A series of social media posts signed under the pseudonym "Q", started a movement known as QAnon, which led some of its most radical supporters to violent and illegal actions. To identify the person(s) behind Q, we evaluate the coincidence between the linguistic properties of the texts written by Q and to those written by a list of suspects provided by journalistic investigation. To identify the authors of these posts, serious challenges have to be addressed. The "Q drops" are very short texts, written in a way that constitute a sort of literary genre in itself, with very peculiar features of style. These texts might have been written by different authors, whose other writings are often hard to find. After an online ethnology of the movement, necessary to collect enough material written by these thirteen potential authors, we use supervised machine learning to build stylistic profiles for each of them. We then performed a rolling analysis on Q's writings, to see if any of those linguistic profiles match the so-called 'QDrops' in part or entirety. We conclude that two different individuals, Paul F. and Ron W., are the closest match to Q's linguistic signature, and they could have successively written Q's texts. These potential authors are not high-ranked personality from the U.S. administration, but rather social media activists.

Gridded satellite precipitation datasets are useful in hydrological applications as they cover large regions with high density. However, they are not accurate in the sense that they do not agree with ground-based measurements. An established means for improving their accuracy is to correct them by adopting machine learning algorithms. This correction takes the form of a regression problem, in which the ground-based measurements have the role of the dependent variable and the satellite data are the predictor variables, together with topography factors (e.g., elevation). Most studies of this kind involve a limited number of machine learning algorithms, and are conducted for a small region and for a limited time period. Thus, the results obtained through them are of local importance and do not provide more general guidance and best practices. To provide results that are generalizable and to contribute to the delivery of best practices, we here compare eight state-of-the-art machine learning algorithms in correcting satellite precipitation data for the entire contiguous United States and for a 15-year period. We use monthly data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) gridded dataset, together with monthly earth-observed precipitation data from the Global Historical Climatology Network monthly database, version 2 (GHCNm). The results suggest that extreme gradient boosting (XGBoost) and random forests are the most accurate in terms of the squared error scoring function. The remaining algorithms can be ordered as follows from the best to the worst: Bayesian regularized feed-forward neural networks, multivariate adaptive polynomial splines (poly-MARS), gradient boosting machines (gbm), multivariate adaptive regression splines (MARS), feed-forward neural networks, linear regression.

Competitive influence maximization has been studied for several years, and various frameworks have been proposed to model different aspects of information diffusion under the competitive environment. This work presents a new gameboard for two competing parties with some new features representing loyalty in social networks and reflecting the attitude of not completely being loyal to a party when the opponent offers better suggestions. This behavior can be observed in most political occasions where each party tries to attract people by making better suggestions than the opponent and even seeks to impress the fans of the opposition party to change their minds. In order to identify the best move in each step of the game framework, an improved Monte Carlo tree search is developed, which uses some predefined heuristics to apply them on the simulation step of the algorithm and takes advantage of them to search among child nodes of the current state and pick the best one using an epsilon-greedy way instead of choosing them at random. Experimental results on synthetic and real datasets indicate the outperforming of the proposed strategy against some well-known and benchmark strategies like general MCTS, minimax algorithm with alpha-beta pruning, random nodes, nodes with maximum threshold and nodes with minimum threshold.

Models using structured state space sequence (S4) layers have achieved state-of-the-art performance on long-range sequence modeling tasks. An S4 layer combines linear state space models (SSMs), the HiPPO framework, and deep learning to achieve high performance. We build on the design of the S4 layer and introduce a new state space layer, the S5 layer. Whereas an S4 layer uses many independent single-input, single-output SSMs, the S5 layer uses one multi-input, multi-output SSM. We establish a connection between S5 and S4, and use this to develop the initialization and parameterization used by the S5 model. The result is a state space layer that can leverage efficient and widely implemented parallel scans, allowing S5 to match the computational efficiency of S4, while also achieving state-of-the-art performance on several long-range sequence modeling tasks. S5 averages 87.4% on the long range arena benchmark, and 98.5% on the most difficult Path-X task.

"Would you like to go out again?" asked the former woodworker, who likes intense, rambling conversations. "Yes, but first I have to tell you something," said the woman seeking someone to laugh with in the face of life's mysteries. And then she explained that it was not her who'd originally set up her profile and arranged the date--it was ChatGPT. And some woman he'd never met. I am to blame--or to credit, if date No. 2 goes well--for this scenario, which occurred last month in a bar in New York. It was just one of quite a few exchanges that I facilitated, using some supposedly transformative A.I. tools, for a friend who (perhaps unwisely!) had given me the keys to her Tinder and Bumble accounts. Here are some examples of A.I.-generated openers I considered … If you were a vegetable, you'd be a cutecumber. I've been reading a book on anti-gravity lately. It's impossible to put down.

The clips are from a YouTube channel called House of News, which presents itself as an English-language media outlet. Researchers say the videos are part of the Venezuelan government's attempts to spin the narrative on social media, considered one of the last bastions of free speech in a nation where outlets are censored and journalists are often persecuted. The incorporation of AI, experts told The Washington Post, seems to be a new addition to the government's disinformation campaigns, which range from incentivizing Twitter users to post specific talking points to using bots that spit out the regime's messaging.

Accuracy competition: This competition hosted by Walmart aimed to forecast daily sales of 3,049 products in 10 stores over a period of 28 days. Participants were required to forecast the sales of each product for each day of the competition using historical sales data provided by Walmart. This competition taught participants how to deal with a large dataset with multiple features and how to use various time-series forecasting techniques, such as ARIMA and Prophet. The Rossmann Store Sales competition: This competition aimed to forecast the daily sales of 1,115 Rossmann stores located in Germany. Participants were required to forecast sales for the next six weeks, taking into account factors such as promotions, school holidays, and store closures.

As a key component of automated speech recognition (ASR) and the front-end in text-to-speech (TTS), grapheme-to-phoneme (G2P) plays the role of converting letters to their corresponding pronunciations. Existing methods are either slow or poor in performance, and are limited in application scenarios, particularly in the process of on-device inference. In this paper, we integrate the advantages of both expert knowledge and connectionist temporal classification (CTC) based neural network and propose a novel method named LiteG2P which is fast, light and theoretically parallel. With the carefully leading design, LiteG2P can be applied both on cloud and on device. Experimental results on the CMU dataset show that the performance of the proposed method is superior to the state-of-the-art CTC based method with 10 times fewer parameters, and even comparable to the state-of-the-art Transformer-based sequence-to-sequence model with less parameters and 33 times less computation.

Machine Learning (ML) has been widely used in Natural Language Processing (NLP) applications. A fundamental assumption in ML is that training data and real-world data should follow a similar distribution. However, a deployed ML model may suffer from out-of-distribution (OOD) issues due to distribution shifts in the real-world data. Though many algorithms have been proposed to detect OOD data from text corpora, there is still a lack of interactive tool support for ML developers. In this work, we propose DeepLens, an interactive system that helps users detect and explore OOD issues in massive text corpora. Users can efficiently explore different OOD types in DeepLens with the help of a text clustering method. Users can also dig into a specific text by inspecting salient words highlighted through neuron activation analysis. In a within-subjects user study with 24 participants, participants using DeepLens were able to find nearly twice more types of OOD issues accurately with 22% more confidence compared with a variant of DeepLens that has no interaction or visualization support.

Time-series representation learning can extract representations from data with temporal dynamics and sparse labels. When labeled data are sparse but unlabeled data are abundant, contrastive learning, i.e., a framework to learn a latent space where similar samples are close to each other while dissimilar ones are far from each other, has shown outstanding performance. This strategy can encourage varied consistency of time-series representations depending on the positive pair selection and contrastive loss. We propose a new time-series representation learning method by combining the advantages of self-supervised tasks related to contextual, temporal, and transformation consistency. It allows the network to learn general representations for various downstream tasks and domains. Specifically, we first adopt data preprocessing to generate positive and negative pairs for each self-supervised task. The model then performs contextual, temporal, and transformation contrastive learning and is optimized jointly using their contrastive losses. We further investigate an uncertainty weighting approach to enable effective multi-task learning by considering the contribution of each consistency. We evaluate the proposed framework on three downstream tasks: time-series classification, forecasting, and anomaly detection. Experimental results show that our method not only outperforms the benchmark models on these downstream tasks, but also shows efficiency in cross-domain transfer learning.