) proposal, which blends the high-level protocol-independent programmability of P4 with lowlevel but cross-platform (HW & SW) APIs brought by OpenDataPlane (ODP), this way supporting many different vendors and architectures. Besides the performance evaluation for varying packet sizes and memory lookup tables, we investigate the impact of increasing pipeline complexity ranging from elemental L2 switching to more complex data center and border network gateways. We investigate the scalability for increasing number of cores and evaluate a novel method for run-time core reallocation. Furthermore, we run experiments on different target platforms (e.g., x86, ARM, 10G/100G), inducing different ways of packet mangling through specific drivers (e.g., DPDK, Netmap), and compare the results to state-of-the-art datapath alternatives.
Quantification of the factors controlling tropical tropospheric ozone and the South Atlantic maximum

[1] We quantify the processes controlling the tropical tropospheric ozone burden with particular attention to the tropical Atlantic, using a global chemical transport model (GEOS-Chem) constrained by satellite and in situ observations of O3, NO2, and HCHO. Lightning is the dominant contributor to tropical tropospheric O3, accounting for more than 37% of the O3 burden over the Atlantic on annual average. The contributions from biomass burning, soils, and fossil fuels are 4 to 6 times smaller, despite comparable source strengths. This discrepancy can be explained by the tropical ozone production efficiency of lightning (32 mol/mol), soils (14 mol/mol), biomass burning (10 mol/mol), and fossil fuel (13 mol/mol) sources, as calculated using sensitivity simulations with a 1% perturbation. The role of volatile organic compound emissions on the tropical Atlantic ozone burden is negligible ( 30%) than from South America (>18%) or the eastern tropics (>11%). Lightning is responsible for more than 39% of the atmospheric oxidation capacity, higher than other sources. The dominant sources of uncertainty in the tropical oxidation rate are the lightning magnitude and the cloud convective parameterization.
Scaling and Placing Bidirectional Services with Stateful Virtual and Physical Network Functions

Network function virtualization requires scaling and placement, deciding the number and the location of function instances. Current approaches are limited in flexibility and practical applicability. Specifically, we study dynamic, single-step, joint scaling and placement of network services with bidirectional flows traversing Physical or Virtual Network Functions (VNFs) and returning to their sources. We develop models to support stateful components and legacy network functions with fixed locations in these network services as well as the possibility of reusing VNFs across network services. We formalize the problem of jointly scaling and placing such network services as a mixed-integer linear program (MILP). We show that this problem is NP-complete and also present a heuristic algorithm to find good solutions in short time. In an extensive evaluation with realistic scenarios, we investigate the capabilities of the two approaches.
Structured Prediction with Adversarial Constraint Learning

Constraint learning is a recently proposed form of weak supervision which attempts to reduce the labeling burden by having users specify general properties that hold over the output space (e.g. physical laws). However, specifying constraints can be difficult and may require extensive domain expertise. In this paper, we introduce an adversarial constraint learning framework in which invariants are automatically extracted from data. In this framework, users only need to provide a black-box simulator that generates valid system outputs; at training time, we constrain the model to produce outputs that cannot be distinguished from simulated samples by a learned discriminator. Further providing our framework with a small number of labeled examples gives rise to a new semi-supervised structured prediction method; we evaluate this method on multiple tasks — object tracking and pose estimation, and we find that our framework achieves high accuracy with only a small amount of labels, and no labels at all in some cases.
Emergence of mixed quintet superfluidity in the chain of partially polarized spin-3/2 ultracold atoms

The system of ultracold atoms with hyperfine spin F=3/2 might be unstable against the formation of quintet pairs if the interaction is attractive in the quintet channel. We have investigated the behavior of correlation functions in a model including only s-wave interactions at quarter filling by large-scale density-matrix renormalization-group simulations. We show that the correlations of quintet pairs become quasi-long-ranged when the system is partially polarized, leading to the emergence of various mixed superfluid phases in which BCS-like pairs carrying different magnetic moment coexist.
Using imaging spectroscopy to predict above-ground plant biomass in alpine grasslands grazed by large ungulates

Aims Imaging spectroscopy enables measurement of vegetation optical properties to predict vegetation characteristics that are important for a wide range of ecological applications. Our aim was to predict fresh above-ground biomass of heterogeneous alpine grasslands in two areas and at two ecological scales. We assessed model plausibility for an intensively studied alpine grassland site (plant community scale) having distinct biomass and ungulate grazing patterns. Location Alpine grasslands in the Swiss National Park. Methods Biomass data were collected in 51 plots and combined with imaging spectroscopy data to establish simple ratio models. We analysed the predictive power and transferability of models developed in two areas (Val Trupchun, Il Fuorn) and at two ecological scales (regional, local). In a next step, we compared our results to the broadband normalized difference vegetation index (NDVI). Finally, we assessed the correlations between model predictions and plant biomass distribution at the plant community scale. Results The best local simple ratio models yielded a model fit of R2 = 0.60 and R2 = 0.30, respectively, the best regional model a fit of R2 = 0.44. NDVI model performance was weaker for the regional and one local area, but slightly better for the other local area. However, at the plant community scale only the local model showed a significant positive correlation (RS = 0.39) with the known biomass distribution. Further, predictive power decreased when models were transferred from one local area to another or from one ecological scale to another. Conclusions Our study demonstrated that imaging spectroscopy is generally useful to predict above-ground plant biomass in alpine grasslands with distinct grazing patterns. Site-specific local models based on simple ratio indices performed better than the NDVI or regional models, suggesting that standardized approaches might not be adequate, particularly in heterogeneous grasslands inhabited by large ungulates. We emphasize the importance of collecting ground reference data covering the expected range of productivity and plant species composition. Moreover, plant community-scale data from a previous study proved to be extremely valuable to test model plausibility.
Coyote prey selection and community stability during a decline in food supply

The foraging behavior of predators can have a large influence on community dynamics and has been shown to increase stability in some cases and decrease stability in others. I studied the foraging behavior of coyotes (Canis latrans) in the Alaska Range during the peak and decline of a snowshoe hare (Lepus americanus) population cycle (1999-2002). Coyote diet was compared with prey availability to test for changes in prey selection and to examine the effect of coyote predation on the vertebrate prey community. Coyotes responded to the hare decline by increasing selection for hares and porcupines, whereas selection for voles, ground squirrels and Dall sheep did not change. Although the study area was characterized by considerable habitat heterogeneity, coyotes utilized the area as a fine-grained environment. Coyote foraging behavior was driven primarily by changes in snowshoe hare abundance, and their sensitivity to change in alternative prey density was low. Predation by coyotes may therefore decrease the stability of alternative prey populations rather than dampening fluctuations. In order for predation to enhance the stability of prey populations, I hypothesize that prey profitability must be determined primarily by abundance.
SELF-MICRO EMULSIFYING DRUG DELIVERY SYSTEMS (SMEDDS): A REVIEW ON PHYSICO-CHEMICAL AND BIOPHARMACEUTICAL ASPECTS

Nearly 40% of new drug candidates exhibit low solubility in water, which is a challenge in development of optimum oral solid dosage form in terms of formulation design and bioavailability of new pharmaceutical products. Many strategies have been used to overcome these problems either by means of modifying the solubility or maintaining the drug in dissolved form throughout gastric transit time. Much attention has focused on lipid solutions, emulsions and emulsion pre-concentrates, which can be prepared as physically stable formulations suitable for encapsulation of such poorly soluble drugs. Recently, self-micro emulsifying drug delivery systems (SMEDDS) especially have attracted increasing interest primarily because these are physically stable, easy to manufacture, can be filled in soft gelatin capsules and then will generate a drug containing micro-emulsion with a large surface area upon dispersion in the gastrointestinal tract. The emulsions will further facilitate the absorption of the drug due via intestinal lymphatic pathway and by partitioning of drug into the aqueous phase of intestinal fluids. In the present review, an overview of SMEDDS as a key technology for formulating lipophilic drugs and various factors that potentially affect the oral bioavailability of such drugs are presented. Keywords: Low solubility, Oral bioavailability, Self-micro emulsion, Intestinal lymphatic pathway.
Spectral Clustering based Active Learning with Applications to Text Classification

Active learning is a kind of machine learning algorithms that spontaneously choose data samples from which they will learn. It has been widely used in many data mining fields such as text classification, in which large amounts of unlabelled data samples are available, but labels are hard to get. In this paper, an improved active learning algorithm is proposed, which takes advantages of the distribution feature of the datasets to reduce the labelling cost and increase the accuracy. Before the active learning process, spectral clustering algorithm is applied to divide the datasets into two categories, and instances located at the boundary of two categories are labelled to train the initial classifier. In order to reduce the calculation cost, an incremental method is added in the present algorithm. The algorithm is applied to several text classification problems. The results show it is more effective and more accurate than the traditional active learning algorithm.
Towards Net-based Formal Methods for Complex Event Processing

Complex event processing (CEP) systems evaluate continuous queries over event streams in order to detect event patterns that characterise a situation of interest. Over the last decade, models and systems for CEP have been an active area of research. A plethora of languages for the definition of queries as well as algorithms and architectures for their efficient and robust evaluation have been proposed. However, we argue that much of this work adopted a pragmatic view, striving for technical solutions of problems that are rooted directly in specific applications. As such, formal methods that help in the design and implementation of CEP systems are underdeveloped. In this paper, we put forward the idea of exploiting well-established formalisms for concurrent systems in the analysis of CEP systems. We discuss query verification, query parallelisation, and out-of-order event handling as three exemplary use cases for formal methods for CEP. Moreover, we outline our initial results on addressing these use cases through a Petri-net-based formalisation of event queries, event streams, and evaluation architectures.
Leisure Time and Technology

Generally speaking, the amount of available leisure time continually increased from the mid-19th century onward. This development culminated in Western Europe in the 1980s in the so-called leisure society, a phenomenon that is recognised today as being of both social and economic importance. The available leisure time presents a framework for a very wide scope of individual leisure activities that often reflect contemporary changes. The hallmark of most recreational activities, at least since industrialisation, seems to be the interpenetration of leisure and rapidly evolving technology. As this article demonstrates, technology is linked with the parameters of leisure and with the recreational activities themselves.
J-DSP-C, a control systems simulation environement: labs and assessment

This paper presents various on-line computer laboratory exercises and related assessment results for Java-Digital Signal-Processing-Controls (J-DSP-C). J-DSP- C is an object oriented simulation environment that is intended for control systems simulations. All J-DSP-C component functions appear as graphical blocks that can be connected to form a variety of educational controls simulations. Dynamical systems with various interconnection topologies can be developed and simulated while results can be examined graphically. J-DSP-C has been designed to be easily accessible through the internet with any computer system equipped with a Java -enabled browser.
A graph-based method for playlist generation

The advance of online music libraries has increased the importance of recommendation systems. The task of automatic playlist generation naturally arises as an interesting approach to this problem. Most of existing applications use some similarity criterion between the songs or are based on manual user interaction. In this work, we propose a novel algorithm for automatic playlist generation based on paths in Minimum Spanning Trees (MST’s) of music networks. A motivation is to incorporate the relationship between music genres and expression of emotions by capturing the presence of temporal rhythmic patterns. One of the major advantages of the proposed method is the use of edge weights in the searching process (maximizing the similarity between subsequent songs), while Breadth-First (BF) and Depth-First (DF) search algorithms assume the hypothesis that all the songs are equidistant.
A holistic perspective on the theoretical foundations for ICT4D research

ABSTRACT While many theories have guided research Information and Communication Technologies for Development (ICT4D), we are yet to construct a clear and coherent narrative that would help us answer the question of how ICT fosters development in underdeveloped communities. In this paper, we argue that one of the main reasons for this is that our holistic understanding of ICT4D is seldom grounded in theories to understand the core areas that define the field, namely, ICT, Development, and, ‘4’ which are the transformative processes that link the two. Through a brief literature review, we list theories that have informed ICT4D research in each of these areas. We present examples of theories, namely, Capability Approach, Affordances, and Actor-Network Theory together with Social Capital and illustrate how we have used them in our research. Building on this holistic perspective on theoretical foundation, we propose five agendas for ICT4D research.
From Invisibility to Transparency: Identifying the Implications

This paper explores the need for a broader and more inclusive approach to decisions about land and resources, one that recognizes the legitimacy of cultural values and traditional knowledge in environmental decision making and policy. Invisible losses are those not widely recognized or accounted for in decisions about resource planning and decision making in resource- and land-use negotiations precisely because they involve considerations that tend to be ignored by managers and scientists or because they are often indirect or cumulative, resulting from a complex, often cumulative series of events, decisions, choices, or policies. First Nations communities in western North America have experienced many such losses that, together, have resulted in a decline in the overall resilience of individuals and communities. We have identified eight types invisible losses that are often overlapping and cumulative: cultural/lifestyle losses, loss of identity, health losses, loss of self-determination and influence, emotional and psychological losses, loss of order in the world, knowledge losses, and indirect economic losses and lost opportunities. To render such invisible losses more transparent, which represents the first step in developing a more positive and equitable basis for decision making and negotiations around land and resources, we recommend six processes: focusing on what matters to the people affected, describing what matters in meaningful ways, making a place for these concerns in decision making, evaluating future losses and gains from a historical baseline, recognizing culturally derived values as relevant, and creating better alternatives for decision making so that invisible losses will be diminished or eliminated in the future.
A NEW SPATIAL AND TEMPORAL FUSION MODEL

Abstract. As Moderate Resolution Imaging Spectroradiometer (MODIS) and Enhanced Thematic Mapper Plus (ETM+) has a tradeoff between the high temporal resolution and high spatial resolution, this paper proposed a spatial and temporal model with auto-regression error correction (AREC) method to blend the two types of images in order to obtain the composed image with both high spatial and temporal resolution. Experiments and validation were conducted on a data set located in Shenzhen, China and compared with Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) in several objective indexes and visual analysis. It was found that AREC could effectively predict the land cover changes and the fusion results had better performances versus the ones of STARFM.
Understanding eyewitness reports on Twitter during disasters

Social media platforms such as Twitter provide convenient ways to share and consume important information during disasters and emergencies. Information from bystanders and eyewitnesses can be useful for law enforcement agencies and humanitarian organizations to get firsthand and credible information about an ongoing situation to gain situational awareness among other uses. However, identification of eyewitness reports on Twitter is challenging for many reasons. This work investigates the sources of tweets and classifies them into three types (i) direct eyewitnesses, (ii) indirect eyewitness, and (iii) vulnerable accounts. Moreover, we investigate various characteristics associated with each kind of eyewitness account. We observe that words related to perceptual senses (feeling, seeing, hearing) tend to be present in direct eyewitness messages, whereas emotions, thoughts, and prayers are more common in indirect witnesses. We believe these characteristics can help make more efficient computational methods and systems in the future for automatic identification of eyewitness accounts.
Role of Microbes in Food and Industrial Microbiology

Virgin means that the oil was created by merely pressing olives. It did not endure any of the economic processes accustomed build refined oils like canola, sunflower, soybean and also the lower grades of oil labeled Pure, Light, and easily Olive Oil. Virgin olive oils that haven’t any style defects and pass strict tests in terms of chemistry will be labeled Extra Virgin. Virgin olive oils that have modest style defects and meets somewhat less strict chemical parameters square measure labeled Virgin. Sadly, you do not see Virgin oils purchasable as a result of too usually producer’s market Virgin oils as Extra Virgin to command higher costs. Till standards social control catches up with apply, real Extra Virgin is arduous to return.
Love Styles, Self-Monitoring, and Relational Message Interpretation: What's Being in Love Got To Do with It?.

ABSTRACT A study tested the notion that falling in love causes changes in love styles, relational message interpretations, and personality characteristics such as self-monitoring. Subjects, 209 undergraduate communication students, completed measures of love styles, self-monitoring, and relational message interpretations. In addition, participants indicated whether or not they were in love. Participation for students partially fulfilled a research requirement for either a public speaking or interpersonal communication class. If falling in love causes drastic changes in personality, relational message interpretations, and love styles, partial correlations between these variables should be zero when being in love is controlled for. Results indicated that partial correlations were slightly smaller than their zero-order counterparts. Pairs of variables with significant zero-order correlations, however, tended to also have significant partial correlations. Findings suggest that being in love does not totally account for the covariation of self-monitoring, love styles, and relational message interpretations. (Contains 23 references and 6 tables of data.) (RS)
Computer game associating self-concept to images of acceptance can reduce adolescents' aggressiveness in response to social rejection

The experience of social rejection can lead to an aggressive response. However, the ability to maintain a sense of social connection may reduce the likelihood of this type of response. We tested a computer-based intervention designed to use simple learning principles to boost the sense of social connection and acceptance. Adolescents aged 9–15 (n=138) first completed a conditioning game on computer that repeatedly paired their own name with images of social acceptance (versus a control condition with no systematic pairing), and subsequently reported how aggressively they would behave in response to being rejected by a peer. Those completing the self-acceptance conditioning (particularly those low in self-esteem) reported less aggressive feelings and intentions.