Unconscious processing of invisible visual stimuli

Unconscious processing of subliminal visual information, as illustrated by the above-chance accuracy in discriminating invisible visual stimuli, is evident in both blindsight patients and healthy human observers. However, the dependence of such unconscious processing on stimulus properties remains unclear. Here we studied the impact of stimulus luminance and stimulus complexity on the extent of unconscious processing. A testing stimulus presented to one eye was rendered invisible by a masking stimulus presented to the other eye, and healthy human participants made a forced-choice discrimination of the stimulus identity followed by a report of the perceptual awareness. Without awareness of the stimulus existence, participants could nevertheless reach above-chance accuracy in discriminating the stimulus identity. Importantly, the discrimination accuracy for invisible stimuli increased with the stimulus luminance and decreased with the stimulus complexity. These findings suggested that the input signal strength and the input signal complexity can affect the extent of unconscious processing without altering the subjective awareness

Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices

We study the streaming model for approximate matrix multiplication (AMM). We are interested in the scenario that the algorithm can only take one pass over the data with limited memory. The state-of-the-art deterministic sketching algorithm for streaming AMM is the co-occurring directions (COD), which has much smaller approximation errors than randomized algorithms and outperforms other deterministic sketching methods empirically. In this paper, we provide a tighter error bound for COD whose leading term considers the potential approximate low-rank structure and the correlation of input matrices. We prove COD is space optimal with respect to our improved error bound. We also propose a variant of COD for sparse matrices with theoretical guarantees. The experiments on real-world sparse datasets show that the proposed algorithm is more efficient than baseline methods

Temporal and spatial variability of temperature and precipitationover East Africa from 1951 to 2010

This study presents temporal and spatial changes in temperature and precipitation over East Africa (EA) from 1951 to 2010. The study utilized monthly Climate Research Unit (CRU) rainfall and temperature datasets, and Global Precipitation Climate Centre (GPCC) rainfall datasets. Sequential Mann–Kendall test statistic was used for trend analysis. The CRU data performs better than GPCC data in reproducing EA annual rainfall cycle. Overall decrease and increase in rainfall and temperature trends were observed, respectively, with the reduction in the March–May rainfall being significant. The highest rate of change in annual rainfall was experienced in the 1960s at −21.76 mm/year. Although there has been increase in temperature from the late 1960s to date, sudden change in its trend change happened in 1994. The increase in temperature reached a significant level in the year 1992. The highest warming rate of 0.05 °C/year was observed in the 1990s. The highest drying rate was recorded in the 1960s at −21.76 mm/year. There was an observed change in rainfall trend in the year 1953 and about four times in 1980, although the changes are insignificant throughout the study period except for 1963 when a positive significant change occurred at 5 % significance level. The highest amount of rainfall was recorded in the 1960s. Generally, positive rainfall and temperature anomalies are observed over the northern sector of the study area and opposite conditions are noted in the southern sector. The results of this study provide a reliable basis for future climate monitoring, as well as investigating extreme weather phenomena in EA

MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding

Online real-time bidding (RTB) is known as a complex auction game where ad platforms seek to consider various influential key performance indicators (KPIs), like revenue and return on investment (ROI). The trade-off among these competing goals needs to be balanced on a massive scale. To address the problem, we propose a multi-objective reinforcement learning algorithm, named MoTiAC, for the problem of bidding optimization with various goals. Specifically, in MoTiAC, instead of using a fixed and linear combination of multiple objectives, we compute adaptive weights overtime on the basis of how well the current state agrees with the agent's prior. In addition, we provide interesting properties of model updating and further prove that Pareto optimality could be guaranteed. We demonstrate the effectiveness of our method on a real-world commercial dataset. Experiments show that the model outperforms all state-of-the-art baselines.Comment: 8 Pages, Extensive Experiment

Variability of extreme weather events over the equatorial East Africa, a case study of rainfall in Kenya and Uganda

This study investigates the variability of extreme rainfall events over East Africa (EA), using indices from the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). The analysis was based on observed daily rainfall from 23 weather stations, with length varying within 1961 and 2010. The indices considered are: wet days (R ≥1 mm), annual total precipitation in wet days (PRCPTOT), simple daily intensity index (SDII), heavy precipitation days (R ≥ 10 mm), very heavy precipitation days (R ≥ 20 mm), and severe precipitation (R ≥ 50 mm). The non-parametric Mann-Kendall statistical analysis was carried out to identify trends in the data. Temporal precipitation distribution was different from station to station. Almost all indices considered are decreasing with time. The analysis shows that the PRCPTOT, very heavy precipitation, and severe precipitation are generally declining insignificantly at 5 % significant level. The PRCPTOT is evidently decreasing over Arid and Semi-Arid Land (ASAL) as compared to other parts of EA. The number of days that recorded heavy rainfall is generally decreasing but starts to rise in the last decade although the changes are insignificant. Both PRCPTOT and heavy precipitation show a recovery in trend starting in the 1990s. The SDII shows a reduction in most areas, especially the in ASAL. The changes give a possible indication of the ongoing climate variability and change which modify the rainfall regime of EA. The results form a basis for further research, utilizing longer datasets over the entire region to reduce the generalizations made herein. Continuous monitoring of extreme events in EA is critical, given that rainfall is projected to increase in the twenty-first century

Evaluation of CMIP5 twentieth century rainfall simulation over the equatorial East Africa

This study assesses the performance of 22 Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations of rainfall over East Africa (EA) against reanalyzed datasets during 1951–2005. The datasets were sourced from Global Precipitation Climatology Centre (GPCC) and Climate Research Unit (CRU). The metrics used to rank CMIP5 Global Circulation Models (GCMs) based on their performance in reproducing the observed rainfall include correlation coefficient, standard deviation, bias, percentage bias, root mean square error, and trend. Performances of individual models vary widely. The overall performance of the models over EA is generally low. The models reproduce the observed bimodal rainfall over EA. However, majority of them overestimate and underestimate the October–December (OND) and March–May (MAM) rainfall, respectively. The monthly (inter-annual) correlation between model and reanalyzed is high (low). More than a third of the models show a positive bias of the annual rainfall. High standard deviation in rainfall is recorded in the Lake Victoria Basin, central Kenya, and eastern Tanzania. A number of models reproduce the spatial standard deviation of rainfall during MAM season as compared to OND. The top eight models that produce rainfall over EA relatively well are as follows: CanESM2, CESM1-CAM5, CMCC-CESM, CNRM-CM5, CSIRO-Mk3-6-0, EC-EARTH, INMCM4, and MICROC5. Although these results form a fairly good basis for selection of GCMs for carrying out climate projections and downscaling over EA, it is evident that there is still need for critical improvement in rainfall-related processes in the models assessed. Therefore, climate users are advised to use the projections of rainfall from CMIP5 models over EA cautiously when making decisions on adaptation to or mitigation of climate change

Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold

The conjugate gradient method is a crucial first-order optimization method that generally converges faster than the steepest descent method, and its computational cost is much lower than the second-order methods. However, while various types of conjugate gradient methods have been studied in Euclidean spaces and on Riemannian manifolds, there has little study for those in distributed scenarios. This paper proposes a decentralized Riemannian conjugate gradient descent (DRCGD) method that aims at minimizing a global function over the Stiefel manifold. The optimization problem is distributed among a network of agents, where each agent is associated with a local function, and communication between agents occurs over an undirected connected graph. Since the Stiefel manifold is a non-convex set, a global function is represented as a finite sum of possibly non-convex (but smooth) local functions. The proposed method is free from expensive Riemannian geometric operations such as retractions, exponential maps, and vector transports, thereby reducing the computational complexity required by each agent. To the best of our knowledge, DRCGD is the first decentralized Riemannian conjugate gradient algorithm to achieve global convergence over the Stiefel manifold