Miracle-dz/UniFAR-FTUs huggingface dataset:
Spread of current from monopolar stimulation of the lateral hypothalamus.

the boundary of a system. The characteristics of the threshold bared of insulation. The effects of stimulation were tested gradients suggest that, with intensities on the order of 25 pa, the for 5 days with the electrode at each lateral hypothalamic effective spread of current from small monopolar electrodes may site; each week the electrodes were moved approximately be as little as l/8 mm. The changes in threshold with electrode 34 mm further toward the base of the hypothalamus. movement also suggest that the lateral hypothalamic eating and The details of the stimulation procedure are given elsedrinking systems are homogeneous, have distinct boundaries, and are bordered on the ventral aspect by systems that do not mediate where (8). The important data for the present discussion are
Inverse-Free Extreme Learning Machine With Optimal Information Updating

The extreme learning machine (ELM) has drawn insensitive research attentions due to its effectiveness in solving many machine learning problems. However, the matrix inversion operation involved in the algorithm is computational prohibitive and limits the wide applications of ELM in many scenarios. To overcome this problem, in this paper, we propose an inverse-free ELM to incrementally increase the number of hidden nodes, and update the connection weights progressively and optimally. Theoretical analysis proves the monotonic decrease of the training error with the proposed updating procedure and also proves the optimality in every updating step. Extensive numerical experiments show the effectiveness and accuracy of the proposed algorithm.
ANALISA PENYEBARAN STASIUN HUJAN TERHADAP DEBIT BANJIR RANCANGAN PADA DAS KEDUNGLARANGAN (Kabupaten Pasuruan Jawa Timur)

Analisa hidrologi merupakan satu bagian analisis awal dalam perancangan bangunan hidrolik. Analisis hidrologi membutuhkan data yang terdiri dari data curah hujan, data debit dan data iklim. Hal ini membutuhkan beberapa jumlah stasiun hujan dan penempatan lokasi stasiun yang tepat. Untuk menetapkan jumlah hujan yang jatuh di dalam suatu DAS, dibutuhkan sejumlah stasiun hujan yang ada pada DAS yang bersangkutan, Ketelitian pengukuran data hujan dipengaruhi oleh jumlah stasiun hujan dan pola penyebaran di dalam DAS. Penempatan stasiun hujan yang tepat, jumlah stasiun hujan, pola penyebaran akan dapat diperoleh data yang akurat mengenai kedalaman, penyebaran dan intensitas hujannya. Kondisi persebaran stasiun hujan di DAS Kedunglarangan tergolong relative banyak, dimana saat ini terdapat 16 stasiun hujan pada wilayah DAS seluas 267,3 km2. Menurut standar WMO cukup diwakili oleh 2 stasiun hujan, sedangkan Bleasdale menyarankan 4 stasiun. Varshney menetapkan 8 stasiun sebagai angka yang ideal, kemudian Sugawara menyarankan 10 atau 15 stasiun hujan.
The insulin-stimulated receptor kinase is a tyrosine-specific casein kinase.

Insulin stimulates a kinase that phosphorylates tyrosines in the insulin receptor; this kinase is tightly associated with the insulin receptor itself. We now show that the insulin-stimulated casein kinase, present in solubilized, lectin-purified receptor preparations from rat liver, is indistinguishable from the insulin receptor kinase. As with phosphorylation of the insulin receptor, insulin selectively enhanced by 2-3-fold the phosphorylation of tyrosines in casein. The insulin-stimulated activities of both kinases were inactivated at 37 degrees C with the same t0.5 of 5 min and were identically affected by alkylating agents. Both receptor and casein kinase activities were specifically coprecipitated by anti-receptor antibodies or by insulin and anti-insulin antibodies. When the latter type of immune complexes were incubated with an excess of insulin, both kinase activities were quantitatively recovered. We therefore conclude that insulin-stimulated receptor and casein phosphorylations are probably catalyzed by a single enzyme which is tightly associated with the receptor itself. Now, by replacing casein for receptor as substrate, it is possible to measure the enzymatic activity of this receptor-related kinase itself, i.e. independent of the receptor as substrate. Detection of this activity is improved in the presence of certain alkylating agents. Use of artificial substrates (in combination with alkylating agents) is particularly important to dissect the functional components of the receptor complex, to study mechanisms of enzyme regulation and especially in situations where the available receptor for study is limited, e.g. fresh or cultured cells from patients.
Simulation modelling of the felling-bunching machine logging operation with the multiple-tree accumulating head

This study deal with the productivity of felling-bunching machine for differently aged forest when using conventional logging heads (CH) and multiple-tree accumulating head (AH). Experimental studies of logging technological processes using AH and CH-based felling-bunching machine were carries out for harvesting of large-sized and thin tree. We used total number of thin and large-sized trees in the working zone, 6…10 and 1…5 trees, respectively, and the average volume of thin and large-sized tree stick, 0.0042…0.096 and 0.26…2.26 m3, respectively, as independent variables. It is shown that the felling-bunching machine productivity per shift increases with the number of trees within the working zone. At the same time, with increase of total number of trees within the working zone, the productivity decreases. The highest productivity of the machine is in the harvesting only large-sized trees. In practice, it is possible to recommend joint harvesting of large-sized and thin trees, when the average volume of the large-sized tree stick does not exceed the two volumes of thin trees.
DQM: Decentralized Quadratically Approximated Alternating Direction Method of Multipliers

This paper considers decentralized consensus optimization problems where nodes of a network have access to different summands of a global objective function. Nodes cooperate to minimize the global objective by exchanging information with neighbors only. A decentralized version of the alternating directions method of multipliers (DADMM) is a common method for solving this category of problems. DADMM exhibits linear convergence rate to the optimal objective for strongly convex functions but its implementation requires solving a convex optimization problem at each iteration. This can be computationally costly and may result in large overall convergence times. The decentralized quadratically approximated ADMM algorithm (DQM), which minimizes a quadratic approximation of the objective function that DADMM minimizes at each iteration, is proposed here. The consequent reduction in computational time is shown to have minimal effect on convergence properties. Convergence still proceeds at a linear rate with a guaranteed factor that is asymptotically equivalent to the DADMM linear convergence rate factor. Numerical results demonstrate advantages of DQM relative to DADMM and other alternatives in a logistic regression problem.
Laser modification of ultracold atomic collisions: Theory.

Specific molecular mechanisms are proposed for associative ionization collisions of ultracold sodium atoms in a hybrid optical trap. When an intense, strongly detuned optical trap laser is on, the ionization rate is modulated by molecular bound-state resonances which are strongly affected by field dressing. When the weak, slightly detuned optical molasses lasers are on to provide cooling, an excitation mechanism which produces the two excited atoms at large internuclear separation in different hyperfine states accounts for the lower observed ionization rate.
Evaluation of dyspnea in the elderly patient.

The symptom of dyspnea in the elderly person should not be considered part of the "normal aging process. " Instead, the history, examination, and testing should focus on cardiac disease, respiratory disease, and deconditioning as the most likely causes. Because respiratory sensation is diminished with aging, breathlessness may not develop until a more advanced stage of disease or dysfunction. Clinical measurement of dyspnea is important to assess its severity and to evaluate response to treatment. Specific treatment should be directed toward the pathophysiology of the underlying disease. General strategies for relieving dyspnea include breathing techniques, exercise training and reconditioning, oxygen therapy, improved nutrition, and, in selective cases, psychotropic medication.
Cooperative Air-Defense System of System Immune Multi-agent Model Based on Danger theory for Surface Warship Formation

This paper is inspired by the principle of the biological immune system (BIS) and the Danger Theory (DT). On the base of analyzing the comparability on the defense function and adaptation and self-organization between the cooperative air-defense system of system (SoS) for surface warship formation (CASoSSWF) and the BIS, the CASoSSWF was mapped to the BIS, furthermore the new function structure models of immune agents of the CASoSSWF, which include monitoring agent, adjusting agent, killing agent and pre-warning agent, were designed. The novel immune multi-agent danger network model (IMDNM) was established. The original model exploited for the cooperative air-defense system of system for surface warship formation, and can provide a theoretical model to found a modern naval combat simulation system.
First-Order Multi-class Subgroup Discovery

Subgroup discovery is concerned with finding subsets of a population whose class distribution is significantly different from the overall distribution. Previously subgroup discovery has been predominantly investigated under the propositional logic framework. This paper investigates multi-class subgroup discovery in an inductive logic programming setting, where subgroups are defined by conjunctions in first-order logic. We present a new weighted covering algorithm, inspired by the Aleph first-order rule learner, that uses seed examples in order to learn diverse, representative and highly predictive subgroups that capture interesting patterns across multiple classes. Our approach experimentally shows considerable and statistically significant improvement of predictive power, both in terms of accuracy and AUC, and theory construction time, by considering fewer hypotheses.
Investigating the Optimization Strategies on Performance of Rainfall-Runoff Modeling

Regarding to importance of modeling calibration, this study will be focused on probabilistic role of different strategies in calibration and verification steps. Tank lumped conceptual model was selected as a hydrological platform to investigate the effects of each optimization strategy on model performance. However, much considerable efforts are required to calibrate a large number of parameters in conceptual models to obtain better results. With development of artificial intelligence, three probabilistic Global Search Algorithms (GSAs) including Shuffled Complex Evolution (SCE), Genetic Algorithm (GA) and Rosenbrock Multi-Start Search (RBN) and also three Objective Functions (OFs) consisted of NashSutcliffe (NSE), Root Mean Square Error (RMSE) and mean absolute error (MAE) were employed for model calibration (comparing the performance of different GSAs versus OFs). The best set of parameters, which is derived from the calibration step, will be used as prediction coefficients for the model verification stage. Performance evaluation of the simulation results was undertaken using Coefficient of Correlation (r) and Descriptive Statistics. Results indicated that all of optimization strategies have a relative ability to retrieve optimal values of eighteen parameters of the Tank model. However, the best GSAs for daily runoff simulation are SCE (0.871) and GA (0.864), respectively, for calibration and verification phases. In case of the OFs result, NSE (0.763) and RMSE (0.834) are more performant for calibration and verification of the model. Engineering EPiC Series in Engineering Volume 3, 2018, Pages 827–835 HIC 2018. 13th International Conference on Hydroinformatics G. La Loggia, G. Freni, V. Puleo and M. De Marchis (eds.), HIC 2018 (EPiC Series in Engineering, vol. 3), pp. 827–835 Finally, the best strategy was selected by combining the results of GSAs and OFs models. Finally, SCE*MAE (0.906) and GA*RMSE (0.868) were selected as a top series.
Depletion of reduction potential and key energy generation metabolic enzymes underlies tellurite toxicity in Deinococcus radiodurans

Oxidative stress resistant Deinococcus radiodurans surprisingly exhibited moderate sensitivity to tellurite induced oxidative stress (LD50 = 40 μM tellurite, 40 min exposure). The organism reduced 70% of 40 μM potassium tellurite within 5 h. Tellurite exposure significantly modulated cellular redox status. The level of ROS and protein carbonyl contents increased while the cellular reduction potential substantially decreased following tellurite exposure. Cellular thiols levels initially increased (within 30 min) of tellurite exposure but decreased at later time points. At proteome level, tellurite resistance proteins (TerB and TerD), tellurite reducing enzymes (pyruvate dehydrogense subunits E1 and E3), ROS detoxification enzymes (superoxide dismutase and thioredoxin reductase), and protein folding chaperones (DnaK, EF‐Ts, and PPIase) displayed increased abundance in tellurite‐stressed cells. However, remarkably decreased levels of key metabolic enzymes (aconitase, transketolase, 3‐hydroxy acyl‐CoA dehydrogenase, acyl‐CoA dehydrogenase, electron transfer flavoprotein alpha, and beta) involved in carbon and energy metabolism were observed upon tellurite stress. The results demonstrate that depletion of reduction potential in intensive tellurite reduction with impaired energy metabolism lead to tellurite toxicity in D. radiodurans.
A 1V 10-bit 500KS/s energy-efficient SAR ADC using Master-Slave DAC technique in 180nm CMOS

This work verifies the technique - Master-Slave digital to analog converter (M-S DAC) - for reducing the significant energy dissipation of 93% in comparison with the conventional capacitor array in successive approximation register analog to digital converter (SAR ADC). This technique avoiding the redundant charge and discharge in larger capacitors is demonstrated by a fabricated chip in 180nm CMOS standard process, and reaches performance including signal-to-noise-and-distortion ratio of 59.2dB in equivalent 9.6-bit, power consumption of 28 μW at the sampling frequency of 500KS/s with the conditions of supplying voltage of 1V in core area of 0.15mm2.
Beyond Triangle Inequality: Sifting Noisy and Outlier Distance Measurements for Localization

Knowing accurate positions of nodes in wireless ad-hoc and sensor networks is essential for a wide range of pervasive and mobile applications. However, errors are inevitable in distance measurements and we observe that a small number of outliers can degrade localization accuracy drastically. To deal with noisy and outlier ranging results, triangle inequality is often employed in existing approaches. Our study shows that triangle inequality has a lot of limitations which make it far from accurate and reliable. In this study, we formally define the outlier detection problem for network localization and build a theoretical foundation to identify outliers based on graph embeddability and rigidity theory. Our analysis shows that the redundancy of distance measurements plays an important role. We then design a bilateration generic cycles based outlier detection algorithm, and examine its effectiveness and efficiency through a network prototype implementation of MicaZ motes as well as extensive simulations. The results shows that our design significantly improves the localization accuracy by wisely rejecting outliers.
Gene-Disease Network Analysis Reveals Functional Modules in Mendelian, Complex and Environmental Diseases

Background Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult. Principal Findings We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell. Conclusions For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases. Availability The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim. es/DisGeNET/DisGeNETweb.html#Download.
Motion Estimation from Image and Inertial Measurements

Cameras and inertial sensors are each good candidates for autonomous vehicle navigation, modeling from video, and other applications that require six-degrees-of-freedom motion estimation. However, these sensors are also good candidates to be deployed together, since each can be used to resolve the ambiguities in estimated motion that result from using the other modality alone. In this paper, we consider the specific problem of estimating sensor motion and other unknowns from image, gyro, and accelerometer measurements, in environments without known fiducials. This paper targets applications where external positions references such as global positioning are not available, and focuses on the use of small and inexpensive inertial sensors, for applications where weight and cost requirements preclude the use of precision inertial navigation systems. We present two algorithms for estimating sensor motion from image and inertial measurements. The first algorithm is a batch method, which produces estimates of the sensor motion, scene structure, and other unknowns using measurements from the entire observation sequence simultaneously. The second algorithm recovers sensor motion, scene structure, and other parameters recursively, and is suitable for use with long or “infinite” sequences, in which no feature is always visible. We evaluate the accuracy of the algorithms and their sensitivity to their estimation parameters using a sequence of four experiments. These experiments focus on cases where estimates from image or inertial measurements alone are poor, on the relative advantage of using inertial measurements and omni directional images, and on long sequences in which the percentage of the image sequence in which individual features are visible is low.
The deca-GX3 proteins Yae1-Lto1 function as adaptors recruiting the ABC protein Rli1 for iron-sulfur cluster insertion

Cytosolic and nuclear iron-sulfur (Fe-S) proteins are involved in many essential pathways including translation and DNA maintenance. Their maturation requires the cytosolic Fe-S protein assembly (CIA) machinery. To identify new CIA proteins we employed systematic protein interaction approaches and discovered the essential proteins Yae1 and Lto1 as binding partners of the CIA targeting complex. Depletion of Yae1 or Lto1 results in defective Fe-S maturation of the ribosome-associated ABC protein Rli1, but surprisingly no other tested targets. Yae1 and Lto1 facilitate Fe-S cluster assembly on Rli1 in a chain of binding events. Lto1 uses its conserved C-terminal tryptophan for binding the CIA targeting complex, the deca-GX3 motifs in both Yae1 and Lto1 facilitate their complex formation, and Yae1 recruits Rli1. Human YAE1D1 and the cancer-related ORAOV1 can replace their yeast counterparts demonstrating evolutionary conservation. Collectively, the Yae1-Lto1 complex functions as a target-specific adaptor that recruits apo-Rli1 to the generic CIA machinery. DOI: http://dx.doi. org/10.7554/eLife.08231. 001
On Finding maximally redundant trees in strictly linear time

Redundant trees are commonly used for protection and restoration in communications networks. Zhang et al. presented a linear time algorithm to compute node-redundant trees in 2-node-connected networks, which has become widely cited in the literature. In this paper, we show that it is difficult to implement this algorithm providing both correctness and linear complexity at the same time. Therefore, we present a revised algorithm with strict linear time complexity. Moreover, we generalize the concept of node-redundant trees from 2-node-connected networks to arbitrary topologies, a crucial development since real networks can not always satisfy 2-connectedness, especially after a failure.
Parallel Genetic Algorithm Model Based on AHP and Neural Networks for Enterprise Comprehensive Business

The comprehensive business includes many links, and the radical of agile networked manufacturing is reducing cost and improving agility for manufacturing enterprises. So,in order to form an agile networked manufacturing system, it needs selecting appropriate partners in every link. But it is difficult to select from so large number of partners. Genetic algorithm is a method for resolving combinatorial optimization problem, however, simple genetic algorithm still exist defect on computing speed, combining parallel method and neural networks, the computer speed of parallel genetic algorithm can become faster. Moreover, to determine the important of each selection attributes for agile networked manufacturing system, we adopt AHP. With parallel genetic algorithm model based on AHP, the agile networked manufacturing alliance can establish swiftly and accurately.
eyePatterns: software for identifying patterns and similarities across fixation sequences

Fixation sequence analysis can reveal the cognitive strategies that drive eye movements. Unfortunately this type of analysis is not as common as other popular eye movement measures, such as fixation duration and trace length, because the proper tools for fixation sequence analysis are not incorporated into most popular eye movement software. This paper describes eyePatterns, a new tool for discovering similarities in fixation sequences and identifying the experimental variables that may influence their characteristics.