Miracle-dz/UniFAR-FTUs huggingface dataset:
Points-to Analysis for Java Using Annotated Inclusion Constraints

1 Abstract The goal of points-to analysis for Java is to determine the set of objects pointed to by a reference variable or a reference object ﬁeld. In this paper we deﬁne and evaluate a points-to analysis for Java which extends Andersen’s points-to analysis for C [4]. Andersen’s analysis for C can be implemented eﬃciently by using systems of set-inclusion constraints and by employing several techniques for constraint representation and resolution. We extend these techniques to eﬃciently represent and solve systems of annotated inclusion constraints. The annotations play two roles in our analysis. Method annotations are used to model precisely and eﬃciently the semantics of virtual calls. Field annotations allow us to distinguish the ﬂow of values through diﬀerent ﬁelds of an object. In addition, our analysis keeps track of all methods reachable from the entry point of the program, and avoids analyzing dead library code. We evaluate the performance of the analysis on a large set of realistic Java programs. Our results show that the analysis is practical and therefore will be useful as a relatively precise general-purpose points-to analysis for Java. The experiments also show that the points-to solution has signiﬁcant impact on call graph construction, virtual call resolution, elimination of unnecessary synchronization, and stack-based object allocation.
DEVELOPMENT OF AVERAGE MODEL FOR CONTROL OF A FULL BRIDGE PWM DC-DC CONVERTER

paper presents a detailed small-signal and transient analysis of a full bridge PWM DC-DC converter designed for high voltage, high power applications using an average model. The derived model is implemented in a typical system and used to produce the small-signal and transient characteristics of the converter. Results obtained in the analysis of the high voltage and high power design example is validated by comparison for actual system and derived model. The derived small signal model is used to design a controller to regulate output voltage of the converter under several disturbances. A PD controller with combination of a feedforward input voltage is designed so that the output voltage is equal to desired voltage and the time response is very short under load and input voltage disturbances.
Audience Expansion for Online Social Network Advertising

Online social network advertising platforms, such as that provided by LinkedIn, generally allow marketers to specify targeting options so that their ads appear to a desired demographic. Audience Expansion is a technique developed at LinkedIn to simplify targeting and identify new audiences with similar attributes to the original target audience. We developed two methods to achieve Audience Expansion: campaign-agnostic expansion and campaign-aware expansion. In this paper, we describe the details of these methods, present in-depth analysis of their trade-offs, and demonstrate a hybrid strategy that possesses the combined strength of both methods. Through large scale online experiments, we show the effectiveness of the proposed approach, and as a result, the benefits it brings to the whole marketplace including both LinkedIn and advertisers. The achieved benefits can be characterized as: 1) simplified targeting process and increased reach for advertisers, and 2) better utilization of LinkedIn's ads inventory and higher and more efficient market participation.
Work behaviors in cation driven conducting polymer soft actuators

Work behaviours of soft actuators based on conducting polymer, polypyrrole (PPy) were studied as the function of tensile loads. The PPy actuator was operated in aqueous electrolyte of NaCl showing contraction and expansion by oxidation and reduction, respectively. The actuator lifted suspended load weights during contraction. It was found that the contractive strain of 1% was kept almost constant with increasing tensile loads up to 5 MPa or until the breakdown. The contractive stroke increased under heavy loads, and was discussed taking the uniaxial deformation and training effect into consideration. The conversion energy efficiency was estimated to be 0.05%.
Logical Characterization of Petri Net !-languages

In this paper, we study some classes of Petri net deenable ! -languages. We consider several types of accepting conditions on Petri nets, corresponding to those considered on nite automata in the theory of ! -regular languages. Then, we establish a neat correspondence between the classes of Petri net deenable ! -languages and the classes of nite automata deenable ! -languages. Moreover, each class of Petri net deenable ! -languages is characterized by logical formulas.
Optimization and Validation of RP-HPLC-UV/Vis Method for Determination Phenolic Compounds in Several Personal Care Products

An HPLC method with ultraviolet-visible spectrophotometry detection has been optimized and validated for the simultaneous determination of phenolic compounds, such as butylated hydroxyanisole (BHA) and butylated hydroxytoluene (BHT) as antioxidants, and octyl methyl cinnamate (OMC) as UVB-filter in several personal care products. The dynamic range was between 1 to 250 mg/L with relative standard deviation less than 0.25% (n = 4). Limits of detection for BHA, BHT, and OMC were 0.196, 0.170, and 0.478 mg/L, respectively. While limits of quantification for BHA, BHT, and OMC were 0.593, 0.515, and 1.448 mg/L, respectively. The recovery for BHA, BHT, and OMC was ranged from 92.1–105.9%, 83.2–108.9%, and 87.3–103.7%, respectively. The concentration ranges of BHA, BHT, and OMC in 12 commercial personal care samples were 0.13–4.85, 0.16–2.30, and 0.12–65.5 mg/g, respectively. The concentrations of phenolic compounds in these personal care samples were below than maximum allowable concentration in personal care formulation, that is, 0.0004–10 mg/g, 0.002–5 mg/g, and up to 100 mg/g for BHA, BHT, and OMC, respectively.
Effects of Citicoline on Phospholipid and Glutathione Levels in Transient Cerebral Ischemia

Background and Purpose— Cytidine-5′-diphosphocholine (citicoline or CDP-choline) is an essential intermediate in the biosynthesis of phosphatidylcholine, an important component of the neural cell membrane. Citicoline provided significant neuroprotection after transient forebrain ischemia in gerbils. This study was undertaken to examine changes and effects of citicoline on phospholipids and glutathione synthesis after transient cerebral ischemia and reperfusion. Methods— Ten-minute transient forebrain ischemia was induced by bilateral carotid artery occlusion in male Mongolian gerbils with reperfusion up to 6 days. Citicoline (500 mg/kg IP in saline) was given to gerbils just after the end of ischemia, at 3-hour reperfusion, and daily thereafter until 1 day before euthanasia. Hippocampal lipids were extracted and analyzed by thin-layer and gas chromatography. Glutathione was measured by using an enzymatic recycling assay. Glutathione reductase activity was determined by measuring NADPH oxidation. Results— Significant decreases in phospholipids occurred at 1-day reperfusion. Citicoline significantly restored the phosphatidylcholine, sphingomyelin, and cardiolipin levels but did not affect phosphatidylinositol and phosphatidylserine at 1 day. The phospholipids returned to sham levels over days 2 to 6 and were unaffected by citicoline. Ceramide levels significantly increased by 3 and 6 days of reperfusion and were unaltered by citicoline. Ischemia resulted in significant decreases in glutathione and glutathione reductase activity over 3 days of reperfusion. Citicoline significantly increased total glutathione and glutathione reductase activity and decreased the glutathione oxidation ratio, an indicator of glutathione redox status. Conclusions— Our data indicated that the effects of citicoline on phospholipids occurred primarily during the first day of reperfusion, with effects on glutathione being important over the 3-day reperfusion period.
Modelling the Dynamics of Post-Vaccination Immunity Rate in a Population of Sahelian Sheep after a Vaccination Campaign against Peste des Petits Ruminants Virus

Background Peste des petits ruminants (PPR) is an acute infectious viral disease affecting domestic small ruminants (sheep and goats) and some wild ruminant species in Africa, the Middle East and Asia. A global PPR control strategy based on mass vaccination—in regions where PPR is endemic—was recently designed and launched by international organizations. Sahelian Africa is one of the most challenging endemic regions for PPR control. Indeed, strong seasonal and annual variations in mating, mortality and offtake rates result in a complex population dynamics which might in turn alter the population post-vaccination immunity rate (PIR), and thus be important to consider for the implementation of vaccination campaigns. Methods In a context of preventive vaccination in epidemiological units without PPR virus transmission, we developed a predictive, dynamic model based on a seasonal matrix population model to simulate PIR dynamics. This model was mostly calibrated with demographic and epidemiological parameters estimated from a long-term follow-up survey of small ruminant herds. We used it to simulate the PIR dynamics following a single PPR vaccination campaign in a Sahelian sheep population, and to assess the effects of (i) changes in offtake rate related to the Tabaski (a Muslim feast following the lunar calendar), and (ii) the date of implementation of the vaccination campaigns. Results The persistence of PIR was not influenced by the Tabaski date. Decreasing the vaccination coverage from 100 to 80% had limited effects on PIR. However, lower vaccination coverage did not provide sufficient immunity rates (PIR < 70%). As a trade-off between model predictions and other considerations like animal physiological status, and suitability for livestock farmers, we would suggest to implement vaccination campaigns in September-October. This model is a first step towards better decision support for animal health authorities. It might be adapted to other species, livestock farming systems or diseases.
High-Resolution Infrared Imaging of the Compact Nuclear Source in NGC 4258

We present high-resolution imaging of the nucleus of NGC 4258 from 1 to 18 μm. Our observations reveal that the previously discovered compact source of emission is unresolved even at the near-infrared resolution of ~0. ″2 FWHM, which corresponds to about 7 pc at the distance of the galaxy. This is consistent with the source of emission being the region in the neighborhood of the purported 3.5 × 107 M☉ black hole. After correcting for about 18 mag of visual extinction, the infrared data are consistent with an Fν ∝ ν-1.4±0.1 spectrum from 1.1 to 18 μm, implying a nonthermal origin. Based on this spectrum, the total extinction-corrected infrared luminosity (1-20 μm) of the central source is 2 × 108 L☉. We argue that the infrared spectrum and luminosity of the central source obviates the need for a substantial contribution from a standard, thin accretion disk at these wavelengths and calculate the accretion rate through an advection-dominated accretion flow to be ~ 10-3 M☉ yr -1. The agreement between these observations and the theoretical spectral energy distribution for advection-dominated flows provides evidence for the existence of an advection-dominated flow in this low-luminosity active galactic nucleus.
Combining semantic and acoustic features for valence and arousal recognition in speech

The recognition of affect in speech has attracted a lot of interest recently; especially in the area of cognitive and computer sciences. Most of the previous studies focused on the recognition of basic emotions (such as happiness, sadness and anger) using categorical approach. Recently, the focus has been shifting towards dimensional affect recognition based on the idea that emotional states are not independent from one another but related in a systematic manner. In this paper, we design a continuous dimensional speech affect recognition model that combines acoustic and semantic features. We design our own corpus that consists of 59 short movie clips with audio and text in subtitle format, rated by human subjects in arousal and valence (A-V) dimensions. For the acoustic part, we combine many features and use correlation based feature selection and apply support vector regression. For the semantic part, we use the affective norms for English words (ANEW), that are rated also in A-V dimensions, as keywords and apply latent semantics analysis (LSA) on those words and words in the clips to estimate A-V values in the clips. Finally, the results of acoustic and semantic parts are combined. We show that combining semantic and acoustic information for dimensional speech recognition improves the results. Moreover, we show that valence is better estimated using semantic features while arousal is better estimated using acoustic features.
ISC–Intelligent Subspace Clustering, A Density Based Clustering Approach for High Dimensional Dataset

Many real-world data sets consist of a very high dimensional feature space. Most clustering techniques use the distance or similarity between objects as a measure to build clusters. But in high dimensional spaces, distances between points become relatively uniform. In such cases, density based approaches may give better results. Subspace Clustering algorithms automatically identify lower dimensional subspaces of the higher dimensional feature space in which clusters exist. In this paper, we propose a new clustering algorithm, ISC – Intelligent Subspace Clustering, which tries to overcome three major limitations of the existing state-of-art techniques. ISC determines the input parameter such as є – distance at various levels of Subspace Clustering which helps in finding meaningful clusters. The uniform parameters approach is not suitable for different kind of databases. ISC implements dynamic and adaptive determination of Meaningful clustering parameters based on hierarchical filtering approach. Third and most important feature of ISC is the ability of incremental learning and dynamic inclusion and exclusions of subspaces which lead to better cluster formation. Keywords—Density based Clustering, High Dimensional Data, Subspace Clustering, Dynamic Parameter Setting.
Lessons Learned When Building a Greenfield High Performance Computing Ecosystem

Faced with a fragmented research computing environment and growing needs for high performance computing resources, Michigan State University established the High Performance Computing Center in 2005 to serve as a central high performance computing resource for MSU's research community. Like greenfield industrial development, the center was unconstrained by existing infrastructure. The lessons learned are useful when building or maintaining an effective HPC resource and may provide insight for developing other computational services.
Jumping to explanations vs . Jumping to conclusions

Abduction is usually defined as the process of inferring the best explanation of an observation. There are many information processing operations that can be viewed as a search for an explanation. For instance, diagnosis, natural language interpretation and plan recognition. This paper is concerned about the following aspects of abduction: (i) what are the logical properties of abduction when it is regarded as a form of inference? and (ii) how close is abduction to reversed deduction? In the logic-based approach to abduction, the background theory is given by a consistent set of formulas Σ. The notion of an explanation is defined by saying that a formula γ (consistent with Σ) is an explanation of α if Σ ∪ {γ} ` α. An explanatory relation is a binary relation ¤ among formulas where the intended meaning of α¤γ is “γ is a preferred explanation of α”. To each explanatory relation is associated a consequence relation |∼ab defined as follows: α |∼ab β if Σ ∪ {γ} ` β for each γ such that α ¤ γ. The study of the logical properties of explanatory reasoning is approached by a systematic analysis of |∼ab. We show that there are rationality postulates for abduction (i.e. constrains on the explanatory relation ¤ ) that are, in a very precise sense, equivalent to rationality postulates (in the Krauss-Lehmann-Magidor tradition) for non-monotonic reasoning (i.e. for the relation |∼ab). This tight correspondence between postulates for explanatory reasoning and non-monotonic reasoning will make apparent a strong duality between these two forms of inference. Isolating the postulates and showing this duality are one of the main contributions of the paper. We introduce the notion of a causal explanatory relation and show its close connection with reversed non-monotonic reasoning.
Selective Introduction of Aspects for Program Comprehension

We propose a technique that uses webmining principles on event traces for uncovering important classes in a system’s architecture. These classes can form starting points for the program comprehension process. Furthermore, we argue that these important classes can be used to define pointcuts for the introduction of aspects. Based on a medium-scale case study – Apache Ant – and detailed architectural information from its developers, we show that the important classes found by our technique are prime candidates for the introduction of aspects.
An Ant-based Clustering System for Knowledge Discovery in DNA Chip Analysis Data

Biological data has several characteristics that strongly differentiate it from typical business data. It is much more complex, usually large in size, and continuously changes. Until recently business data has been the main target for discovering trends, patterns or future expectations. However, with the recent rise in biotechnology, the powerful technology that was used for analyzing business data is now being applied to biological data. With the advanced technology at hand, the main trend in biological research is rapidly changing from structural DNA analysis to understanding cellular functions of the DNA sequences. DNA chips are now being used to perform experiments and DNA analysis processes are being used by researchers. Clustering is one of the important processes used for grouping together similar entities. There are many clustering algorithms such as hierarchical clustering, self-organizing maps, K-means clustering and so on. In this paper, we propose a clustering algorithm that imitates the ecosystem taking into account the features of biological data. We implemented the system using an Ant-Colony clustering algorithm. The system decides the number of clusters automatically. The system processes the input biological data, runs the Ant-Colony algorithm, draws the Topic Map, assigns clusters to the genes and displays the output. We tested the algorithm with a test data of 100 to1000 genes and 24 samples and show promising results for applying this algorithm to clustering DNA chip data. Keywords—Ant Colony System, Biological data, Clustering, DNA chip.
Denser than diamond: Ab initio search for superdense carbon allotropes

A). Searching for possible superdense carbon allotropes, we have found three structures (hP3, tI12, and tP12) that have significantly greater density. The hP3 and tP12 phases have strong analogy with two polymorphs of silica (β-quartz and keatite), while the tI12 phase is related to the high-pressure SiS2 polymorph. Furthermore, we found a collection of other superdense structures based on the motifs of the aforementioned structures, but with different ways of packing carbon tetrahedra, and among these the hP3 and tI12 structures are the densest. At ambient conditions, the hP3 phase is a semiconductor with the GW band gap of 3.0 eV, tI12 is an insulator with the band gap of 5.5 eV, while tP12 is an insulator, the band gap of which is remarkably high (7.3 eV), making it the widest-gap carbon allotrope. These allotropes are metastable and have comparable to diamond or slightly higher bulk moduli; their Vickers hardnesses are calculated to be 87.6 GPa for hP3, 87.2 GPa for tI12, and 88.3 GPa for tP12, respectively, thus making these allotropes nearly as hard as diamond (for which the same model gives the hardness of 94.3 GPa). Superdense carbon allotropes are predicted to have remarkably high refractive indices and strong dispersion of light.
Rebound spiking properties of mouse medial entorhinal cortex neurons in vivo

The medial entorhinal cortex is the gateway between the cortex and hippocampus, and plays a critical role in spatial coding as represented by grid cell activity. In the medial entorhinal cortex, inhibitory circuits are robust, and the presence of the h‐current leads to rebound potentials and rebound spiking in in vitro experiments. It has been hypothesized that these properties, combined with network oscillations, may contribute to grid cell formation. To examine the properties of in vivo rebound spikes, we performed whole‐cell patch‐clamp recordings in medial entorhinal cortex neurons in anaesthetized mice. We injected hyperpolarizing inputs representing inhibitory synaptic inputs along with sinusoidal oscillations and found that hyperpolarizing inputs injected at specific phases of oscillation had a higher probability of inducing subsequent spikes at the peak of the oscillation in some neurons. This effect was prominent in the cells with large sag potential, which is a marker of the h‐current. In addition, larger and longer hyperpolarizing current square‐pulse stimulation resulted in a larger probability of eliciting rebound spikes, though we did not observe a relationship between the amplitude or duration of hyperpolarizing current pulse stimulation and the delay of rebound spikes. Overall these results suggest that rebound spikes are observed in vivo and may play a role in generating grid cell activity in medial entorhinal cortex neurons.
Comparison between Single Mesh Network and Cell Based Mesh Network

Industrial wireless applications have stringent requirements of safety, data update rate, time delay, reliability, failure tolerance, and security. In this paper, a concept of cell-based mesh networks is proposed, and the performance is evaluated by computer simulations. The results show that the average number of hops from sensor nodes to the backbone routers and the average route cost are significantly reduced with a comparison to a conventional mesh topology in multipath fading environments. It is also shown that the cell-based mesh networks can tolerate failures of links, notes and backbone routers. All those imply that the cell-based mesh networks are suitable for industrial wireless sensor networks with stringent requirements of data update rate, time delay, and reliability and failure tolerance.
Vision-based measurement of weld pool geometry in constant-current gas tungsten arc welding

Abstract The common commercial charge coupled device (CCD) camera is combined with a composite light filter to form a vision sensing system of low cost. It can capture the whole image of a weld pool clearly during constant-current gas tungsten arc welding (GTAW). Based on the image-processing algorithm, the edges of the weld pool under different welding conditions can be extracted. Calibration is made to obtain the weld pool geometry in real size. The measured results are useful for developing a welding process control system and verifying the mathematical models of weld pool behaviours.