Miracle-dz/UniFAR-FTUs huggingface dataset:
When Behavior Matters: Games and Computation in A Behavioral Theory of the Firm

A game-theoretic rendering of the duopoly model described in Cyert and March (1963) was developed, sans behavioral constructs, and the equilibria values for the two economic decision variables (price and advertising expense) were derived assuming either noncooperative or cooperative strategic behavior. The results were compared to a computational form of the same model that additionally supported behavioral constructs and learning. A second study was conducted to examine the effects of setting the initial conditions of the computational-behavioral model to the price and advertising equilibria of the game-theoretic paradigm (reflecting cooperative and noncooperative strategies). The results of the first study indicate that the computational-behavioral model consistently performed in a hybrid manner, neither purely cooperative nor noncooperative as suggested by the game-theoretic paradigm. Pricing choices of the computational-behavioral model most resembled the noncooperative strategy, but the choice of advertising expenditure was lower than that predicted by the game-theoretic strategy (cooperative or noncooperative). Firm performance, as measured by discounted profits, was significantly higher in the game-theoretic analysis  an outcome that might be expected given the limited organizational realism in this paradigm. The second study demonstrated that initializing the decision variables of the computational-behavior models to those of the economic equilibria resulted in the firms’ pricing strategies shifting toward that found with noncooperative behavior, while the firms’ advertising strategies shifting toward that found with cooperative behavior. Firm performance, in terms of discounted profits, was substantially improved when firms were given noncooperative initial values but was worse when firms were given cooperative initial values.
Behavioral / Systems / Cognitive Evidence for Two Concurrent Inhibitory Mechanisms during Response Preparation

Julie Duque,1,2,3 David Lew,1,2 Riccardo Mazzocchio,4 Etienne Olivier,3 and Richard B. Ivry1,2 1Department of Psychology, 2Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California 94720, 3Institute of Neuroscience, Laboratory of Neurophysiology, Université catholique de Louvain, B-1200 Brussels, Belgium, and 4Sezione di Neurofisiologia Clinica, Dipartimento di Scienze Neurologiche, Neurochirurgiche e del Comportamento, Universita’ di Siena, I-53100 Siena, Italy
R EFLECTIVE D ECODING : U NSUPERVISED P ARAPHRASING AND A BDUCTIVE R EASONING

Pretrained Language Models (LMs) generate text with remarkable quality, novelty, and coherence. Yet applying LMs to the problems of paraphrasing and inﬁlling currently requires direct supervision, since these tasks break the left-to-right generation setup of pretrained LMs. We present R EFLECTIVE D ECODING , a novel unsupervised approach to apply the capabilities of pretrained LMs to non-sequential tasks. Our approach is general and applicable to two distant tasks – paraphrasing and abductive reasoning . It requires no supervision or parallel corpora, only two pretrained language models: forward and backward . R EFLECTIVE D ECODING operates in two intuitive steps. In the contextualization step, we use LMs to generate many left and right contexts which collectively capture the meaning of the input sentence. Then, in the reﬂection step we decode in the semantic neighborhood of the input, conditioning on an ensemble of generated contexts with the reverse direction LM. We reﬂect through the generated contexts, effectively using them as an intermediate meaning representation to generate conditional output. Empirical results demonstrate that R EFLECTIVE D ECODING outperforms strong unsupervised baselines on both paraphrasing and abductive text inﬁlling, signiﬁcantly narrowing the gap between unsupervised and supervised methods. R EFLECTIVE D ECODING introduces the concept of using generated contexts to represent meaning, opening up new possibilities for unsupervised conditional text generation.
Talking a team into being in online workplace collaborations: the discourse of virtual work

Using computer-mediated communication technologies is now ubiquitous in almost all workplace environments: to smaller or greater extent most people are engaged in some form of ‘virtual’ work. In fact, an increasing number of organisations now invest in technologies that enable online collaboration with an aim to improve communication within their organisations locally, as well as globally (El Tayeh et al., 2008). In their recent market survey, the Radicati group (2017) has found that the reliance on digital communication technologies continues to grow with business organizations of all sizes. The reasons for such strong adoption, and in particular the popularity of text-based online interaction platforms are well documented both in popular media (The Economist, 2016) and in scholarship (Anders, 2016).
PROBLEM: SHARDED RESILIENT TRANSACTION PROCESSING WITH MINIMAL COSTS

To enable scalable resilient blockchain systems, several powerful general-purpose approaches toward sharding such systems have been demonstrated. Unfortunately, these approaches all come with substantial costs for ordering and execution of multi-shard transactions. In this work, we ask whether one can achieve significant cost reductions for processing multi-shard transactions by limiting the type of workloads supported. To initiate the study of this problem, we propose core-CHIMERA (CCHIMERA). CCHIMERA uses strict UTXO-based environmental requirements to enable powerful multi-shard transaction processing with an absolute minimum amount of coordination between shards. In the environment we designed CCHIMERA for, CCHIMERA will operate perfectly with respect to all transactions proposed and approved by well-behaved clients, but does not provide any other guarantees. To illustrate that CCHIMERA-like protocols can also be of use in environments with faulty clients, we also demonstrate two generalizations of CCHIMERA, optimistic-CHIMERA and resilient-CHIMERA, that make different tradeoffs in complexity and costs when dealing with faulty behavior and attacks. Finally, we compare these three protocols and show their potential scalability and performance benefits over state-ofthe-art general-purpose systems. These results underline the importance of the study of specialized approaches toward sharding in resilient systems.
Exploiting Manifold Feature Representation for Eficient Classification of 3D Point Clouds

Classification of 3D Point Clouds DINGHAO YANG and WEI GAO, School of Electronic and Computer Engineering, Peking University, and also Peng Cheng Laboratory, China GE LI, School of Electronic and Computer Engineering, Peking University, China HUI YUAN, School of Control Science and Engineering, Shandong University, China JUNHUI HOU and SAM KWONG, Department of Computer Science, City University of Hong Kong, Hong Kong
Effect of irrigation quantities and tillage systems on sunflower yield under Khartoum State conditions, Sudan

The objective of this study was to investigate the response of sunflower (Helianthus annus L.) yield to different irrigation quantities and tillage systems. The experimental work was conducted at the Demonstration Farm of the Faculty of Agriculture, University of Khartoum, Shambat during two different seasons 2011/012 and 2012/013. The treatments were three irrigation quantities (100%, 85% and 70% of crop evapotranspiration) and four tillage systems (no till, discing, chiseling and ridging). Split plot experimental design with three replications was used in which the irrigation water amounts were assigned to the main plotsand the different tillage systems to the subplots.The parameters tested were soil physical properties, the vegetative growth and yield.The results indicated that,tillage systems affected clearly soil physical properties; namely bulk density and moisture content. Deep ploughing system decreased the bulk density as compared to no till system. The vegetative growth was significantly (P ≤ 0.05) affected by the different irrigation water amounts and tillage systems. Irrigation water amounts of 100% and 85% of ETc gave the higher values than 70% ETc in both seasons. The combination of irrigation water amounts with tillage systems significantly affected the yield. However the combination of no till with 85% ETc gave the highest yield (2.3 ton/fed) in the first season while chiseling with 85% ETc had the highest yield (1.14 ton/fed) in the second season
Determinants of livelihood strategies of fishing households in the volta Basin, Ghana

Abstract The main objective of this study is to identify livelihood strategies of fishing households in response to prevalent vulnerabilities they face within the Volta Basin. Questionnaires were administered to fishery households in the Basin using the multistage sampling technique. A non-hierarchical k-means cluster analysis partitioned the households into four livelihood strategies on which the multinomial logit regression was performed. These four identified strategies are fishery only, fishery and farming, fishery and non-farm, and fishery, non-farming and farming. The results from the multinomial logit regression revealed that marital status of head of household, number of months of food shortage experienced by a household per year, access to credit, access to extension services, distance to regular markets and district capital as well as experience in fishery were the major determinants of livelihood strategies. Implications for policy include the need for public extension services and training to invigorate fishery households’ income. As majority of the fishery households combined fishing and non-fishing strategies, livelihood intervention programmes should prioritize improvement of the non-fishing activities and lead to opening other opportunities for rural development. This will take pressure off the fish stock by facilitating the regeneration of fish stock.
Improving Convection Trigger Functions in Deep 1 Convective Parameterization Schemes Using Machine 2 Learning 3

17 Deficiencies in convection trigger functions used in deep convection parameterizations in General 18 Circulation Models (GCMs) have critical impacts on climate simulations. A novel convection 19 trigger function is developed using the machine learning (ML) classification model XGBoost. The 20 large-scale environmental information associated with convective events is obtained from the 21 long-term constrained variational analysis forcing data from the Atmospheric Radiation 22 Measurement (ARM) program at its Southern Great Plains (SGP) and Manaus (MAO) sites 23 representing, respectively, continental mid-latitude and tropical convection. The ML trigger is 24 separately trained and evaluated per site, and jointly trained and evaluated at both sites as a unified 25 trigger. The performance of the ML trigger is compared with four convective trigger functions 26 commonly used in GCMs: dilute convective available potential energy (CAPE), undilute CAPE, 27
Short-Segment Versus Long-Segment Stabilization in Thoracolumbar Burst Fracture: A Review of the Literature

Background: Thoracolumbar fractures are the most common traumatic fractures of the spinal column. They result from a compression load without the associated shear, rotational, or translational components. Although neurological compression and spine instability are the recognized indications for surgery, it is unclear which approach should be used to stabilize the spine. Shortsegment (SS) stabilization involves the upper and lower levels of the fractured vertebra, while use of more extended instrumentations is defined as long-segment (LS) stabilization. The SS stabilization is the most common approach; nevertheless, its superiority to LS stabilization is not clear yet. Objectives: The aim of the present study was to conduct a review of the literature to find evidence supporting SS or LS posterior
Energy-Efﬁcient Resource Allocation for D2D Communications Underlaying Cloud-RAN-Based LTE-A Networks

— Device-to-device (D2D) communication is a key enabler to facilitate the realization of the Internet of Things (IoT). In this paper, we study the deployment of D2D communications as an underlay to long-term evolution-advanced (LTE-A) networks based on novel architectures such as cloud radio access network (C-RAN). The challenge is that both energy efﬁciency (EE) and quality of service (QoS) are severely degraded by the strong intracell and intercell interference due to dense deployment and spectrum reuse. To tackle this problem, we propose an energy-efﬁcient resource allocation algorithm through joint channel selection and power allocation design. The proposed algorithm has a hybrid structure that exploits the hybrid architecture of C-RAN: distributed remote radio heads (RRHs) and centralized baseband unit (BBU) pool. The distributed resource allocation problem is modeled as a noncooperative game, and each player optimizes its EE individually with the aid of distributed RRHs. We transform the nonconvex optimization problem into a convex one by applying constraint relaxation and nonlinear fractional programming. We propose a centralized interference mitigation algorithm to improve the QoS performance. The centralized algo-rithm consists of an interference cancellation technique and a transmission power constraint optimization technique, both of which are carried out in the centralized BBU pool. The achievable performance of the proposed algorithm is analyzed through simulations, and the implementation issues and complexity analysis are discussed in detail.
Purple Auroral Rays and Global Pc1 Pulsations Observed at the CIR‐Associated Solar Wind Density Enhancement on 21 March 2017

This paper reports two unique auroral features: postmidnight purple auroral rays and global Pc1 geomagnetic pulsations, observed before the onset of the corotating interaction region (CIR) storm of 21 March 2017, at the beginning of the first campaign of the new Particles and Waves in the Inner magnetosphere using Ground‐based network observation (PWING) longitudinal ground network with the Arase satellite. The purple auroral rays were observed from ~0315 to 0430 UT (~03–04 magnetic local time) in the northeastern sky at Husafell, Iceland (magnetic latitude: 64.9°N). We newly propose that the entry of high‐density CIR plasma into the magnetotail created purple auroral rays in the sunlit ionosphere. Pc1 geomagnetic pulsations at frequencies of 0–0.5 Hz were observed after ~00 UT over a wide local time range, of 13 hr, from midnight to afternoon sectors at subauroral latitudes associated with CIR arrival. These results indicate preconditioning of the magnetosphere due to crossing of a CIR.
Critical Study of Supervised Learning Techniques in Predicting Attacks

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. ABSTRACT This paper presents a study about the use of some supervised learning techniques to predict intrusions. The aim of the research is to analyze the performances of such techniques to determine which one best addresses the intrusion detection problem. The performances of six machine learning algorithms involving C4.5, ID3, Classification and Regression Tree (CART), Multinomial Logistic Regression (MLR), Bayesian Networks (BN), and CN2 rule-based algorithm are investigated. The " boosting-arcing " concept was used to obtain a better prediction model while executing a machine learning method. KDD'99 data sets were used to evaluate the considered algorithms. For these evaluations, three cases were considered: the whole attacks case, the five behaviors classes' case, and the two behaviors classes' case. The performances of each technique were compared, and simulations showed that our approach is very competitive with some previous works.
EEGLAB: an open source toolbox for analysis of single - trial EEG dynamics

A thermal polymerization of monomeric materials in a porous workpiece such as wood is carried out using a temperature-controlling or heat-carrying agent. The controlled heat supply for developing the threshold temperature for an exothermic reaction or for supplying the heat for an endothermic chemical reaction and for removal of excess heat in exothermic reactions is effected by water whose addition in small quantities is not detrimental to the workpiece or the reactions.
Forgot Your Password : Randomness Attacks Against PHP Applications

We provide a number of practical techniques and algorithms for exploiting randomness vulnerabilities in PHP applications.We focus on the predictability of password reset tokens and demonstrate how an attacker can take over user accounts in a web application via predicting or algorithmically derandomizing the PHP core randomness generators. While our techniques are designed for the PHP language, the principles behind our techniques and our algorithms are independent of PHP and can readily apply to any system that utilizes weak randomness generators or low entropy sources. Our results include: algorithms that reduce the entropy of time variables, identifying and exploiting vulnerabilities of the PHP system that enable the recovery or reconstruction of PRNG seeds, an experimental analysis of the H̊astad-Shamir framework for breaking truncated linear variables, an optimized online Gaussian solver for large sparse linear systems, and an algorithm for recovering the state of the Mersenne twister generator from any level of truncation. We demonstrate the gravity of our attacks via a number of case studies. Specifically, we show that a number of current widely used web applications can be broken using our techniques including Mediawiki, Joomla, Gallery, osCommerce and others.
An assessment of upper troposphere and lower stratosphere water vapor in MERRA, MERRA2, and ECMWF reanalyses using Aura MLS observations

Global water vapor (H2O) measurements from Microwave Limb Sounder (MLS) are used to evaluate upper tropospheric (UT) and lower stratospheric (LS) H2O products produced by NASA Modern‐Era Retrospective Analysis for Research and Applications (MERRA), its newest release MERRA2, and European Centre for Medium‐Range Weather Forecasts (ECMWF) Interim Reanalyses. Focusing on the H2O amount and transport from UT to LS, we show that all reanalyses overestimate annual global mean UT H2O by up to ~150% compared to MLS observations. Substantial differences in H2O transport are also found between the observations and reanalyses. Vertically, H2O transport across the tropical tropopause (16–20 km) in the reanalyses is faster by up to ~86% compared to MLS observations. In the tropical LS (21–25 km), the mean vertical transport from ECMWF is 168% faster than the MLS estimate, while MERRA and MERRA2 have vertical transport velocities within 10% of MLS values. Horizontally at 100 hPa, both observation and reanalyses show faster poleward transport in the Northern Hemisphere (NH) than in the Southern Hemisphere (SH). Compared to MLS observations, the H2O horizontal transport for both MERRA and MERRA2 is 106% faster in the NH but about 42–45% slower in the SH. ECMWF horizontal transport is 16% faster than MLS observations in both hemispheres. The ratio of northward to southward transport velocities for ECMWF is 1.4, which agrees with MLS observation, while the corresponding ratios for MERRA and MERRA2 are about 3.5 times larger.
Fractional Binding in Vector Symbolic Architectures as Quasi-Probability Statements

Distributed vector representations are a key bridging point be- tween connectionist and symbolic representations of cognition. It is unclear how uncertainty should be modelled in sys- tems using such representations. One may place vector-valued distributions over vector representations, although that may as- sign non-zero probabilities to vector symbols that cannot oc-cur. In this paper we discuss how bundles of symbols in Vector Symbolic Architectures (VSAs) can be understood as defin-ing an object that has a relationship to a probability distribu- tion, and how statements in VSAs can be understood as being analogous to probabilistic statements. We sketch novel designs for networks that compute entropy and mutual information of VSA-represented distributions. In this paper we restrict our- selves to operators proposed for Holographic Reduced Representations, and representing real-valued data. However, we suggest that the methods presented in this paper should translate to any VSA where the dot product between fractionally bound symbols induces a valid kernel.
Timing of Exposure to High-Concentrates versus High-Quality Forages on Growth and Marbling Deposition in Steers

Forty Angus-cross steers (280 ± 21.4 kg BW, 8 mo.) were used to examine the effects of exposure to 2 diets [high concentrate diets (CONC) versus high quality forages (FOR)] during 2 time periods [early (EARLY; at 30-d post weaning) or late (LATE; just prior to slaughter)] on animal growth, marbling deposition and tenderness. Steers were blocked by weight and randomly assigned to four dietary treatments: 1) CONC-FOR, 2) CONC-CONC, 3) FOR-CONC, or 4) FOR-FOR. Exposure to CONC during the EARLY or LATE period increased (P < 0.05) growth and fat deposition compared to FOR-FOR. Hot carcass weight was greater (P < 0.05) for CONC-CONC and FOR-CONC steers than FOR-FOR and CONC-FOR due to changes in dressing percent. Marbling score was greater (P < 0.05) for CONC-CONC and CONC-FOR compared to FOR-FOR. Exposure to CONC during the EARLY period (CONC-FOR and CONC-CONC) increased (P < 0.05) n-6 polyunsaturated fatty acids (PUFA) deposition in longissimus muscle (LM) and subcutaneous adipose tissue (SQ); whereas, exposure to CONC during the LATE period (CONC-CONC and FOR-CONC) reduced (P < 0.05) n-3 PUFA, trans-11 octadecenoic acid and cis-9 trans-11 isomer of conjugated linoleic acid (CLA). Warner-Bratzler shear force at d 2 and 7 of postmortem aging in ribeye steaks from CONC-CONC and FOR-CONC was greater (P < 0.05) than FOR-FOR and CONC-FOR. Lipogenic gene expression was up-regulated (P < 0.05) and lipolytic gene expression was downregulated (P < 0.06) in SQ from CONC-CONC and FOR-CONC compared to FOR-FOR. Overall, exposure to CONC in both periods increased growth rate and marbling deposition but LATE exposure had the greatest impact on adipose lipogenesis and lipolysis, fatty acid composition, and tenderness.