Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance of the two-stage, dual-mode oculomotor servo system

Creating an optimum man-machine interface in the visual domain requires detailed knowledge of the human Precision Optical Servo system (POS) with particular focus on the oculomotor servosystem. The physiology of the human oculomotor system is more advanced than that of even his closest animal relatives. To satisfy the human's needs for defensive safety as well as provide the analytical capability supporting his inquisitiveness, two primary modes of information analysis are provided within the POS. Within the analytical mode, humans employ a multidimensional, fundamentally luminance-based, narrow-field-of-view associative correlator for interp and percept extraction. This associative correlation process relies upon the orthogonal phase coherent character of the "tremor" associated with the fine motions of the eyes. Simultaneously, a lower resolution, fundamentally luminance-based, spatial-change-detection mechanism is used to maintain awareness of a larger external environment. To support the above modes, a two-stage servomechanism is used. The angular performances of these two stages are quite different. These differences have a profound impact on good interface design.This paper provides a schematic of the complete POS, a more detailed description of the oculomotor servosystem, and the numerics describing its performance parameters. These parameters lead to the minimum recognition interval required for symbolic displays. Color is shown to play an ancillary, though important, role in the capability of the POS of the visual system. A New Chromaticity Diagram is offered that makes it easier to understand the role of color in POS operation and in color perception in general. All of the above descriptions are supported by a larger scale schematic of the overall sensory/cognitive system.
A Comparative Study of Word Embeddings for Reading Comprehension

The focus of past machine learning research for Reading Comprehension tasks has been primarily on the design of novel deep learning architectures. Here we show that seemingly minor choices made on (1) the use of pre-trained word embeddings, and (2) the representation of out-of-vocabulary tokens at test time, can turn out to have a larger impact than architectural choices on the final performance. We systematically explore several options for these choices, and provide recommendations to researchers working in this area.
A TWO-TIER OR MULTI-TIER EUROPE: ASSESSING THE INNOVATION CAPACITIES OF CENTRAL AND EAST EUROPEAN

2 This paper assesses the innovation capacities of the central and east European (CEE) candidate countries and the countries of the EU using a set of 25 indicators organized within the national innovation capacity (NIC) framework. Based on regression and cluster analysis the paper shows that NIC in the enlarged EU does not follow a simple 'East – West' divide. The wider Europe is structured into three groups of countries and CEE countries are fall into two, the less developed and medium developed EU, groups. NICs in the enlarged EU are assessed across four dimensions of capacity: absorptive, R&D, diffusion and demand. In each of these dimensions we observe a two-or three-tiering of countries, which goes beyond a simple 'East' – ' West' pattern. The results of this assessment have important implications for candidate countries as well as for the EU policy towards structural assistance.
High-Threshold K+ Current Increases Gain by Offsetting a Frequency-Dependent Increase in Low-Threshold K+ Current

High-frequency firing neurons are found in numerous central systems, including the auditory brainstem, thalamus, hippocampus, and neocortex. The kinetics of high-threshold K+ currents (IKHT) from the Kv3 subfamily has led to the proposal that these channels offset cumulative Na+ current inactivation and stabilize tonic high-frequency firing. However, all high-frequency firing neurons, examined to date, also express low-threshold K+ currents (IKLT) that have slower kinetics and play an important role in setting the subthreshold and filtering properties of the neuron. IKLT has also been shown to dampen excitability and is therefore likely to oppose high-frequency firing. In this study, we examined the role of IKHT in pyramidal cells of the electrosensory lobe of weakly electric fish, which are characterized by high-frequency firing, a very wide frequency range, and high levels of IKHT. In particular, we examined the mechanisms that allow IKHT to set the gain of the F-I relationship by interacting with another low-threshold K+ current. We found that IKHT increases the gain of the F-I relationship and influences spike waveform almost exclusively in the high-frequency firing range. The frequency dependence arises from IKHT influencing both the IKLT and Na+ currents. IKHT thus plays a significant role in stabilizing high-frequency firing by preventing a steady-state accumulation of IKLT that is as important as preventing Na+ current inactivation.
Elimination of atretic follicles from the mouse ovary: a TEM and immunohistochemical study in mice

We examined numerous ovarian follicles from 32–35 d virgin mice by transmission electron microscopy and light microscopic immunohistochemistry. No macrophages were seen, but various stages of apoptotic granulosa cells were encountered. Presumably a granulosa cell or its debris in an advanced stage of apoptosis was destined to be phagocytosed by adjacent normal‐looking granulosa cells. Other granulosa cells of normal appearance were seen in the region of the zona pellucida in contact with and apparently phagocytosing atrophic oocytes. Such granulosa cells were characterised by the presence of gap junctions with other cells and frequently contained annular gap junctions in the cytoplasm. To confirm the lack of involvement of macrophages in the process of follicular atresia and elimination, specially prepared ovarian sections were incubated with antimouse macrophage monoclonal antibodies (F4/80, Mac‐1, Mac‐2). None of the follicles examined showed positive immunoreactivity with these antibodies. Atretic follicles may shrink and eventually disappear from the ovary as a result of repeated apoptosis and phagocytosis by granulosa cells. There is no evidence for the presence or involvement of macrophages in the atretic follicles, at least in prereproductive mice as examined.
A Conceptual Model of the System for Online Maintenance of a Living Organism

This paper proposes a conceptual model of the system for online tracking and tuning a living organism (e.g., the human body) to insure that each physiological process of the organism runs within its recommended boundaries at all times. The system includes a Biofeedback Module, a Biological Module, an Adjusting Module, and a Recommender Module. The conception exploits an interdisciplinary approach. The Biofeedback Module deploys innovations in bio-sensing technology. The Biological Module uses concepts of biological and mathematical sciences; it is responsible for biological signal processing and is designed as a Multi-Agent System (MAS). The Adjusting Module is inspired by findings in sport, nutrition, pharmacy, meditation, and other health related areas. Its computational model is implemented using Agent Technology and Game Theory. The Recommender Module insures interaction with a user representing computational results in a readable format. All elements are integrated in a small handy device. The proposed conception has a wide range of applications, including biology, health care, sport, entertain, robotics, and military.
Predictors of Self-Determination in Postsecondary Education for Students with Intellectual and Developmental Disabilities

Given the increasing enrollment of students with intellectual and developmental disabilities in postsecondary education and the potential impact of self-determination on postsecondary outcomes, this study analyzed data on the self-determination status of students with intellectual and developmental disabilities completing their first year of a postsecondary education program. Secondary school (e.g., inclusion in high school and participation in state assessments) and postsecondary education experiences (e.g., advocacy for accommodations, participation in social activities, and living arrangements) that predicted self-determination status were examined. Directions for future research and practice are discussed. Researchers have consistently identified selfdetermination as a key factor to success in postsecondary education for students with disabilities (Field, Sarver, & Shaw, 2003; Shogren & Shaw, 2016; Test et al., 2009; Thoma & Getzel, 2005). In a systematic review of the literature on evidence-based predictors of postschool outcomes, Test et al. (2009) identified self-determination and self-advocacy interventions in secondary school as predictor of more positive postsecondary education outcomes for students with disabilities. Shogren and Shaw (2016), analyzing data from the National Longitudinal Transition Study-2, found that students’ level of autonomy, an essential characteristic of self-determination, predicted access and progress in postsecondary education for students with high incidence disabilities. Thoma and Getzel (2005), in interviews with students with disabilities in college, found that students identified skills associated with self-determination, including problem-solving, self-realization, goalsetting, and self-management, as critical to
Igusa modular forms and 'the simplest' Lorentzian Kac-Moody algebras

Automorphic corrections for the? Lorentzian Kac-Moody algebras with the? simplest generalized Cartan matrices of rank? 3, ? and? are found. For this correction, which is a? generalized Kac-Moody Lie super algebra, is delivered by , the Igusa -modular form of weight 35, while for it is given by some Siegel modular form of weight? 30 with respect to a 2-congruence subgroup of . Expansions of and in infinite products are obtained and the? multiplicities of all the? roots of the corresponding generalized Lorentzian Kac-Moody superalgebras are calculated. These multiplicities are determined by the Fourier coefficients of certain Jacobi forms of weight? 0 and index? 1. The? method adopted for constructing and leads in a? natural way to an? explicit construction (as infinite products or sums) of Siegel modular forms whose divisors are Humbert surfaces with fixed discriminants. A geometric construction of these forms was proposed by van der Geer in 1982. To show the? prospects for further studies, the? list of all hyperbolic symmetric generalized Cartan matrices with the? following properties is presented: is a? matrix of rank? 3 and of elliptic or parabolic type, has a? lattice Weyl vector, and contains a? parabolic submatrix .
Channel Estimation in Link Adaptation Strategies for MIMO-OFDM Systems

Multiple transmit-and-receive antennas jointly with orthogonal frequency division multiplexing (OFDM) provide a high number degrees of freedom for link adaptation according to the wireless channel condition. In this paper we analyze the impact of two different channel estimation techniques in the link adaptation of MIMO-OFDM wireless systems. We propose to add the choice of the channel estimation technique according to the redundancy and error caused by each strategy, which can be measured in terms of a degradation in the goodput of the system.
Entropy based feature extraction for motorbike engine faults diagnosing using neural network and wavelet transform

The sound of working vehicle provides an important clue for engine faults diagnosis. Endless efforts have been put into the research of fault diagnosis based on sound. It offers concrete economic benefits, which can lead to high system reliability and save maintenance cost. A number of diagnostic systems for vehicle repair have been developing in recent years. Artificial Neural Network is a very demanding application and popularly implemented in many industries including condition monitoring via fault diagnosis. This paper presents a feature extraction algorithm using total entropy of 5 level decomposition of wavelet transform. The engine noise signal is decomposed into 5 levels (A5, D5, A4, D4, A3, D3, A2, D2, A1, D1) using Daubechies “db4” wavelet family. From the decomposed signals, the entropy is applied for each levels and the feature are extracted and used to develop a backpropagation neural network.
Learning Pigeon Behaviour Using Binary Latent Variables

In an effort to better understand the complex courtship behaviour of pigeons, we have built a model learned from motion capture data. We employ a Conditional Restricted Boltzmann Machine with binary latent features and real-valued visible units. The units are conditioned on information from previous time steps in a sequence to learn long-term effects and infer current features. We validate a trained model by quantifying the characteristic “head-bobbing” present in generated pigeon motion. We also introduce a method of predicting missing data by marginalizing out the hidden variables and minimizing the free energy of the model. An alternative prediction method using forward and reverse passes over gaps of missing markers was presented as well. Lastly, the effects of head and foot motion on prediction results were analyzed. Website: http://www.matthewzeiler. com/videos/
Defects in the interfacial layer of SiO2-HfO2 gate stacks: Depth distribution and indentification

The defects in the SIO<sub>2</sub> interfacial layer (IL) in SiO<sub>2</sub>-HfO<sub>2</sub> gate stacks are studied using charge pumping (CP). To that aim, conventional CP curves and interface trap density vs. energy, D<sub>it</sub>(E), profiles measured on these devices are compared with those recorded from state of the art (S-A) MOSFETs. By doing so, specific features in the results recorded from the devices with HfO<sub>2</sub> are pointed out. Then, trap depth concentration, N<sub>1</sub>(x), profiles, recorded from the Si- SIO<sub>2</sub> interface towards HIO<sub>2</sub> are reported. With regard to S-A MOSFETs, these profiles provide evidence of 1) a strong increase in N, towards HfO<sub>2</sub>, 2) an impact of HfO<sub>2</sub> on the trap properties up to the Si-SiO<sub>2</sub> interface, and 3), a large increase in the minimum N, value. As the SiO<sub>2</sub>-HfO<sub>2</sub> interface is probed for the devices annealed at the lowest temperature, T<sub>A</sub>, the profiles show a continuous increase of N<sub>t</sub> from SiO<sub>2</sub> to HfO<sub>2</sub>. Then, the impacts on the trap profiles of both T<sub>A</sub>, and the HfO<sub>2</sub> thickness deposited, d<sub>HfO2</sub>, are dealt with. A higher T<sub>A</sub> yields SIO<sub>2</sub> re-growth. This 1) pushes the increase of Nt observed towards HIO<sub>2</sub> further away from the Si-SiO<sub>2</sub> interface but 2) yields also to a deeper penetration of this increase in the IL. The first effect dominates. Also, it seems that increasing d<sub>HfO2</sub> slightly increases the trap penetration into the IL. Finally, the two states of the P<sub>bo</sub> center are identified in the trap depth concentration profiles. Their characteristics differ from those in S-A MOSFETs. They are correlated for the first time in fresh and fully processed devices with the interface trap densities measured in both parts of the silicon bandgap. By adjusting the experimental conditions, the N<sub>1</sub>(x) profiles corresponding to the two P^ center levels can be drawn.
Argus: Low-Cost, Comprehensive Error Detection in Simple Cores

We have developed Argus, a novel approach for providing low-cost, comprehensive error detection for simple cores. The key to Argus is that the operation of a von Neumann core consists of four fundamental tasks - control flow, dataflow, computation, and memory access - that can be checked separately. We prove that Argus can detect any error by observing whether any of these tasks are performed incorrectly. We describe a prototype implementation, Argus-1, based on a single-issue, 4-stage, in-order processor to illustrate the potential of our approach. Experiments show that Argus-1 detects transient and permanent errors in simple cores with much lower impact on performance (<4% average overhead) and chip area (<17% overhead) than previous techniques.
Studying process in a Title IV-E waiver evaluation project: Interviews with regional and executive managers

ABSTRACT A Midwestern state’s child welfare agency contracted with a university to evaluate a 5-year Title IV-E Waiver Demonstration Project. As part of the process study, evaluators employed case study analysis to investigate Waiver knowledge and effects as reported by Regional and Executive Managers across 4 years of the demonstration period. Data from 78 interviews reveal enhanced levels of manager–evaluator rapport and integrated Waiver knowledge across years of data collection. This study highlights the value of middle managers as rich data sources, studying process through qualitative methods in child welfare evaluation, and how consistent relationships can support partnerships over time.
Protection of depth-image-based rendering 3D images using blind watermarking

The content protection for image-based 3D data is getting more importance with the advance of low cost 3D display devices. The depth-image-based rendering (DIBR) 3D image is one of the image-based 3D data which consists of the centre image and the depth image generated by the content provider. The left-eye image and the right-eye image are rendered from the centre image and the depth image at the content consumer side. The blind watermarking for DIBR 3D image is rarely studied in the literature. In this project, a novel blind multiple watermarking scheme is proposed to deal with the content protection problem of DIBR 3D images. Besides the usual requirement of mutual orthogonally among reference patterns for multiple water mark embedding, we found that proper embedding order plays an even more important role in watermarking the DIBR 3D images. Experimental results show that the proposed scheme is robust against the JPEG compression and noise adding attacks. More interestingly, it is found that the proposed watermarking can also tolerate large range variations of the depth image during rendering.
Applying Link Prediction for Repository Recommendation on GitHub

GitHub is one of the world’s most popular platforms for open source software development. As different developers have different expertise and interests, given information about the repositories to which a user has contributed, it may be useful to suggest “similar” repositories that the user may wish to contribute to. For our project, we attempt to solve this problem by performing link prediction on the GitHub repository graph using Supervised Random Walks, and then applying Personalized PageRank on the resulting graph for specific users to obtain repository recommendations.
Vision Paper: Towards an Understanding of the Limits of Map-Reduce Computation

A significant amount of recent research work has addressed the problem of solving various data management problems in the cloud. The major algorithmic challenges in map-reduce computations involve balancing a multitude of factors such as the number of machines available for mappers/reducers, their memory requirements, and communication cost (total amount of data sent from mappers to reducers). Most past work provides custom solutions to specific problems, e.g., performing fuzzy joins in map-reduce, clustering, graph analyses, and so on. While some problems are amenable to very efficient map-reduce algorithms, some other problems do not lend themselves to a natural distribution, and have provable lower bounds. Clearly, the ease of "map-reducability" is closely related to whether the problem can be partitioned into independent pieces, which are distributed across mappers/reducers. What makes a problem distributable? Can we characterize general properties of problems that determine how easy or hard it is to find efficient map-reduce algorithms? This is a vision paper that attempts to answer the questions described above.
Using a Support Vector Machine to identify pre-miRNAs in soybean (Glycine max) introns

MicroRNAs (miRNAs) are small Ribonucleic Acid (RNA) molecules ∼18–22 nucleotides (nt) in length that regulates gene expression in animals, plants and viruses. Due to its small size and occurrence in different development stages of organisms, the experimental identification of miRNAs becomes difficult, and computational approaches are being developed in order to precede and guide biological experiments. This paper describes our approach based on a Support Vector Machine (SVM) algorithm to identify miRNA's precursor (pre-miRNA) in soybean (Glycine max) transcript introns, that was developed using a secondary structure predictor of pre-miRNAs sequences to establish the feature set for training, testing and validation phases of SVM algorithm.
The Role of Parts-of-Speech in Feature Selection

This research explores the role of parts-of-speech (POS) in feature selection in text categorization. We compare the use of different POS, namely nouns, verbs, adjectives and adverbs with a feature set that contains all POS. The best results are obtained with the use of only nouns. Therefore, we make use of a WordNet-based POS feature selection approach using the nouns feature set to compare with popular feature selection methods, namely Chi Square (Chi2) and Information Gain (IG). We find that the WordNet-based POS approach using only nouns as features can outperform Chi2 and IG in categorization effectiveness. Here, a machine learning approach to text categorization is employed and the Reuters-21578 top ten categories are used as the dataset.
Dynamic Scheduling with Convex Delay Costs: The Generalized CU Rule

We consider a general single-server multiclass queueing system that incurs a delay cost Ck(Tk) for each class k job that resides Tk units of time in the system. This paper derives a scheduling policy that minimizes the total cumulative delay cost when the system operates during a finite time horizon. Denote the marginal delay cost function and the (possibly non-stationary) average processing time of class k by ck = C'k and 1/uk, respectively, and let ak(t) be the "age" or time that the oldest class k job has been waiting at time t. We call the scheduling policy that at time t serves the oldest waiting job of that class k with the highest index uk(t)ck(ak(t)), the generalized cu rule. As a dynamic priority rule that depends on very little data, the generalized cu rule is attractive to implement. We show that, with nondecreasing convex delay costs, the generalized cu rule is asymptotically optimal if the system operates in heavy traffic and give explicit expressions for the associated performance characteristics: the delay (throughput time) process and the minimum cumulative delay cost. The optimality result is robust in that it holds for a countable number of classes and several homogeneous servers in a nonstationary, deterministic or stochastic environment where arrival and service processes can be general and interdependent.
Extending list scheduling to consider execution frequency

Frequency-based list scheduling (FBLS) extends standard list scheduling by considering execution frequencies within a schedule. This is useful for global instruction scheduling methods that schedule groups of basic blocks, called meta-blocks; as though they were a single block. Traditional local schedulers operate on the premise that each instruction is executed the same number of times as every other instruction in the "block", an unwarranted assumption for meta-blocks. This assumption can lead metablocks schedulers to produce inefficient code. FBLS provides an answer to this problem by considering the differing execution frequencies within meta-blocks when scheduling operations. To evaluate our contention that FBLS is a useful extension to standard list scheduling, we implemented FBLS and compared it to standard list scheduling within the context of dominator-path scheduling, a meta-block global scheduling algorithm. Experimental results show overall run-time improvement of 10.9% for livermore loops.