Miracle-dz/UniFAR-FTUs huggingface dataset:
ReStore: Reusing Results of MapReduce Jobs

'Big Data' analysis has become a central activity in business and science. Companies such as Facebook, Yahoo, and Google now own petabyte-scale data warehouses that are accessed on a regular basis. Terabyte-scale data warehouses are now common in many smaller organizations. This big data analysis is mostly supported by the MapReduce programming and execution model and its implementations, most notably Hadoop which is now one of the major big data platforms. Users of MapReduce often have analysis tasks that are too complex to express as one MapReduce job. Instead, they often use high-level query languages such as Pig Latin, Hive, or Jaql to express their complex analysis tasks. The compilers of these query languages translate queries into workflows of MapReduce jobs. Each job in such a workflow produces an output that is stored in the distributed file system used by the MapReduce system (e.g., HDFS in the case of Hadoop). These intermediate results are used as input by subsequent jobs in the workflow. The current practice is to delete these intermediate outputs after finishing the execution of the workflow. In our work, we developed ReStore, a system that improves the performance of workflows of MapReduce jobs generated from high-level query languages by storing the intermediate results of executed workflows and reusing them for future workflows submitted to the system. ReStore can be built on top of dataflow language processor such as Pig, which translates queries into workflows of MapReduce jobs. Each of these MapReduce jobs has a physical query execution plan that contains one or more physical operators that are executed by this job. ReStore rewrites the MapReduce jobs in a submitted workflow at the level of the physical query execution plan in order to reuse job outputs previously stored in the system. ReStore also stores the outputs of executed jobs for future reuse, and creates more reuse opportunities by storing the outputs of parts of jobs (which we call sub-jobs). We have implemented ReStore as an extension to the Pig dataflow system on top of Hadoop, and we experimentally demonstrated significant speedups on queries from the PigMix benchmark.
Analysis of Serial-Search-Based Code Acquisition in the Multiple-Transmit/Multiple-Receive-Antenna-Aided DS-CDMA Downlink

In this paper, we investigate the serial-search-based initial code-acquisition performance of direct-sequence code division multiple access (DS-CDMA) employing multiple transmit/multiple receive antennas when communicating over uncorrelated Rayleigh channels. We characterize the associated performance trends as a function of the number of antennas. It is demonstrated that, in contrast to our expectation, the achievable correct-detection probability degrades in our typical target operational range as the number of transmit antennas is increased. When maintaining a given total transmit power, our findings suggest that increasing the number of transmit antennas results in the combination of the low-energy noise-contaminated signals of the transmit antennas, which ultimately increases the mean acquisition time (MAT). However, it is extremely undesirable to increase the MAT when the system is capable of attaining its target bit-error-ratio performance at reduced signal-power levels, as a benefit of employing multiple transmit antennas.
Cardiopulmonary Resuscitation Pattern Evaluation Based on Ensemble Empirical Mode Decomposition Filter via Nonlinear Approaches

Good quality cardiopulmonary resuscitation (CPR) is the mainstay of treatment for managing patients with out-of-hospital cardiac arrest (OHCA). Assessment of the quality of the CPR delivered is now possible through the electrocardiography (ECG) signal that can be collected by an automated external defibrillator (AED). This study evaluates a nonlinear approximation of the CPR given to the asystole patients. The raw ECG signal is filtered using ensemble empirical mode decomposition (EEMD), and the CPR-related intrinsic mode functions (IMF) are chosen to be evaluated. In addition, sample entropy (SE), complexity index (CI), and detrended fluctuation algorithm (DFA) are collated and statistical analysis is performed using ANOVA. The primary outcome measure assessed is the patient survival rate after two hours. CPR pattern of 951 asystole patients was analyzed for quality of CPR delivered. There was no significant difference observed in the CPR-related IMFs peak-to-peak interval analysis for patients who are younger or older than 60 years of age, similarly to the amplitude difference evaluation for SE and DFA. However, there is a difference noted for the CI (p < 0.05). The results show that patients group younger than 60 years have higher survival rate with high complexity of the CPR-IMFs amplitude differences.
A Novel Angiotensin-Converting Enzyme–Related Carboxypeptidase (ACE2) Converts Angiotensin I to Angiotensin 1-9

ACE2, the first known human homologue of angiotensin-converting enzyme (ACE), was identified from 5′ sequencing of a human heart failure ventricle cDNA library. ACE2 has an apparent signal peptide, a single metalloprotease active site, and a transmembrane domain. The metalloprotease catalytic domains of ACE2 and ACE are 42% identical, and comparison of the genomic structures indicates that the two genes arose through duplication. In contrast to the more ubiquitous ACE, ACE2 transcripts are found only in heart, kidney, and testis of 23 human tissues examined. Immunohistochemistry shows ACE2 protein predominantly in the endothelium of coronary and intrarenal vessels and in renal tubular epithelium. Active ACE2 enzyme is secreted from transfected cells by cleavage of the N-terminal to the transmembrane domain. Recombinant ACE2 hydrolyzes the carboxy terminal leucine from angiotensin I to generate angiotensin 1-9, which is converted to smaller angiotensin peptides by ACE in vitro and by cardiomyocytes in culture. ACE2 can also cleave des-Arg bradykinin and neurotensin but not bradykinin or 15 other vasoactive and hormonal peptides tested. ACE2 is not inhibited by lisinopril or captopril. The organ- and cell-specific expression of ACE2 and its unique cleavage of key vasoactive peptides suggest an essential role for ACE2 in the local renin-angiotensin system of the heart and kidney. The full text of this article is available at http://www.circresaha. org.
Toddlers Always Get the Last Word: Recency biases in early verbal behavior

A popular conception about language development is that comprehension precedes production. Although this is certainly true during the earliest stages of phonological development, once a child possesses the basic articulatory skills required for imitation, it need not necessarily be the case. A child could produce a word without possessing the fully formed lexical representation through imitation. In some cases, such as in response to questions containing fixed choices, this behavior could be mistaken for a deeper understanding of the words’ semantic content. In this paper, we present evidence that 2to 3-year-old children exhibit a robust recency bias when verbally responding to two-alternative choice questions (i.e., they select the second, most recently mentioned option), possibly due to the availability of the second word in phonological memory. We find further evidence of this effect outside of a laboratory setting in naturalistic conversational contexts in CHILDES (MacWhinney, 2000), a large corpus of transcribed child-adult interactions.
Effect of Replacing Sugar with Non-Caloric Sweeteners in Beverages on the Reward Value after Repeated Exposure

Background The reward value of food is partly dependent on learned associations. It is not yet known whether replacing sugar with non-caloric sweeteners in food is affecting long-term acceptance. Objective To determine the effect of replacing sugar with non-caloric sweeteners in a nutrient-empty drink (soft drink) versus nutrient-rich drink (yoghurt drink) on reward value after repeated exposure. Design We used a randomized crossover design whereby forty subjects (15 men, 25 women) with a mean±SD age of 21±2 y and BMI of 21.5±1.7 kg/m2 consumed a fixed portion of a non-caloric sweetened (NS) and sugar sweetened (SS) versions of either a soft drink or a yoghurt drink (counterbalanced) for breakfast which were distinguishable by means of colored labels. Each version of a drink was offered 10 times in semi-random order. Before and after conditioning the reward value of the drinks was assessed using behavioral tasks on wanting, liking, and expected satiety. In a subgroup (n=18) fMRI was performed to assess brain reward responses to the drinks. Results Outcomes of both the behavioral tasks and fMRI showed that conditioning did not affect the reward value of the NS and SS versions of the drinks significantly. Overall, subjects preferred the yoghurt drinks to the soft drinks and the ss drinks to the NS drinks. In addition, they expected the yoghurt drinks to be more satiating, they reduced hunger more, and delayed the first eating episode more. Conditioning did not influence these effects. Conclusion Our study showed that repeated consumption of a non-caloric sweetened beverage, instead of a sugar sweetened version, appears not to result in changes in the reward value. It cannot be ruled out that learned associations between sensory attributes and food satiating capacity which developed preceding the conditioning period, during lifetime, affected the reward value of the drinks.
Clinical proteomics and mass spectrometry profiling for cancer detection

A key challenge in the clinical proteomics of cancer is the identification of biomarkers that would enable early detection, diagnosis and monitoring of disease progression to improve long-term survival of patients. Recent advances in proteomic instrumentation and computational methodologies offer a unique chance to rapidly identify these new candidate markers or pattern of markers. The combination of retentate affinity chromatography and mass spectrometry is one of the most interesting new approaches for cancer diagnostics using proteomic profiling. This review presents two technologies in this field, surface-enhanced laser desorption/ionization time-of-flight and Clinprot™, and aims to summarize the results of studies obtained with the first of them for the early diagnosis of human cancer. Despite promising results, the use of the proteomic profiling as a diagnostic tool brought some controversies and technical problems, and still requires some efforts to be standardized and validated.
Interface of the Polarizable Continuum Model of Solvation with Semi-Empirical Methods in the GAMESS Program

An interface between semi-empirical methods and the polarized continuum model (PCM) of solvation successfully implemented into GAMESS following the approach by Chudinov et al (Chem. Phys. 1992, 160, 41). The interface includes energy gradients and is parallelized. For large molecules such as ubiquitin a reasonable speedup (up to a factor of six) is observed for up to 16 cores. The SCF convergence is greatly improved by PCM for proteins compared to the gas phase.
Performance Comparison and Analysis of Table-Driven and On-Demand Routing Protocols for Mobile Ad-hoc Networks

— Mobile ad hoc network is a collection of mobile nodes communicating through wireless channels without any existing network infrastructure or centralized administration. Because of the limited transmission range of wireless network interfaces, multiple "hops" may be needed to exchange data across the network. In order to facilitate communication within the network, a routing protocol is used to discover routes between nodes. The primary goal of such an ad hoc network routing protocol is correct and efficient route establishment between a pair of nodes so that messages may be delivered in a timely manner. Route construction should be done with a minimum of overhead and bandwidth consumption. This paper examines two routing protocols for mobile ad hoc networks– the Destination Sequenced Distance Vector (DSDV), the table- driven protocol and the Ad hoc On- Demand Distance Vector routing (AODV), an On –Demand protocol and evaluates both protocols based on packet delivery fraction, normalized routing load, average delay and throughput while varying number of nodes, speed and pause time.
Using the M-Learning to Support Learning of the Concept of the Derivative

One of the main obstacles in Mexico’s engineering programs is math comprehension, especially in the Derivative concept. Due to this, we present a study case that relates Mobile Computing and Classroom Learning in the “Escuela Superior de Cómputo”, based on the Educational model of the Instituto Politécnico Nacional (competence based work and problem solutions) in which we propose apps and activities to teach the concept of the Derivative. M-Learning is emphasized as one of its lines, as the objective is the use of mobile devices running an app that uses its components such as sensors, screen, camera and processing power in classroom work. In this paper, we employed Augmented Reality (ARRoC), based on the good results this technology has had in the field of learning. This proposal was developed using a qualitative research methodology supported by quantitative research. The methodological instruments used on this proposal are: observation, questionnaires, interviews and evaluations. We obtained positive results with a 40% increase using M-Learning, from the 20% increase using traditional means.
Embeddings of Schatten Norms with Applications to Data Streams

Given an n×d matrix A, its Schatten-p norm, p >= 1, is defined as |A|_p = (sum_{i=1}^rank(A) sigma(i)^p）^{1/p} where sigma_i(A) is the i-th largest singular value of A. These norms have been studied in functional analysis in the context of non-commutative L_p-spaces, and recently in data stream and linear sketching models of computation. Basic questions on the relations between these norms, such as their embeddability, are still open. Specifically, given a set of matrices A_1, . , A_poly(nd) in R^{n x d}, suppose we want to construct a linear map L such that L(A_i) in R^{n' x d'} for each i, where n' = 1. Then how large do n' and d' need to be as a function of D_{p,q}? We nearly resolve this question for every p, q >= 1, for the case where L(A_i) can be expressed as R*A_i*S, where R and S are arbitrary matrices that are allowed to depend on A_1, . ,A_t, that is, L(A_i) can be implemented by left and right matrix multiplication. Namely, for every p, q >= 1, we provide nearly matching upper and lower bounds on the size of n' and d' as a function of D_{p,q}. Importantly, our upper bounds are oblivious, meaning that R and S do not depend on the A_i, while our lower bounds hold even if R and S depend on the A_i. As an application of our upper bounds, we answer a recent open question of Blasiok et al. about space-approximation trade-offs for the Schatten 1-norm, showing in a data stream it is possible to estimate the Schatten-1 norm up to a factor of D >= 1 using O~(min(n, d)^2/D^4) space.
Rift Valley Fever Virus Encephalitis Is Associated with an Ineffective Systemic Immune Response and Activated T Cell Infiltration into the CNS in an Immunocompetent Mouse Model

Background Rift Valley fever virus (RVFV) causes outbreaks of severe disease in livestock and humans throughout Africa and the Arabian Peninsula. In people, RVFV generally causes a self-limiting febrile illness but in a subset of individuals, it progresses to more serious disease. One manifestation is a delayed-onset encephalitis that can be fatal or leave the afflicted with long-term neurologic sequelae. In order to design targeted interventions, the basic pathogenesis of RVFV encephalitis must be better understood. Methodology/Principal Findings To characterize the host immune responses and viral kinetics associated with fatal and nonfatal infections, mice were infected with an attenuated RVFV lacking NSs (ΔNSs) that causes lethal disease only when administered intranasally (IN). Following IN infection, C57BL/6 mice developed severe neurologic disease and succumbed 7–9 days post-infection. In contrast, inoculation of ΔNSs virus subcutaneously in the footpad (FP) resulted in a subclinical infection characterized by a robust immune response with rapid antibody production and strong T cell responses. IN-inoculated mice had delayed antibody responses and failed to clear virus from the periphery. Severe neurological signs and obtundation characterized end stage-disease in IN-inoculated mice, and within the CNS, the development of peak virus RNA loads coincided with strong proinflammatory responses and infiltration of activated T cells. Interestingly, depletion of T cells did not significantly alter survival, suggesting that neurologic disease is not a by-product of an aberrant immune response. Conclusions/Significance Comparison of fatal (IN-inoculated) and nonfatal (FP-inoculated) ΔNSs RVFV infections in the mouse model highlighted the role of the host immune response in controlling viral replication and therefore determining clinical outcome. There was no evidence to suggest that neurologic disease is immune-mediated in RVFV infection. These results provide important insights for the future design of vaccines and therapeutic options.
Issues and Ideas Paper The Value of Campus in Contemporary Higher Education

The idea of ‘going to university’ is integrally tied to the concept of the university campus as a physical place. The campus is both the location in which learning occurs and a place that gives meaning to the learning experience. It is a setting that provides the opportunity for social and intellectual engagement with a scholarly community as well as a place with the capacity to engender knowledge, self-discovery and personal transformation. Higher education institutions have produced some of the world’s most famous physical settings, many of which have served as the source of inspiration for art, poetry, literature and film. Narratives about university life impart a strong sense of the places in which they are set, creating memorable depictions of student experiences on campus. Paul Temple suggests that the powerful images of university campuses evoked by certain novels stem from an understanding that ‘in universities, the interactions between people and they material world are more than usually significant’ (xxvi). Universities have a unique sense of place that is made meaningful by the actions and interactions that occur within them; they are illustrative of Henri Lefebvre’s notion that space is a complex social construction that is not merely built but produced, and made productive through social practices (1991).
Locality-Preserved Maximum Information Projection

Dimensionality reduction is usually involved in the domains of artificial intelligence and machine learning. Linear projection of features is of particular interest for dimensionality reduction since it is simple to calculate and analytically analyze. In this paper, we propose an essentially linear projection technique, called locality-preserved maximum information projection (LPMIP), to identify the underlying manifold structure of a data set. LPMIP considers both the within-locality and the between-locality in the processing of manifold learning. Equivalently, the goal of LPMIP is to preserve the local structure while maximize the out-of-locality (global) information of the samples simultaneously. Different from principal component analysis (PCA) that aims to preserve the global information and locality-preserving projections (LPPs) that is in favor of preserving the local structure of the data set, LPMIP seeks a tradeoff between the global and local structures, which is adjusted by a parameter alpha, so as to find a sub- space that detects the intrinsic manifold structure for classification tasks. Computationally, by constructing the adjacency matrix, LPMIP is formulated as an eigenvalue problem. LPMIP yields orthogonal basis functions, and completely avoids the singularity problem as it exists in LPP. Further, we develop an efficient and stable LPMIP/QR algorithm for implementing LPMIP, especially, on high-dimensional data set. Theoretical analysis shows that conventional linear projection methods such as (weighted) PCA, maximum margin criterion (MMC), linear discriminant analysis (LDA), and LPP could be derived from the LPMIP framework by setting different graph models and constraints. Extensive experiments on face, digit, and facial expression recognition show the effectiveness of the proposed LPMIP method.
Practical implementation of a 16-channel C-band phased array radar receiver

This paper presents the practical implementation of a 16 channel, C-band FMCW radar phased array receiver developed for the purpose of imaging flowing avalanches. The phased array is designed for a 30° field of view to give full coverage of an avalanche track. A fully populated, uniform linear array design is achieved using overlapping sub-arrays of 2A spacing, and with 16 elements, the beamwidth of the array is 1.6°. The phased array is arranged on a three layer PCB that is split into four panels due to its large physical width (1.95 m). The array is mounted on plastic pillars screwed to an aluminium base in a watertight radome to provide mechanical strength. Radio absorbent material between the PCB's and the aluminium base prevent back lobe reflections. A continuous ground across the PCB panels is maintained using grounded copper sheet bridges. Initial laboratory measurements of the phased array show that its performance agrees well with predictions.
Trends of Staple Crops Yields in Nasarawa State, Nigeria

The studies focused on getting the crop yield trends of Maize, yam and Rice which are staple crops in Nasarawa State. Crop yields data were collected from the archives of Nasarawa Agricultural Development Programme for the years 1998-2016. The yields were subjected to simple regression analysis in order to determine the trends of the yields. Results show that maize and yam are on the positive trend while rice yield is on the decrease all over the state. Urbanization is seen as one of the factors hampering high yield of rice while more effort should be intensify for both dry and wet farming in the state.
Taming Wireless Fluctuations by Predictive Queuing Using a Sparse-Coding Link-State Model

We introduce State-Informed Link-Layer Queuing (SILQ), a system that models, predicts, and avoids packet delivery failures caused by temporary wireless outages in everyday scenarios. By stabilizing connections in adverse link conditions, SILQ boosts throughput and reduces performance variation for network applications, for example by preventing unnecessary TCP timeouts due to dead zones, elevators, and subway tunnels. SILQ makes predictions in real-time by actively probing links, matching measurements to an overcomplete dictionary of patterns learned offline, and classifying the resulting sparse feature vectors to identify those that precede outages. We use a clustering method called sparse coding to build our data-driven link model, and show that it produces more variation-tolerant predictions than traditional loss-rate, location-based, or Markov chain techniques. We present extensive data collection and field-validation of SILQ in airborne, indoor, and urban scenarios of practical interest. We show how offline unsupervised learning discovers link-state patterns that are stable across diverse networks and signal-propagation environments. Using these canonical primitives, we train outage predictors for 802.11 (Wi-Fi) and 3G cellular networks to demonstrate TCP throughput gains of 4x with off-the-shelf mobile devices. SILQ addresses delivery failures solely at the link layer, requires no new hardware, and upholds the end-to-end design principle, to enable easy integration across applications, devices, and networks.