Miracle-dz/UniFAR-FTUs huggingface dataset:
Getting From "Know-What" To "Know-How" Via Online Communities: A Conversational Analysis Of An Openoffice.Org Language Project

Information systems (IS) innovation researchers explain that in the early phases of IS innovation, organizations must acquire knowledge about, “what, “when”, “why” and “how” to innovate with information technologies (IT). To acquire this knowledge, innovators with IT may access business and technology publications or the more costly option of consulting services. However, widespread use of online communities has created a more cost effective way of learning about IT innovations and is now being used by organizations. Using online communities, organizations can circumvent logistical constraints to take advantage of networks of human capital. In this study we analysed collaborations in the online community OpenOffice.org. The study showed that successful knowledge sharing in online communities where interaction is temporal and focused requires context specific knowledge gaps. The analysis revealed patterns in the communication that exposed a taken-for-granted knowledge sharing mechanism we conceptualise as “gap-filling”. Within this mechanism “what I know” and “what I don’t know” statements provided a mechanism for identify context based knowledge gaps that enable participants to go from “know-what” to “know-how”.
Attenuation of Delayed Neuronal Death after Mild Focal Ischemia in Mice by Inhibition of the Caspase Family

Inhibitors of apoptosis and of excitotoxic cell death reduce brain damage after transient and permanent middle cerebral artery occlusion. We compared the neuroprotective effects of two caspase family inhibitors with the N-methyl-d-aspartate receptor antagonist (+)-MK-801 hydrogen maleate (MK-801) in a newly characterized cycloheximidesensitive murine model of transient middle cerebral artery occlusion (30 minutes) in which apoptotic cell death is prominent. Ischemic infarction, undetected by 2,3,5-triphenyltetrazolium chloride staining at 24-hour reperfusion, featured prominently in the striatum at 72 hours and 7 days on hematoxylin-eosin—stained sections. Markers of apoptosis, such as oligonucleosomal DNA damage (laddering) and terminal deoxynucleotidyl transferase—mediated dUTP-biotin nick-end labeling (TUNEL)–positive cells first appeared at 24 hours and increased significantly at 72 hours and 7 days after reperfusion. The TUNEL-labeled cells were mostly neurons and stained negative for glial (GFAP, glial fibrillary acid protein) and leukocyte specific markers (CD-45). The caspase inhibitors, N-benzyloxycarbonyl-Val-Ala-Asp-fluoromethyl ketone (z-VAD.FMK; 120 ng intracerebroventricularly) or N-benzyloxycarbonyl-Asp-Glu-Val-Asp-fluoromethyl ketone (z-DEVD.FMK; 480 ng intracerebroventricularly) decreased infarct size and neurologic deficits when administered 6 hours after reperfusion. The extent of protection was greater than in models of more prolonged ischemia or after permanent occlusion, and the therapeutic window was extended from 0 to 1 hours after 2-hour middle cerebral artery occlusion to at least 6 hours after brief ischemia. Also, z-VAD.FMK and z-DEVD.FMK treatment decreased oligonucleosomal DNA damage (DNA laddering) as assessed by quantitative autoradiography after gel electrophoresis. By contrast, MK-801 protected brain tissue only when given before ischemia (3 mg/kg intraperitoneally), but not at 3 or 6 hours after reperfusion. Despite a decrease in infarct size after MK-801 pretreatment, the amount of DNA laddering did not decrease 72 hours after reperfusion, thereby suggesting a mechanism distinct from inhibition of apoptosis. Hence, 30 minutes of reversible ischemia augments apoptotic cell death, which can be attenuated by delayed z-VADPMK and z-DEVD.FMK administration with preservation of neurologic function. By contrast, the therapeutic window for MK-801 does not extend beyond the time of occlusion, probably because its primary mechanism of action does not block the development of apoptotic cell death.
Dissociating Object Directed and Non-Object Directed Action in the Human Mirror System; Implications for Theories of Motor Simulation

Mirror neurons are single cells found in macaque premotor and parietal cortices that are active during action execution and observation. In non-human primates, mirror neurons have only been found in relation to object-directed movements or communicative gestures, as non-object directed actions of the upper limb are not well characterized in non-human primates. Mirror neurons provide important evidence for motor simulation theories of cognition, sometimes referred to as the direct matching hypothesis, which propose that observed actions are mapped onto associated motor schemata in a direct and automatic manner. This study, for the first time, directly compares mirror responses, defined as the overlap between action execution and observation, during object directed and meaningless non-object directed actions. We present functional MRI data that demonstrate a clear dissociation between object directed and non-object directed actions within the human mirror system. A premotor and parietal network was preferentially active during object directed actions, whether observed or executed. Moreover, we report spatially correlated activity across multiple voxels for observation and execution of an object directed action. In contrast to predictions made by motor simulation theory, no similar activity was observed for non-object directed actions. These data demonstrate that object directed and meaningless non-object directed actions are subserved by different neuronal networks and that the human mirror response is significantly greater for object directed actions. These data have important implications for understanding the human mirror system and for simulation theories of motor cognition. Subsequent theories of motor simulation must account for these differences, possibly by acknowledging the role of experience in modulating the mirror response.
A Particle Swarm Optimization Algorithm with Local Sparse Representation for Visual Tracking

Handling appearance variations caused by the occlusion or abrupt motion is a challenging task for visual tracking. In this paper, we propose a novel tracking method that deals with the appearance changes based on sparse representation in a particle swarm optimization (PSO) framework. First, we divide each candidate state into multiple structural patches to cope with the partial occlusions of the object. Once the object is lost, we present an object’s recovery scheme by the scale invariant feature transforms (SIFT) correspondence between two frames to reacquire the rough object position. Then the tracking state is searched in the vicinage of the rough object position using the PSO iteration. In addition, an online dictionary updating mechanism is presented to capture the object appearance variations. The object information from the initial frame is never updated in the tracking, while other templates in the dictionary are progressively updated based on the coefficients of templates. Compared with several conventional trackers, the experimental results demonstrate that our approach is more robust in dealing with the occlusions and abrupt motion variations.
Key Concept Identification for Medical Information Retrieval

The difficult language in Electronic Health Records (EHRs) presents a challenge to patients’ understanding of their own conditions. One approach to lowering the barrier is to provide tailored patient education based on their own EHR notes. We are developing a system to retrieve EHR note-tailored online consumer oriented health education materials. We explored topic model and key concept identification methods to construct queries from the EHR notes. Our experiments show that queries using identified key concepts with pseudo-relevance feedback significantly outperform (over 10-fold improvement) the baseline system of using the full text note.
PHY and MAC layer modeling of LTE and WiFi RATs

We consider LTE and WiFi networks in order to model both PHY and MAC layers. Our physical layer abstraction consists a mapping between user’s SINR and their corresponding rate. On the other hand, we will propose a proper queue model to represent the schedulers of each radio access technique (RAT), at the MAC layer.
Drawing dominant dataset from big sensory data in wireless sensor networks

The amount of sensory data manifests an explosive growth due to the increasing popularity of Wireless Sensor Networks. The scale of the sensory data in many applications has already exceeds several petabytes annually, which is beyond the computation and transmission capabilities of the conventional WSNs. On the other hand, the information carried by big sensory data has high redundancy because of strong correlation among sensory data. In this paper, we define the concept of e-dominant dataset, which is only a small data set and can represent the vast information carried by big sensory data with the information loss rate being less than e, where e can be arbitrarily small. We prove that drawing the minimum e-dominant dataset is polynomial time solvable and provide a centralized algorithm with 0(n3) time complexity. Furthermore, a distributed algorithm with constant complexity (O(l)) is also designed. It is shown that the result returned by the distributed algorithm can satisfy the e requirement with a near optimal size. Finally, the extensive real experiment results and simulation results are carried out. The results indicate that all the proposed algorithms have high performance in terms of accuracy and energy efficiency.
Costs and Limits of Phenotypic Plasticity in Island Populations of the Common Frog Rana temporaria Under Divergent Selection Pressures

Costs and limits are assumed to be the major constraints on the evolution of phenotypic plasticity. However, despite their expected importance, they have been surprisingly hard to find in natural populations. It has therefore been argued that natural selection might have removed high-cost genotypes in all populations. However, if costs of plasticity are linked to the degree of plasticity expressed, then high costs of plasticity would only be present in populations where increased plasticity is under selection. We tested this hypothesis by investigating costs and limits of adaptive phenotypic plasticity in development time in a common garden study of island populations of the common frog Rana temporaria, which have varying levels of development time and phenotypic plasticity. Costs of plasticity were only found in populations with high-plastic genotypes, whereas the populations with the most canalized genotypes instead had a cost of canalization. Moreover, individuals displaying the most extreme phenotypes also were the most plastic ones, which mean we found no limits of plasticity. This suggests that costs of plasticity increase with increased level of plasticity in the populations, and therefore costs of plasticity might be more commonly found in high-plastic populations.
Stemming Implicit Bias in High-Tech STEM: The Vision of Project Include

Diversity and inclusion are topics about which we hear a great deal, especially in science, technology, engineering, and math (STEM) fields. These topics are not only of great interest but also of great concern as we consider the ability of the United States to improve educational opportunities for more Americans and remain competitive in the global economy. Project Include (PI) is a diversity and inclusion initiative spearheaded by a group of women in high-tech professions to support start-up companies who wish to build inclusive organizations. I conducted a qualitative content analysis of the PI Web site and found that the vision of PI suggests useful means by which tech organizations can avoid common missteps that undermine their ability to operate inclusively. I found evidence of three themes: interrogating assumptions about human resources (HR) and hiring, defining true diversity, and building diversity and inclusivity. I discuss each theme and conclude with offering a 10-question self-evaluation that tech and other organizations can use or adapt to critically evaluate HR and operational practices to better align with diversity and inclusion goals.
Learn on the Fly: Quiescent Routing in Sensor Network Backbones

In the context of IEEE 802.11b network testbeds, we examine the differences between unicast and broadcast link properties, and we show the inherent dif(cid:2)culties in precisely estimating uni-cast link properties via those of broadcast beacons even if we make the length and transmission rate of beacons be the same as those of data packets. To circumvent the dif(cid:2)culties in link estimation, we propose to estimate unicast link properties directly via data traf(cid:2)c itself without using beacons. To this end, we de-sign a beacon-free routing protocol Learn on the Fly (LOF). LOF estimates link quality based solely on data traf(cid:2)c, and it chooses routes by way of a locally measurable metric ELD, the expected MAC latency per unit-distance to the destination . Using a realistic sensor network traf(cid:2)c trace and an 802.11b testbed of 195 Stargates, we experimentally compare the performance of LOF with that of existing protocols, represented by the geography-unaware ETX and the geography-based PRD. We (cid:2)nd that LOF reduces end-to-end MAC latency by a factor of 3, enhances energy ef(cid:2)ciency by a factor up to 2.37, and improves route stability by 2 orders of magnitude. The results demonstrate the feasibility as well as potential bene(cid:2)ts of data-driven beacon-free link estimation and routing. (cid:0)(cid:2)(cid:1)(cid:4)(cid:3)(cid:6)(cid:5)(cid:8)(cid:7)(cid:10)(cid:9)(cid:12)(cid:11)(cid:6)(cid:13) (cid:151)experiment-based
BSS for improved interference estimation for Blind speech signal Extraction with two microphones

Blind Source Extraction (BSE) as desirable for acoustic cocktail party scenarios requires estimates for the target or interfering signals. Conventional single-channel approaches for obtaining the interference estimate rely on noise and interference estimates during absence of the target signal. For multichannel approaches using multiple microphone signals, a separation of simultaneously active target and interference signals becomes possible if the positions of the target and interfering sources are known. We propose a new method which exploits Directional BSS (Blind Source Separation with a geometric constraint) to estimate the interfering speech sources and diffuse background noise jointly and blindly. Herewith we can effectively deal with the underdetermined BSS scenario (more point sources than sensors) in reverberant environments and can even allow for additional babble noise in the background.
Enhancements of unitary ESPRIT for non-circular sources

Estimating the directions of arrival of several wavefronts impinging on an array of sensors is a requirement in a variety of applications including radar, mobile communications, sonar, and seismology. Subspace based high-resolution parameter estimation schemes like ESPRIT and MUSIC have become very popular. Such parameter estimation algorithms often use forward-backward averaging to enhance their resolution, especially in the case of correlated sources. Further enhancements can be achieved if the source signals are non-circular. We derive an efficient subspace estimation scheme that exploits the non-circularity of the sources and already includes forward-backward averaging. Moreover, appropriate spatial smoothing techniques are introduced. Completely real-valued implementations of 1D and 2D unitary ESPRIT for non-circular sources are presented as examples. In these cases, NC unitary ESPRIT improves the resolution capability and the noise robustness of standard ESPRIT as well as unitary ESPRIT and can handle more sources than sensors.
Big Data Processing for Pervasive Environment in Cloud Computing

The Internet of Things (IoT) has given rise to new types of data, emerging for instance from the collection of sensor data and the control of actuators. The explosion of devices that have automated and perhaps improved the lives of all of us has generated a huge mass of information that will continue to grow exponentially. For this reason the need to store, manage, and treat the ever increasing amounts of data that comes via the Internet of Things has become urgent. In this context, Big Data becomes immensely important, making possible to turn into this amount of data in information, knowledge, and, ultimately, wisdom. This paper aims at analyze Big Data challenges and requirements coming from different Smart City applications providing an original solution that uses Big Data technologies for redesigning an IoT context aware application for the exploitation of pervasive environment addressing problems and discussing the important aspects of the selected solution.
The Characterization of High-Fat Diet and Multiple Low-Dose Streptozotocin Induced Type 2 Diabetes Rat Model

Aim. Based on the previously established method, we developed a better and stable animal model of type 2 diabetes mellitus by high-fat diet combined with multiple low-dose STZ injections. Meanwhile, this new model was used to evaluate the antidiabetic effect of berberine. Method. Wistar male rats fed with regular chow for 4 weeks received vehicle (control groups), rats fed with high-fat diet for 4 weeks received different amounts of STZ once or twice by intraperitoneal injection (diabetic model groups), and diabetic rats were treated with berberine (100 mg/kg, berberine treatment group). Intraperitoneal glucose tolerance test and insulin tolerance test were carried out. Moreover, fasting blood glucose, fasting insulin, total cholesterol, and triglyceride were measured to evaluate the dynamic blood sugar and lipid metabolism. Result. The highest successful rate (100%) was observed in rats treated with a single injection of 45 mg/kg STZ, but the plasma insulin level of this particular group was significantly decreased, and ISI has no difference compared to control group. The successful rate of 30 mg/kg STZ twice injection group was significantly high (85%) and the rats in this group presented a typical characteristic of T2DM as insulin resistance, hyperglycemia, and blood lipid disorder. All these symptoms observed in the 30 mg/kg STZ twice injection group were recovered by the treatment of berberine. Conclusion. Together, these results indicated that high-fat diet combined with multiple low doses of STZ (30 mg/kg at weekly intervals for 2 weeks) proved to be a better way for developing a stable animal model of type 2 diabetes, and this new model may be suitable for pharmaceutical screening.
Testing the Performance of Tropical Cyclone Genesis Indices in Future Climates Using the HiRAM Model

AbstractTropical cyclone genesis indices (TCGIs) are functions of the large-scale environment that are designed to be proxies for the probability of tropical cyclone (TC) genesis. While the performance of TCGIs in the current climate can be assessed by direct comparison to TC observations, their ability to represent future TC activity based on projections of the large-scale environment cannot. Here the authors examine the performance of TCGIs in high-resolution atmospheric model simulations forced with sea surface temperatures (SST) of future, warmer climate scenarios. They investigate whether the TCGIs derived for the present climate can, when computed from large-scale fields taken from future climate simulations, capture the simulated global mean decreases in TC frequency. The TCGIs differ in their choice of environmental predictors, and several choices of predictors perform well in the present climate. However, some TCGIs that perform well in the present climate do not accurately reproduce the simulate.
Validation of Scoring Systems That Predict Outcomes in Patients With Coronary Artery Disease Undergoing Coronary Artery Bypass Grafting Surgery

Abstract Several risk stratification scores, based on angiographic or clinical parameters, have been developed to evaluate outcomes in patients with left main coronary artery disease (LMCAD) who undergo coronary artery bypass grafting (CABG). This study aims to validate the predictive ability of different risk scoring systems with regard to long-term outcomes after CABG. This single-center study retrospectively re-evaluated the Synergy Between PCI with TAXUS and Cardiac Surgery (SYNTAX) score; EuroSCORE; age, creatinine, and ejection fraction (ACEF) score; modified ACEF score; clinical SYNTAX; logistic clinical SYNTAX score (logistic CSS); and Parsonnet scores for 305 patients with LMCAD who underwent CABG. The endpoints were 5-year rate of all-cause death and major adverse cardio-cerebral events (MACCEs), including cardiovascular (CV) death, myocardial infarction (MI), and stroke and target vessel revascularization (TVR). Compared with the SYNTAX score, other scores were significantly higher in discriminative ability for all-cause death (SYNTAX vs others: P < 0.01). The EuroSCORE ≥6 showed significant outcome difference on all-cause death, CV death, MI, and MACCE (P < .01). Multivariate analysis indicated the SYNTAX score was a non-significant predictor for different outcomes. Adjusted multivariate analysis revealed that the EuroSCORE was the strongest predictor of all-cause death (hazard ratio[HR]: 1.17; P < 0.001), CV death (HR: 1.16; P < 0.001), and MACCE (HR: 1.09; P = 0.01). The ACEF score and logistic CSS were predictive factors for TVR (HR: 0.25, P = 0.03; HR: 0.85, P = 0.01). The EuroSCORE scoring system most accurately predicts all-cause death, CV death, and MACCE over 5 years, whereas low ACEF score and logistic CSS are independently associated with TVR over the 5-year period following CABG in patients with LMCAD undergoing CABG.
Detection of real-life emotions in call centers

In contrast to most previous studies conducted on artificial data with archetypal emotions, this paper addresses some of the challenges faced when studying real-life non-basic emotions. A 10-hour dialog corpus recorded in a French Medical emergency call center has been studied. In this paper, our aim is twofold, to study emotion mixtures in natural data and to achieve high level of performances in real-life emotion detection. An annotation scheme using two labels per segment has been used for representing emotion mixtures. A closer study of these mixtures has been carried out, revealing the presence of conflictual valence emotions. A correct detection rate of about 82% was obtained between Negative and Positive emotions using paralinguistic cues without taking into account these conflictual blended emotions. A perceptive study and the confusions analysis obtained with automatic detection enable to validate the annotation scheme with two emotion labels per segment.