Miracle-dz/UniFAR-FTUs huggingface dataset:
Running head: DIFFUSION MODEL PARAMETERS AND BEHAVIORAL VARIABLES

1 The diffusion model belongs to the class of sequential sampling models. Based on 2 information about RT distributions from binary tasks, the model allows the separation of 3 distinct processes underlying decision-making. With the present simulation study, we 4 demonstrate that diffusion modeling has essential advantages over the mere analysis of 5 behavioral variables such as accuracy rate or mean RT. Specifically, our results show that 6 qualitatively different sets of diffusion model parameters lead to the same pattern of these 7 aggregated variables, making interpretation ambiguous. Even apparently clear patterns of 8 results can be the result of different parameter sets. For example, if one group’s performance 9 (or performance in one experimental condition) is superior in both mean RT (i.e., faster) and 10 accuracy rate (i.e., fewer errors), this does not necessarily mean that in this group (or 11 condition) information processing is faster (drift rate of the diffusion model). Accordingly, 12 we conclude that the mere analysis of behavioral variables can result in false conclusions. 13 14
Variational Fisheye Stereo

Dense 3D maps from wide-angle cameras is beneficial to robotics applications such as navigation and autonomous driving. In this work, we propose a real-time dense 3D mapping method for fisheye cameras without explicit rectification and undistortion. We extend the conventional variational stereo method by constraining the correspondence search along the epipolar curve using a trajectory field induced by camera motion. We also propose a fast way of generating the trajectory field without increasing the processing time compared to conventional rectified methods. With our implementation, we were able to achieve real-time processing using modern GPUs. Our results show the advantages of our non-rectified dense mapping approach compared to rectified variational methods and non-rectified discrete stereo matching methods.
A Neuromorphic Device for Detecting High-Frequency Oscillations in Human iEEG

Among diagnostic biomarkers, high frequency oscillations in human iEEG are used to identify epileptogenic brain tissue during epilepsy surgery. However, current methods typically analyse the raw data offline using complex time-consuming algorithms. We developed a compact neuromorphic sensory-processing system-on-chip that can monitor the iEEG signals and detect high frequency oscillations in real-time using spiking neural networks. To this end, we present an integrated device with an analog front-end that can extract predefined spectral features and encode them as address-events, and a neuromorphic processor core that implements a network of integrate and fire neurons with dynamic synapses. The device was fabricated using a standard $0.18\mu\mathrm{m}$ CMOS technology node. The estimated power consumption of the front-end is $6.2\mu\mathrm{W}$/channel and the area-on-chip for a single channel is 0.15 square millimetres. The SNN classifier provides 90.5% sensitivity and 67.7% specificity for detecting high frequency oscillations. This is the first feasibility study towards identifying relevant features in intracranial human data in real-time on-chip using event-base processors.
DISE: A Distributed in-Memory SPARQL Processing Engine over Tensor Data

SPARQL is a W3C standard for querying the data stored as Resource Description Framework (RDF). The SPARQL queries are represented using triple-patterns, and the querying process searches for these patterns in given RDF. Most of the existing SPARQL evaluators provide centralized, DBMS inspired solutions consuming high resources and offering limited flexibility. To deal with the increasing size of RDF data, it is important to develop scalable and efficient solutions for distributed SPARQL query evaluation. In this paper, we present DISE – an open-source implementation of distributed in-memory SPARQL engine that can scale out to a cluster of machines. DISE represents the RDF graph as a three-way distributed tensor for querying large-scale RDF datasets. This distributed tensor representation offers opportunities for novel distributed applications. DISE translates the SPARQL queries into Spark-tensor operations by exploiting the information about the query complexity and creating a dynamic execution plan. We have tested the scalability and efficiency of DISE on different datasets. The results for this new representation based querying have been found scalable, efficient and comparable to a related approach.
Identification of the Risk Factor for Colorectal Cancer in Females in Lahore

The main aim of this study was to investigate the risk factors of Colorectal cancer in Lahore by using the Logistic model. It was aCross-sectional case control study. For this study, the information was collected from three public hospitals of Lahore including ANMOL, General Hospital and Shaikh Zayed. The required information consisting of 124 female subjects including 62 cases and 62 controls was obtained from January 2011 to May 2012. The factors were studied descriptively and analytically. By using the Logistic Regression model, Odds Ratios and 95% confidence intervals were obtained. Five factors including age, residential area, lifestyle, constipation, and fruit were found to be significant. The Odds Ratios and 95% confidence intervals of these factors were [1.107, 1.0511. 167], [3.541, 1.040-12.052], [0.136, 0.037-0.499], [10.669, 3.031-67.553], and [0.169, 0.051-0.559], respectively. The constipation was observed to be the major risk factor of Colorectal cancer in females. The factors age, residential area and constipation were found to be directly related to Colorectal cancer. On the other hand, the lifestyle and fruit were observed to be protective factors against the Colorectal cancer.
Efficiency for Lives, Equality for Everything Else

The allocation of scarce public resources such as transplant organs and limited public funding involves a trade-off between equality—equal access and efficiency—maximizing total benefit. The current research explores how preferences shift when allocation decisions involve human lives versus when they do not. Fifteen experiments test this question using a variety of allocation scenarios including allocation of lifesaving medical aid, money, road construction, vaccines, and other resources. The results consistently show an increased preference for efficiency, when the allocation involves saving human lives, and equality, when the allocation involves outcomes with other consequences. We found no preference shift when stakes were manipulated in allocations where lives were not on the line, suggesting that the effect cannot be explained by lifesaving resources simply being higher stakes. These findings suggest a unique preference for efficiency for allocations involving life-and-death consequences that has implications for designing and conveying public resource allocation policies.
Control of Saccades with Model of Artificial 3D Biomimetic Eye

The human gaze control system is more complex than any robotic visual system ever built. One of its main challenges is dealing with the degrees of freedom problem: the six extra-ocular muscles provide the eye three degrees of freedom to rotate, while only two are necessary to gaze at any direction, controlling movements in a plane called Listing’s plane (LP). In this plane, the torsion of the eye is zero. Another distinctive trait of the saccadic system are its nonlinear dynamic properties, known as the main sequence. In this thesis we develop open-loop optimal control strategies that satisfy both criteria, finding the general optimization principles that unify them. These policies are tested on a model of a 3D biomimetic robot eye, whose kinematics were obtained using physics modelling of a previously built mechanical prototype. System identification procedures were employed to obtain a linear approximation of the nonlinear simulator within a small ocular range of the straight-ahead orientation. First, simpler approaches were tested, employing only minimization terms commonly used in literature. Even though the nonlinear dynamics were verified, the standard deviation of the torsion of the eye around LP (1.8◦) was higher than real data show (0.6◦). Then, the addition of extra cost terms (direct penalizations or minimization of the force) was studied, leading to the reduction of the standard deviation to 0.4◦. Finally, the dependency of the orientation of LP to the geometry of the model was analysed.
Overview of TAC-KBP 2019 Fine-grained Entity Extraction

In the past several years TAC KBP Entity Discovery and Linking (EDL) track has only focused on five major coarse-grained entity types: person (PER), geo-political entity (GPE), location (LOC), organization (ORG) and facility (FAC). However, many real-world applications in scenarios such as disaster relief and technical support require us to significantly extend EDL capabilities to a wider variety of fine-grained entity types (e.g., technical terms, lawsuits, disease, crisis, vehicles, food, biomedical entities). In this overview paper we give an outline of the Ultra-Fine-Grained Name Tagging for Entity Types task (along with system participation) at the Knowledge Base Population (KBP) track at TAC 2019. We will also sketch out remaining challenges and future research directions.
Documentos de Trabajo Elasticity of cigarette demand in Argentina: An empirical analysis using vector error-correction model

Objective: To estimate empirically the short and long-term eff ects on cigarette demand in Argentina based on changes in cigarette price and income. Method: We analyzed data from the Ministry of Economy and P roduction of Argentina. Analysis was based on monthly time-series data between 1994 and 2004. The econometrics specification is a linear double-logarithmic form using cigarettes consumptio n per person older than 14 y. as dependent variable and real income per person older than 14 y . and the real average price of cigarettes sales as independent variables. Empirical analyses were done in three steps: 1) To verify the order of integration of the variables using the augmented Di ckey-Fuller test; 2) To test for co-integration usi ng the Johansen-Juselius maximum likelihood approach t o apture the long-term effects; and 3) To utilize the Vector error-correction model to capture the sh ort-run dynamics of the variables. Results: The empirical results showed that in the long-term period the demand for cigarettes in Argentina is affected by changes in real income and real average price of cigarettes. The value of income elasticity is equal to 0.54 while the value of own-price elasticity is equal to –0.34. The results using vector error-correction model est imation suggest that the short-term cigarette demand in Argentina is independent of price (not st atistically significant). The value of the short-te rm income elasticity is equal to 0.49. A simulation exercise show that increasing the pric es in a 120% we can obtain a maximum of revenues from cigarette tax and obtain also a big i mpact in the fall of the total consumption of cigarettes in the country.
Energy Efficiency and the New Zealand Economy: A Dynamic CGE Analysis

This paper develops a dynamic CGE model of the New Zealand economy to analyze the impact of energy efficiency improvements and other energy shocks. The dynamic CGE model focuses on the energy sector, which is modeled using a technological bundle approach and a bottom-up framework. Alternative energy efficient policies and energy shocks are simulated. The model and results are useful for energy policy analysis including policy development to maximize the potential benefits of advancing to a more energy efficient economy.
Generalized fractional maximal operator on generalized local Morrey spaces

In this paper, we study the boundedness of generalized fractional maximal operator on generalized local Morrey spaces and generalized Morrey spaces, including weak estimates. Firstly, we prove the Spanne type boundedness of generalized fractional maximal operator on generalized local Morrey spaces for 1 < p < q < infinity and, on weak generalized local Morrey spaces for p = 1 and 1 < q < infinity . Secondly, we prove the Adams type boundedness of generalized fractional maximal operator on generalized local Morrey spaces for 1 < p < q < \infinity and, on weak generalized local Morrey spaces for p = 1 and 1 < q < \ infinity . In all cases the conditions for the boundedness of generalized fractional maximal operators are given in terms of supremal- type integral inequalities on (\varphi_1; \varphi_ 2; \rho) and ( \varphi ; \rho), which do not assume any assumption on monotonicity of \varphi_ 1(x; r), \varphi_ 2(x; r) and \varphi (x; r) in r.
LAY-UP FACTORS FOR WOOD-BASED CROSS-PLY LAMINATES

The objective of this paper is to introduce a general approach resulting in lay-up factors for wood-based panels such as Cross Laminated Timber, multi-ply Solid Wood Panels, cross-banded Laminated Veneer Lumber and Plywood. Layup factors quantify the effect of the laminate construction on stiffness and strength properties of such wood-based panels and map these on a scale from 0 to 1. After the discussion of some application examples of lay-up factors on the mentioned wood-based panels and a brief survey of the scarce literature on this subject, the derivation of lay-up factors on the basis of a strain energy ratio is shown. Lay-up factors are given for uniaxial tensile or compressive loading as well as for in-plane and out-of-plane bending. For the transverse out-of-plane bending strength, which so far has only been considered by a linear approximation, exact lay-up factors are provided. Lay-up factors that have not yet been specified are derived for inplane shear as well as for combined out-of-plane bending and in-plane shear. Generally applicable and simplified lay-up factors that only apply under certain assumptions are indicated separately for each type of loading mentioned. The proposed strain energy ratio may also be applied to setting up lay-up factors for hybrid laminates, asymmetric laminate constructions and other combined stress states.
Progress in Prison Substance Abuse Treatment: A Five Year Report

Within the recent years there has been a paradigmatic shift in the direction of correctional policy — a movement away from an exclusive concern with security and control toward a more comprehensive approach that includes rehabilitation and treatment. Looking for ways to reduce recidivism and to control overcrowding (and recognizing the close connection between substance abuse and crime), correctional authorities have begun expanding prison-based drug treatment programs. This movement toward a corrections-treatment perspective has been based upon a growing body of research that has indicated that intensive prison-based drug treatment programs are an effective means of controlling recidivism. Progress in prison substance abuse treatment that has transpired over the last five years is described, along with a brief description of correctional substance abuse treatment approaches. Recommendations for the future of correctional drug treatment and some critical cautions are presented as well.
1 OPTIMAL TIMING FOR WIND FARM MAINTENANCE : AN APPLICATION AND EMPIRICAL EVIDENCE

Wind farms must periodically take their turbines offline in order to perform scheduled maintenance repairs. Since this interruption impacts the generation of energy and any shortfall in production must be covered by energy purchases in the spot market, determining the optimal time to begin maintenance work in a wind farm is a function of both the expected wind speeds and electricity spot prices. In this article we develop a model to determine the optimal maintenance schedule in a wind farm based on forecasted wind speeds and energy prices. We analyze a window of opportunity in the most likely period of the year, and perform weekly updates of expected wind speeds and energy price forecasts. Wind speeds are forecasted with an (ARMAX) model, where monthly dummies are used as exogenous variables to capture the seasonality of wind speeds, while spot prices are simulated under the Newave dual stochastic programing model. The decision to defer maintenance to a future date is modeled as an American real option. We test the model with actual data from a wind farm in the Brazilian Northeast, and compare our results with current practice and with maintenance scheduling considering perfect information in order to determine the benefits of the model. The results suggest that this model may provide significant advantages over a stopping decision that randomly chooses a week to begin maintenance within the opportunity window and is close to the optimal stopping date considering perfect information on future wind speeds and electricity prices.
Quasilinear Rental Harmony

Rental Harmony is the problem of assigning rooms in a rented house to tenants with different preferences, and simultaneously splitting the rent among them, such that no tenant envies the bundle (room+price) given to another tenant. Different papers have studied this problem under two incompatible assumptions: the miserly tenants assumption is that each tenant prefers a free room to a non-free room; the quasilinear tenants assumption is that each tenant attributes a monetary value to each room, and prefers a room of which the difference between value and price is maximum. This note shows how to adapt the main technique used for rental harmony with miserly tenants, using a variant of Sperner's lemma, to rental harmony with quasilinear tenants. This implies that some recent results derived for miserly tenants apply to quasilinear tenants too. Moreover, the proof is valid even for some classes of non-linear preferences.
Fast Inference Procedures for Semivarying Coefficient Models via Local Averaging

The semivarying coefficient models are widely used in the application of finance, economics, medical science and many other areas. The functional coefficients are commonly estimated by local smoothing methods, e.g. local linear estimator. This implies that one should implement the estimation procedure for hundreds of times to obtain an estimate of one function. So the computation cost is very severe. In this paper, we give an insight to the trade-off between statistical efficiency and computation simplicity, and proposes a fast inference procedure for semivarying coefficient model. In our method, the coefficient functions are approximated by piecewise constants, which is a simple and rough approximation. This makes our estimators easy to implement and avoid repeat estimation. In this work, we shall show that though these estimators are not asymptotically optimal, they are efficient enough for building further inference procedure. Furthermore, three tests are brought out to check whether certain coefficient is constant. Our results clearly show that when the room for improving the asymptotic efficiency is limited, a proper trade-off between statistical efficiency and computation simplicity can be taken into consideration to improve the performance of the inference procedure.
Video Depth Estimation by Fusing Flow-to-Depth Proposals

Depth from a monocular video can enable billions of devices and robots with a single camera to see the world in 3D. In this paper, we present a model for video depth estimation, which consists of a flow-to-depth layer, a camera pose refinement module, and a depth fusion network. Given optical flow and camera poses, our flow-to-depth layer generates depth proposals and their corresponding confidence maps by explicitly solving an epipolar geometry optimization problem. Our flow-to-depth layer is differentiable, and thus we can refine camera poses by maximizing the aggregated confidence in the camera pose refinement module. Our depth fusion network can utilize the target frame, depth proposals, and confidence maps inferred from different neighboring frames to produce the final depth map. Furthermore, the depth fusion network can additionally take the depth proposals generated by other methods to further improve the results. The experiments on three public datasets show that our approach outperforms state-of-the-art depth estimation methods, and has reasonable crossdataset generalization ability: our model trained on KITTI still performs well on the unseen Waymo dataset.
Active Learning in Video Tracking

Active learning methods, like uncertainty sampling, combined with probabilistic prediction techniques have achieved success in various problems like image classification and text classification. For more complex multivariate prediction tasks, the relationships between labels play an important role in designing structured classifiers with better performance. However, computational time complexity limits prevalent probabilistic methods from effectively supporting active learning. Specifically, while non-probabilistic methods based on structured support vector machines can be tractably applied to predicting bipartite matchings, conditional random fields are intractable for these structures. We propose an adversarial approach for active learning with structured prediction domains that is tractable for matching. We evaluate this approach algorithmically in an important structured prediction problems: object tracking in videos. We demonstrate better accuracy and computational efficiency for our proposed method.
A Genetic Algorithm based Kernel-size Selection Approach for a Multi-column Convolutional Neural Network

Deep neural network-based architectures give promising results in various domains including pattern recognition. Finding the optimal combination of the hyper-parameters of such a large-sized architecture is tedious and requires a large number of laboratory experiments. But, identifying the optimal combination of a hyper-parameter or appropriate kernel size for a given architecture of deep learning is always a challenging and tedious task. Here, we introduced a genetic algorithm-based technique to reduce the efforts of finding the optimal combination of a hyper-parameter (kernel size) of a convolutional neural network-based architecture. The method is evaluated on three popular datasets of different handwritten Bangla characters and digits. The implementation of the proposed methodology can be found in the following link: this https URL.