nils-herrmann/clean_scirepeval huggingface dataset:
Maximizing Fun by Creating Data with Easily Reducible Subjective Complexity

The Formal Theory of Fun and Creativity (1990–2010) [Schmidhuber,  Formal theory of creativity, fun, and intrinsic motivation (1990–2010).  230–247 (2010b)] describes principles of a curious and creative agent that never stops generating nontrivial and novel and surprising tasks and data. Two modules are needed: a data encoder and a data creator. The former encodes the growing history of sensory data as the agent is interacting with its environment; the latter executes actions shaping the history. Both learn. The encoder continually tries to encode the created data more efficiently, by discovering new regularities in it. Its learning progress is the wow-effect or fun or intrinsic reward of the creator, which maximizes future expected reward, being motivated to invent skills leading to interesting data that the encoder does not yet know but can easily learn with little computational effort. I have argued that this simple formal principle explains science and art and music and humor.  This overview heavily draws on previous publications since 1990, especially Schmidhuber (2010b), parts of which are reprinted with friendly permission by IEEE.
Prenatal maternal stress programs infant stress regulation.

Prenatal exposure to inappropriate levels of glucocorticoids (GCs) and maternal stress are putative mechanisms for the fetal programming of later health outcomes. The current investigation examined the influence of prenatal maternal cortisol and maternal psychosocial stress on infant physiological and behavioral responses to stress.



The study sample comprised 116 women and their full term infants. Maternal plasma cortisol and report of stress, anxiety and depression were assessed at 15, 19, 25, 31 and 36 + weeks' gestational age. Infant cortisol and behavioral responses to the painful stress of a heel-stick blood draw were evaluated at 24 hours after birth. The association between prenatal maternal measures and infant cortisol and behavioral stress responses was examined using hierarchical linear growth curve modeling.



A larger infant cortisol response to the heel-stick procedure was associated with exposure to elevated concentrations of maternal cortisol during the late second and third trimesters. Additionally, a slower rate of behavioral recovery from the painful stress of a heel-stick blood draw was predicted by elevated levels of maternal cortisol early in pregnancy as well as prenatal maternal psychosocial stress throughout gestation. These associations could not be explained by mode of delivery, prenatal medical history, socioeconomic status or child race, sex or birth order.



These data suggest that exposure to maternal cortisol and psychosocial stress exerts programming influences on the developing fetus with consequences for infant stress regulation.
Multi-modal Face Pose Estimation with Multi-task Manifold Deep Learning

Human face pose estimation aims at estimating the gazing direction or head postures with 2D images. It gives some very important information such as communicative gestures, saliency detection and so on, which attracts plenty of attention recently. However, it is challenging because of complex background, various orientations and face appearance visibility. Therefore, a descriptive representation of face images and mapping it to poses are critical. In this paper, we make use of multi-modal data and propose a novel face pose estimation method that uses a novel deep learning framework named Multi-task Manifold Deep Learning (MDL). It is based on feature extraction with improved deep neural networks and multi-modal mapping relationship with multi-task learning. In the proposed deep learning based framework, Manifold Regularized Convolutional Layers (MRCL) improve traditional convolutional layers by learning the relationship among outputs of neurons. Besides, in the proposed mapping relationship learning method, different modals of face representations are naturally combined to learn the mapping function from face images to poses. In this way, the computed mapping model with multiple tasks is improved. Experimental results on three challenging benchmark datasets DPOSE, HPID and BKHPD demonstrate the outstanding performance of MDL.
A direction-sensitive fall detection system using single 3D accelerometer and learning classifier

The rate of fall incidence among the elderly people is ever increasing. It is at the sixth position in the list of causes of death for the people aged between 60 and 65; the second between 65 and 75; the first over 75. Treatment of a patient, experiencing complications due to a fall, within the first 12 minutes after a fall brings a survival rate of 48% –75%. So, fast and accurate detection of fall events is emerging as a big necessity for many countries, especially for the advanced world where the society adopts the culture of independent living for elderly people. It is also important to determine the direction of a fall as it can help determine the locations of joint weakness and fracture quickly. Researchers' claims of fall detection accuracy of over 90% are based on accelerometers and embedded extra sensors like gyroscopes, cardio tachometer, magnetometer, and barometric pressure sensors. However, most such fall detection algorithms have been developed based on observational analysis of the data gathered, leading to thresholds settings for fall/non-fall situations. To detect the direction of fall, some researchers uses gyroscope or more accelerometers. The proposed method, using single 3D accelerometer and machine learning algorithm particularly SVM (Support vector machine) is to detect 4 types of falls (forward, backward, right and left). When applied to experimental data from 13 male subjects, the proposed system discriminates between falls and activities of daily living (ADL) with better than previously reported accuracy level. The system is reliable, user friendly and cost effective.
Flexible transparent electrodes made of electrochemically exfoliated graphene sheets from low-cost graphite pieces

Graphene is a highly promising candidate to replace oxide-based conducting materials for fabrication of transparent electrodes used in flexible displays and electronic devices. However, so far an appropriate method is still not found to provide high-quality, low-cost graphene in a large quantity. To overcome this issue, we demonstrate that massive graphene sheets can be synthesized by electrochemical exfoliation process using artificial graphite as a starting material. The product is mainly composed of bilayer thin graphene sheets. Their lateral size can be up to several to 20 lm, and the quality determined by Raman spectroscopy is better than the reduced graphene oxide derived from graphene oxide. The transparent conducting films made of electrochemically exfoliated graphene can be simply prepared by an airbrush spraying method, which is easy to scale up for large-area deposition, and compatible with flexible poly-
Virtual Experiential Marketing on Online Customer Intentions and Loyalty

Advances in information and Web technologies have open numerous opportunities for online retailing. The pervasiveness of the Internet coupled with the keenness in competition among online retailers has led to virtual experiential marketing (VEM). This study examines the relationship of five VEM elements on customer browse and purchase intentions and loyalty, and the moderating effects of shopping orientation and Internet experience on these relationships. A survey was conducted of customers who frequently visited two online game stores to play two popular games in Taiwan. The results suggest that of the five VEM elements, three have positive effects on browse intention, and two on purchase intentions. Both browse and purchase intentions have positive effects on customer loyalty. Economic orientation was found to moderate that relationships between the VEM elements and browse and purchase intentions. However, convenience orientation moderated only the relationships between the VEM elements and browse intention.
Nested Mini-Batch K-Means

A new algorithm is proposed which accelerates the mini-batch k-means algorithm of Sculley (2010) by using the distance bounding approach of Elkan (2003). We argue that, when incorporating distance bounds into a mini-batch algorithm, already used data should preferentially be reused. To this end we propose using nested mini-batches, whereby data in a mini-batch at iteration t is automatically reused at iteration t+ 1. Using nested mini-batches presents two difficulties. The first is that unbalanced use of data can bias estimates, which we resolve by ensuring that each data sample contributes exactly once to centroids. The second is in choosing mini-batch sizes, which we address by balancing premature fine-tuning of centroids with redundancy induced slow-down. Experiments show that the resulting nmbatch algorithm is very effective, often arriving within 1% of the empirical minimum 100× earlier than the standard mini-batch algorithm.
AD3: alternating directions dual decomposition for MAP inference in graphical models

We present AD, a new algorithm for approximate maximum a posteriori (MAP) inference on factor graphs, based on the alternating directions method of multipliers. Like other dual decomposition algorithms, AD has a modular architecture, where local subproblems are solved independently, and their solutions are gathered to compute a global update. The key characteristic of AD is that each local subproblem has a quadratic regularizer, leading to faster convergence, both theoretically and in practice. We provide closed-form solutions for these AD subproblems for binary pairwise factors and factors imposing first-order logic constraints. For arbitrary factors (large or combinatorial), we introduce an active set method which requires only an oracle for computing a local MAP configuration, making AD applicable to a wide range of problems. Experiments on synthetic and real-world problems show that AD compares favorably with the state-of-the-art.
Who is .com?: Learning to Parse WHOIS Records

WHOIS is a long-established protocol for querying information about the 280M+ registered domain names on the Internet. Unfortunately, while such records are accessible in a ``human-readable'' format, they do not follow any consistent schema and thus are challenging to analyze at scale. Existing approaches, which rely on manual crafting of parsing rules and per-registrar templates, are inherently limited in coverage and fragile to ongoing changes in data representations. In this paper, we develop a statistical model for parsing WHOIS records that learns from labeled examples. Our model is a conditional random field (CRF) with a small number of hidden states, a large number of domain-specific features, and parameters that are estimated by efficient dynamic-programming procedures for probabilistic inference. We show that this approach can achieve extremely high accuracy (well over 99%) using modest amounts of labeled training data, that it is robust to minor changes in schema, and that it can adapt to new schema variants by incorporating just a handful of additional examples. Finally, using our parser, we conduct an exhaustive survey of the registration patterns found in 102M com domains.
Prediction of protein antigenic determinants from amino acid sequences.

A method is presented for locating protein antigenic determinants by analyzing amino acid sequences in order to find the point of greatest local hydrophilicity. This is accomplished by assigning each amino acid a numerical value (hydrophilicity value) and then repetitively averaging these values along the peptide chain. The point of highest local average hydrophilicity is invariably located in, or immediately adjacent to, an antigenic determinant. It was found that the prediction success rate depended on averaging group length, with hexapeptide averages yielding optimal results. The method was developed using 12 proteins for which extensive immunochemical analysis has been carried out and subsequently was used to predict antigenic determinants for the following proteins: hepatitis B surface antigen, influenza hemagglutinins, fowl plague virus hemagglutinin, human histocompatibility antigen HLA-B7, human interferons, Escherichia coli and cholera enterotoxins, ragweed allergens Ra3 and Ra5, and streptococcal M protein. The hepatitis B surface antigen sequence was synthesized by chemical means and was shown to have antigenic activity by radioimmunoassay.
Having a BLAST with bioinformatics (and avoiding BLASTphemy)

Searching for similarities between biological sequences is the principal means by which bioinformatics contributes to our understanding of biology. Of the various informatics tools developed to accomplish this task, the most widely used is BLAST, the basic local alignment search tool. This article discusses the principles, workings, applications and potential pitfalls of BLAST, focusing on the implementation developed at the National Center for Biotechnology Information.
Word lengths are optimized for efficient communication.

We demonstrate a substantial improvement on one of the most celebrated empirical laws in the study of language, Zipf's 75-y-old theory that word length is primarily determined by frequency of use. In accord with rational theories of communication, we show across 10 languages that average information content is a much better predictor of word length than frequency. This indicates that human lexicons are efficiently structured for communication by taking into account interword statistical dependencies. Lexical systems result from an optimization of communicative pressures, coding meanings efficiently given the complex statistics of natural language use.
Sectjunction: Wi-Fi indoor localization based on junction of signal sectors

In Wi-Fi fingerprint localization, a target sends its measured Received Signal Strength Indicator (RSSI) of access points (APs) to a server for its position estimation. Traditionally, the server estimates the target position by matching the RSSI with the fingerprints stored in database. Due to signal measurement uncertainty, this matching process often leads to a geographically dispersed set of reference points, resulting in unsatisfactory estimation accuracy. We propose a novel, efficient and highly accurate localization scheme termed Sectjunction which does not lead to a dispersed set of neighbors. For each selected AP, Sectjunction sectorizes its coverage area according to discrete signal levels, hence achieving robustness against measurement uncertainty. Based on the received AP RSSI, the target can then be mapped to the sector where it is likely to be. To further enhance its computational efficiency, Sectjunction partitions the site into multiple area clusters to narrow the search space. Through convex optimization, the target is localized based on the cluster and the junction of the sectors it is within. We have implemented Sectjunction, and our extensive experiments show that it significantly outperforms recent schemes with much lower estimation error.
Deep Learning in the Wild

Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks. While this interest is fueled by beautiful success stories, practical work in deep learning on novel tasks without existing baselines remains challenging. This paper explores the specific challenges arising in the realm of real world tasks, based on case studies from research & development in conjunction with industry, and extracts lessons learned from them. It thus fills a gap between the publication of latest algorithmic and methodical developments, and the usually omitted nitty-gritty of how to make them work. Specifically, we give insight into deep learning projects on face matching, print media monitoring, industrial quality control, music scanning, strategy game playing, and automated machine learning, thereby providing best practices for deep learning in practice.
Universal Impulse Noise Filter Based on Genetic Programming

In this paper, we present a novel method for impulse noise filter construction, based on the switching scheme with two cascaded detectors and two corresponding estimators. Genetic programming as a supervised learning algorithm is employed for building two detectors with complementary characteristics. The first detector identifies the majority of noisy pixels. The second detector searches for the remaining noise missed by the first detector, usually hidden in image details or with amplitudes close to its local neighborhood. Both detectors are based on the robust estimators of location and scale-median and MAD. The filter made by the proposed method is capable of effectively suppressing all kinds of impulse noise, in contrast to many existing filters which are specialized only for a particular noise model. In addition, we propose the usage of a new impulse noise model-the mixed impulse noise, which is more realistic and harder to treat than existing impulse noise models. The proposed model is the combination of commonly used noise models: salt-and-pepper and uniform impulse noise models. Simulation results show that the proposed two-stage GP filter produces excellent results and outperforms existing state-of-the-art filters.
A Reference Model for Requirements and Specifications

M a y / J u n e 2 0 0 0 I E E E S O F T W A R E 37 between the user-requirements specification and the software-requirements specification, mandating complete documentation of each according to various rules. Other cases emphasize this distinction less. For instance, some groups at Microsoft argue that the difficulty of keeping a technical specification consistent with the program is more trouble than the benefit merits.2 We can find a wide range of views in industry literature and from the many organizations that write software. Is it possible to clarify these various artifacts and study their properties, given the wide variations in the use of terms and the many different kinds of software being written? Our aim is to provide a framework for talking about key artifacts, their attributes, and relationships at a general level, but precisely enough that we can rigorously analyze substantive properties.
Energy-Efficient Stochastic Task Scheduling on Heterogeneous Computing Systems

In the past few years, with the rapid development of heterogeneous computing systems (HCS), the issue of energy consumption has attracted a great deal of attention. How to reduce energy consumption is currently a critical issue in designing HCS. In response to this challenge, many energy-aware scheduling algorithms have been developed primarily using the dynamic voltage-frequency scaling (DVFS) capability which has been incorporated into recent commodity processors. However, these techniques are unsatisfactory in minimizing both schedule length and energy consumption. Furthermore, most algorithms schedule tasks according to their average-case execution times and do not consider task execution times with probability distributions in the real-world. In realizing this, we study the problem of scheduling a bag-of-tasks (BoT) application, made of a collection of independent stochastic tasks with normal distributions of task execution times, on a heterogeneous platform with deadline and energy consumption budget constraints. We build execution time and energy consumption models for stochastic tasks on a single processor. We derive the expected value and variance of schedule length on HCS by Clark's equations. We formulate our stochastic task scheduling problem as a linear programming problem, in which we maximize the weighted probability of combined schedule length and energy consumption metric under deadline and energy consumption budget constraints. We propose a heuristic energy-aware stochastic task scheduling algorithm called ESTS to solve this problem. Our algorithm can achieve high scheduling performance for BoT applications with low time complexity O(n(M + logn)), where n is the number of tasks and M is the total number of processor frequencies. Our extensive simulations for performance evaluation based on randomly generated stochastic applications and real-world applications clearly demonstrate that our proposed heuristic algorithm can improve the weighted probability that both the deadline and the energy consumption budget constraints can be met, and has the capability of balancing between schedule length and energy consumption.
THE NEW HORIZONS MISSION TO PLUTO : ADVANCES IN TELECOMMUNICATIONS SYSTEM DESIGN

This paper presents the RF telecommunications system designed for the New Horizons mission, NASA’s planned mission to Pluto, with focus on new technologies developed to meet mission requirements. These technologies include an advanced digital receiver — a mission-enabler for its low DC power consumption at 2.3 W secondary power. The receiver is one-half of a card-based transceiver that is incorporated with other spacecraft functions into an integrated electronics module, providing further reductions in mass and power. Other developments include extending APL’s long and successful flight history in ultrastable oscillators (USOs) with an updated design for lower DC power. These USOs offer frequency stabilities to 1 part in 10, stabilities necessary to support New Horizons’ uplink radio science experiment. In antennas, the 2.1 meter high gain antenna makes use of shaped suband main reflectors to improve system performance and achieve a gain approaching 44 dBic. New Horizons would also be the first deep-space mission to fly a regenerative ranging system, offering up to a 30 dB performance improvement over sequential ranging, especially at long ranges. The paper will provide an overview of the current system design and development and performance details on the new technologies mentioned above. Other elements of the telecommunications system will also be discussed.  All representations made in this paper are contingent on a decision by NASA to go forward with the preparation for and launch of the mission.
A 5-Gb/s Digital Clock and Data Recovery Circuit With Reduced DCO Supply Noise Sensitivity Utilizing Coupling Network

A digital clock and data recovery (CDR) is presented, which employs a low supply sensitivity scheme for a digitally controlled oscillator (DCO). A coupling network comprising capacitors, resistors, and coupling buffers enhances the supply variation immunity of the DCO and mitigates the jitter performance degradation. A supply variation-dependent bias generator produces the corresponding bias voltage to alleviate the supply variation with minimal area and power penalty. The proposed scheme improves 29.3 ps of peak-to-peak jitter and 11.5 dB of spur level, at 6 and 5 MHz 50 mVpp sinusoidal supply noise tone, respectively. Fabricated in a 65-nm CMOS process, the proposed CDR operates at 5-Gb/s data rate with BER . 10-12 for PRBS 31 and consumes 15.4 mW. The CDR occupies an active die area of 0.075 mm2.