Product descriptions on e-commerce websites often suffer from missing important aspects. Clarification question generation (CQGen) can be a promising approach to help alleviate the problem. Unlike traditional QGen assuming the existence of answers in the context and generating questions accordingly, CQGen mimics user behaviors of asking for unstated information. The generated CQs can serve as a sanity check or proofreading to help e-commerce merchant to identify potential missing information before advertising their product, and improve consumer experience consequently. Due to the variety of possible user backgrounds and use cases, the information need can be quite diverse but also specific to a detailed topic, while previous works assume generating one CQ per context and the results tend to be generic. We thus propose the task of Diverse CQGen and also tackle the challenge of specificity. We propose a new model named KPCNet, which generates CQs with Keyword Prediction and Conditioning, to deal with the tasks. Automatic and human evaluation on 2 datasets (Home & Kitchen, Office) showed that KPCNet can generate more specific questions and promote better group-level diversity than several competing baselines.

The mean shift (MS) algorithm is a nonparametric method used to cluster sample points and find the local modes of kernel density estimates, using an idea based on iterative gradient ascent. In this paper we develop a mean-shift-inspired algorithm to estimate the modes of regression functions and partition the sample points in the input space. We prove convergence of the sequences generated by the algorithm and derive the non-asymptotic rates of convergence of the estimated local modes for the underlying regression model. We also demonstrate the utility of the algorithm for data-enabled discovery through an application on biomolecular structure data. An extension to subspace constrained mean shift (SCMS) algorithm used to extract ridges of regression functions is briefly discussed.

As a result of the greater availability of big data, as well as the decreasing costs and increasing power of modern computing, the use of artificial neural networks for financial time series forecasting is once again a major topic of discussion and research in the financial world. Despite this academic focus, there are still contrasting opinions and bodies of literature on which artificial neural networks perform the best and whether or not they outperform the forecasting capabilities of conventional time series models. This paper uses a long-short term memory network to perform financial time series forecasting on the return data of the JSE Top 40 index. Furthermore, the forecasting performance of the long-short term memory network is compared to the forecasting performance of a seasonal autoregressive integrated moving average model. This paper evaluates the varying approaches presented in the existing literature and ultimately, compares the results to that existing literature. The paper concludes that the long short-term memory network outperforms the seasonal autoregressive integrated moving average model when forecasting intraday directional movements as well as when forecasting the index close price.

With food at the core of the business, Glovo delivers any product within your city at any time of day. Our vision and ambition are not only to make everything immediately available in your city but it is also to offer our employees the job of their lives. A job where you'll be challenged and have the most fun working in through tech-enabled experiences. Your work-life opportunity: Glovo (glovoapp.com) is looking for a passionate, proactive, data-driven and hands-on professional to support our Live Operations Strategy & Analytics department in our headquarters in Barcelona. You will be the reporting and analytical point of contact for our Live Operations department helping provide an excellent and efficient service.

We introduce two methods for improving the performance of agents meeting for the first time to accomplish a communicative task. The methods are: (1) `message mutation' during the generation of the communication protocol; and (2) random permutations of the communication channel. These proposals are tested using a simple two-player game involving a `teacher' who generates a communication protocol and sends a message, and a `student' who interprets the message. After training multiple agents via self-play we analyse the performance of these agents when they are matched with a stranger, i.e. their zero-shot communication performance. We find that both message mutation and channel permutation positively influence performance, and we discuss their effects.

Instead, supervision can be available In this work, we tackle the problem of motor in the shape of desired sensory observations, for instance sequence learning for an embodied agent. A provided by a teaching agent. In the case wide range of approaches have been proposed of handwriting, these desired sensory observations to model sequential data, using various types of are visual observations of the target letters. In reinforcement neural architectures (Recurrent Neural Networks learning, the preference for certain sensory (RNNs), Long Short-Term Memories (LSTMs) states is modeled by assigning rewards to the [1], Restricted Boltzmann Machines (RBMs) [2]) desired states, and the agent learns a behavioral and various learning strategies (backpropagation policy maximizing its expected return (sum of rewards) through time (BPTT), Real-Time Recurrent over time. Alternatively, Active Inference Learning (RTRL) [3], Reservoir Computing (RC) (AIF) [6, 7], derived from the Free Energy Principle [4, 5]).

The scope of data-driven fault diagnosis models is greatly improved through deep learning (DL). However, the classical convolution and recurrent structure have their defects in computational efficiency and feature representation, while the latest Transformer architecture based on attention mechanism has not been applied in this field. To solve these problems, we propose a novel time-frequency Transformer (TFT) model inspired by the massive success of standard Transformer in sequence processing. Specially, we design a fresh tokenizer and encoder module to extract effective abstractions from the time-frequency representation (TFR) of vibration signals. On this basis, a new end-to-end fault diagnosis framework based on time-frequency Transformer is presented in this paper. Through the case studies on bearing experimental datasets, we constructed the optimal Transformer structure and verified the performance of the diagnostic method. The superiority of the proposed method is demonstrated in comparison with the benchmark model and other state-of-the-art methods.

What will the market size be in 2027 and what will the growth rate be? What are the key market trends? What is driving this market? What are the challenges to market growth? Who are the key vendors in this market space?

In this paper, we study the importance of context in predicting the citation worthiness of sentences in scholarly articles. We formulate this problem as a sequence labeling task solved using a hierarchical BiLSTM model. We contribute a new benchmark dataset containing over two million sentences and their corresponding labels. We preserve the sentence order in this dataset and perform document-level train/test splits, which importantly allows incorporating contextual information in the modeling process. We evaluate the proposed approach on three benchmark datasets. Our results quantify the benefits of using context and contextual embeddings for citation worthiness. Lastly, through error analysis, we provide insights into cases where context plays an essential role in predicting citation worthiness.

Traditionally Genetic Algorithm has been used for optimization of unimodal and multimodal functions. Earlier researchers worked with constant probabilities of GA control operators like crossover, mutation etc. for tuning the optimization in specific domains. Recent advancements in this field witnessed adaptive approach in probability determination. In Adaptive mutation primarily poor individuals are utilized to explore state space, so mutation probability is usually generated proportionally to the difference between fitness of best chromosome and itself (fMAX - f). However, this approach is susceptible to nature of fitness distribution during optimization. This paper presents an alternate approach of mutation probability generation using chromosome rank to avoid any susceptibility to fitness distribution. Experiments are done to compare results of simple genetic algorithm (SGA) with constant mutation probability and adaptive approaches within a limited resource constraint for unimodal, multimodal functions and Travelling Salesman Problem (TSP). Measurements are done for average best fitness, number of generations evolved and percentage of global optimum achievements out of several trials. The results demonstrate that the rank-based adaptive mutation approach is superior to fitness-based adaptive approach as well as SGA in a multimodal problem space.