History of one’s own performance modulates evaluative processing of another’s action outcomes, but not vice versa

In action monitoring, i.e., evaluating an outcome of our behavior, a reward prediction error signal is calculated as the difference between actual and predicted outcomes and is used to adjust future behavior. Previous studies demonstrate that this signal, which is reflected by an event-related brain potential called feedback-related negativity (FRN), occurs in response to not only one's own outcomes, but also those of others. However, it is still unknown if predictions of different actors' performance interact with each other. Thus, we investigated how predictions from one’s own and another’s performance history affect each other by manipulating the task difficulty for participants themselves and their partners independently. Pairs of participants performed a time estimation task, randomly switching the roles of actor and observer from trial to trial. Results show that the history of the other’s performance did not modulate the amplitude of the FRN for the evaluation of one’s own outcomes. In contrast, the amplitude of the observer FRN for the other’s outcomes differed according to the frequency of one’s own action outcomes. In conclusion, the monitoring system tracks the histories of one’s own and observed outcomes separately and considers information related to one’s own action outcomes to be more important.

DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation

We propose Depth-to-Space Net (DTS-Net), an effective technique for semantic segmentation using the efficient sub-pixel convolutional neural network. This technique is inspired by depth-to-space (DTS) image reconstruction, which was originally used for image and video super-resolution tasks, combined with a mask enhancement filtration technique based on multi-label classification, namely, Nearest Label Filtration. In the proposed technique, we employ depth-wise separable convolution-based architectures. We propose both a deep network, that is, DTS-Net, and a lightweight network, DTS-Net-Lite, for real-time semantic segmentation; these networks employ Xception and MobileNetV2 architectures as the feature extractors, respectively. In addition, we explore the joint semantic segmentation and depth estimation task and demonstrate that the proposed technique can efficiently perform both tasks simultaneously, outperforming state-of-art (SOTA) methods. We train and evaluate the performance of the proposed method on the PASCAL VOC2012, NYUV2, and CITYSCAPES benchmarks. Hence, we obtain high mean intersection over union (mIOU) and mean pixel accuracy (Pix.acc.) values using simple and lightweight convolutional neural network architectures of the developed networks. Notably, the proposed method outperforms SOTA methods that depend on encoder–decoder architectures, although our implementation and computations are far simpler.

A Data Science Approach to Cost Estimation Decision Making - Big Data and Machine Learning

Cost estimation may become increasingly difficult, slow, and resource-consuming when it cannot be performed analytically. If traditional cost estimation techniques are usable at all under those circumstances, they have important limitations. This article analyses the potential applications of data science to management accounting, through the case of a cost estimation task posted on Kaggle, a Google data science and machine learning website. When extensive data exist, machine learning techniques can overcome some of those limitations. Applying machine learning to the data reveals non-obvious patterns and relationships that can be used to predict costs of new assemblies with acceptable accuracy. This article discusses the advantages and limitations of this approach and its potential to transform cost estimation, and more widely management accounting. The multinational company Caterpillar posted a contest on Kaggle to estimate the price that a supplier would quote for manufacturing a number of industrial assemblies, given historical quotes for similar assemblies. Hitherto, this problem would have required reverse-engineering the supplier’s accounting structure to establish the cost structure of each assembly, identifying non-obvious relationships among variables. This complex and tedious task is usually performed by human experts, adding subjectivity to the process. La estimación de costes puede resultar cada vez más difícil, lenta y consumidora de recursos cuando no puede realizarse de forma analítica. Cuando las técnicas tradicionales de estimación de costes son utilizadas en esas circunstancias se presentan importantes limitaciones. Este artículo analiza las posibles aplicaciones de la ciencia de datos a la contabilidad de gestión, a través del caso de una tarea de estimación de costes publicada en Kaggle, un sitio web de ciencia de datos y aprendizaje automático de Google. Cuando existen muchos datos, las técnicas de aprendizaje automático pueden superar algunas de esas limitaciones. La aplicación del aprendizaje automático a los datos revela patrones y relaciones no evidentes que pueden utilizarse para predecir los costes de nuevos montajes con una precisión aceptable. En nuestra investigación se analizan las ventajas y limitaciones de este enfoque y su potencial para transformar la estimación de costes y, más ampliamente, la contabilidad de gestión. La multinacional Caterpillar publicó un concurso en Kaggle para estimar el precio que un proveedor ofrecería por la fabricación de una serie de conjuntos industriales, dados los presupuestos históricos de conjuntos similares. Hasta ahora, este problema habría requerido una ingeniería inversa de la estructura contable del proveedor para establecer la estructura de costes de cada ensamblaje, identificando relaciones no obvias entre las variables. Esta compleja y tediosa tarea suele ser realizada por expertos humanos, lo que añade subjetividad al proceso.

Time to imagine moving: Simulated motor activity affects time perception

Sensing the passage of time is important for countless daily tasks, yet time perception is easily influenced by perception, cognition, and emotion. Mechanistic accounts of time perception have traditionally regarded time perception as part of central cognition. Since proprioception, action execution, and sensorimotor contingencies also affect time perception, perception-action integration theories suggest motor processes are central to the experience of the passage of time. We investigated whether sensory information and motor activity may interactively affect the perception of the passage of time. Two prospective timing tasks involved timing a visual stimulus display conveying optical flow at increasing or decreasing velocity. While doing the timing tasks, participants were instructed to imagine themselves moving at increasing or decreasing speed, independently of the optical flow. In the direct-estimation task, the duration of the visual display was explicitly judged in seconds while in the motor-timing task, participants were asked to keep a constant pace of tapping. The direct-estimation task showed imagining accelerating movement resulted in relative overestimation of time, or time dilation, while decelerating movement elicited relative underestimation, or time compression. In the motor-timing task, imagined accelerating movement also accelerated tapping speed, replicating the time-dilation effect. The experiments show imagined movement affects time perception, suggesting a causal role of simulated motor activity. We argue that imagined movements and optical flow are integrated by temporal unfolding of sensorimotor contingencies. Consequently, as physical time is relative to spatial motion, so too is perception of time relative to imaginary motion.

Grid Lifespan Enlargement for Assessment in Multihop Wireless Detector Facilities

In energy limited wireless sensor networks, both local quantization andmultihop transmission are essential to save transmission energy and thus prolong the network lifetime. The goal is to maximize the network lifetime, defined as the estimation task cycles accomplished before the network becomes nonfunctional.The network lifetime optimization problem includes three components: Optimizing source coding at each sensor node, optimizing source throughput at each sensor node.Optimizing multihop routing path. Source coding optimization can be decoupled from source throughput and multihop routing path optimization and is solved by introducing a concept of equivalent 1-bit Mean Square Error (MSE) function. Based on optimal source coding, multihop routing path optimization is formulated as a linear programming problem, which suggests a new notion of character based routing. It is also seen that optimal multihop routing improves the network lifetime bound significantly compared with single-hop routing for heterogeneous networks. Furthermore, the gain is more significant when the network is denser since there are more opportunities for multihop routing. Also the gain is more significant when the observation noise variances are more diverse.

A recursive expectation-maximization algorithm for speaker tracking and separation

The problem of blind and online speaker localization and separation using multiple microphones is addressed based on the recursive expectation-maximization (REM) procedure. A two-stage REM-based algorithm is proposed: (1) multi-speaker direction of arrival (DOA) estimation and (2) multi-speaker relative transfer function (RTF) estimation. The DOA estimation task uses only the time frequency (TF) bins dominated by a single speaker while the entire frequency range is not required to accomplish this task. In contrast, the RTF estimation task requires the entire frequency range in order to estimate the RTF for each frequency bin. Accordingly, a different statistical model is used for the two tasks. The first REM model is applied under the assumption that the speech signal is sparse in the TF domain, and utilizes a mixture of Gaussians (MoG) model to identify the TF bins associated with a single dominant speaker. The corresponding DOAs are estimated using these bins. The second REM model is applied under the assumption that the speakers are concurrently active in all TF bins and consequently applies a multichannel Wiener filter (MCWF) to separate the speakers. As a result of the assumption of the concurrent speakers, a more precise TF map of the speakers’ activity is obtained. The RTFs are estimated using the outputs of the MCWF-beamformer (BF), which are constructed using the DOAs obtained in the previous stage. Next, using the linearly constrained minimum variance (LCMV)-BF that utilizes the estimated RTFs, the speech signals are separated. The algorithm is evaluated using real-life scenarios of two speakers. Evaluation of the mean absolute error (MAE) of the estimated DOAs and the separation capabilities, demonstrates significant improvement w.r.t. a baseline DOA estimation and speaker separation algorithm.

The influence of vitality forms on action perception and motor response

During the interaction with others, action, speech, and touches can communicate positive, neutral, or negative attitudes. Offering an apple can be gentle or rude, a caress can be kind or rushed. These subtle aspects of social communication have been named vitality forms by Daniel Stern. Although they characterize all human interactions, to date it is not clear whether vitality forms expressed by an agent may affect the action perception and the motor response of the receiver. To this purpose, we carried out a psychophysics study aiming to investigate how perceiving different vitality forms can influence cognitive and motor tasks performed by participants. In particular, participants were stimulated with requests made through a physical contact or vocally and conveying rude or gentle vitality forms, and then they were asked to estimate the end of a passing action observed in a monitor (action estimation task) or to perform an action in front of it (action execution task) with the intention to pass an object to the other person presented in the video. Results of the action estimation task indicated that the perception of a gentle request increased the duration of a rude action subsequently observed, while the perception of a rude request decreased the duration of the same action performed gently. Additionally, during the action execution task, accordingly with the perceived vitality form, participants modulated their motor response.

Variational quantum algorithms for trace distance and fidelity estimation

Estimating the difference between quantum data is crucial in quantum computing. However, as typical characterizations of quantum data similarity, the trace distance and quantum fidelity are believed to be exponentiallyhard to evaluate in general. In this work, we introduce hybrid quantum-classical algorithms for these two distance measures on near-term quantum devices where no assumption of input state is required. First, we introduce the Variational Trace Distance Estimation (VTDE) algorithm. We in particular provide the technique to extract the desired spectrum information of any Hermitian matrix by local measurement. A novel variational algorithm for trace distance estimation is then derived from this technique, with the assistance of a single ancillary qubit. Notably, VTDE could avoid the barren plateau issue with logarithmic depth circuits due to a local cost function. Second, we introduce the Variational Fidelity Estimation (VFE) algorithm. We combine Uhlmann’s theorem and the freedom in purification to translate the estimation task into an optimization problem over a unitary on an ancillary system with fixed purified inputs. We then provide a purification subroutine to complete the translation. Both algorithms are verified by numerical simulations and experimental implementations, exhibiting high accuracy for randomly generated mixed states.