Intrigued by an Ars Technica post about Amazon's Alexa that suggested all was not well in the tech company's division that looks after its smart home devices, I went rooting in a drawer where the Echo Dot I bought years ago had been gathering dust. Having found it, and set it up to join the upgraded wifi network that hadn't existed when I first got it, I asked it a question: "Alexa, why are you such a loss-maker?" To which she calmly replied: "This might answer your question: mustard gas, also known as Lost, is manufactured by the United States." At which point, I solemnly thanked her, pulled the power cable and returned her to the drawer, where she will continue to gather dust until I can think of an ecologically responsible way of recycling her. I bought the device on 5 December 2016 (on the basis that one shouldn't pontificate on kit that one hasn't purchased oneself) and wrote about it in January 2017.

Lithuania's Foreign Minister, Gabrielius Landsbergis, talked with Fox News Digital about Russia, China and the'global rules-based order' on the 20th anniversary of his country joining NATO. Lithuania commemorated its entry into NATO this last week and its long-standing partnership with the U.S. as leaders look ahead to the increasingly complex security landscape developing around the world. President George W. Bush visited the Lithuanian capital of Vilnius 20 years ago to welcome the country into the still-growing NATO alliance, applauding the character of member states to "stand in the face of evil, to have the courage to always face danger." "President [George W.] Bush made the most famous speech any American has ever made in Lithuania exactly 20 years ago," Lithuanian Foreign Minister Gabrielius Landsbergis told Fox News Digital in an exclusive interview. "That was even before we were a member of NATO, and it was probably the most important security guarantee that we got before Article Five started covering us with its umbrella."

Combining multi-spectral satellite data and machine learning has been suggested as a method for monitoring plastic pollutants in the ocean environment. Recent studies have made theoretical progress regarding the identification of marine plastic via machine learning. However, no study has assessed the application of these methods for mapping and monitoring marine-plastic density. As such, this paper comprised of three main components: (1) the development of a machine learning model, (2) the construction of the MAP-Mapper, an automated tool for mapping marine-plastic density, and finally (3) an evaluation of the whole system for out-of-distribution test locations. The findings from this paper leverage the fact that machine learning models need to be high-precision to reduce the impact of false positives on results. The developed MAP-Mapper architectures provide users choices to reach high-precision ($\textit{abbv.}$ -HP) or optimum precision-recall ($\textit{abbv.}$ -Opt) values in terms of the training/test data set. Our MAP-Mapper-HP model greatly increased the precision of plastic detection to 95\%, whilst MAP-Mapper-Opt reaches precision-recall pair of 87\%-88\%. The MAP-Mapper contributes to the literature with the first tool to exploit advanced deep/machine learning and multi-spectral imagery to map marine-plastic density in automated software. The proposed data pipeline has taken a novel approach to map plastic density in ocean regions. As such, this enables an initial assessment of the challenges and opportunities of this method to help guide future work and scientific study.

This paper uses computational experiments to explore the role of exposure in the emergence of construction grammars. While usage-based grammars are hypothesized to depend on a learner's exposure to actual language use, the mechanisms of such exposure have only been studied in a few constructions in isolation. This paper experiments with (i) the growth rate of the constructicon, (ii) the convergence rate of grammars exposed to independent registers, and (iii) the rate at which constructions are forgotten when they have not been recently observed. These experiments show that the lexicon grows more quickly than the grammar and that the growth rate of the grammar is not dependent on the growth rate of the lexicon. At the same time, register-specific grammars converge onto more similar constructions as the amount of exposure increases. This means that the influence of specific registers becomes less important as exposure increases. Finally, the rate at which constructions are forgotten when they have not been recently observed mirrors the growth rate of the constructicon. This paper thus presents a computational model of usage-based grammar that includes both the emergence and the unentrenchment of constructions.

On Saturday, October 29, a Russian fleet on the Black Sea near Sevastopol was attacked by 16 drones--nine in the air and seven in the water. Purportedly launched by Ukraine, no one knows how much damage was done, but video shot by the attacking drones showed that the vessels were unable to avoid being hit. In response to that and other successful attacks, Russia has retaliated with scores of missiles and Iranian-built Shahed-136 drones aimed at electrical and water systems throughout Ukraine. Despite daily reports of lands taken or lands liberated in the nine-month war, the conflict has been largely fought in the air, with artillery shells, rockets, cruise missiles, and, increasingly, drones. Small, cheap, relatively slow-moving, carrying far less of a wallop than a cruise missile or a 500-pound bomb, the Shaheds in particular have bedeviled Ukraine's otherwise excellent air defenses.

As a futurist, it's my job to look ahead -- so every year, I cover the emerging tech trends that will be shaping our digital world in the next 12 months. What technologies are gaining the most traction? What are the most important trends that business leaders should be prepared for? Read on for the ten essential tech trends you should be following in 2023. In 2023, artificial intelligence will become real in organizations.

This work presents two astonishing findings on neural networks learned for large-scale image classification. 1) Given a well-trained model, the logits predicted for some category can be directly obtained by linearly combining the predictions of a few other categories, which we call \textbf{neural dependency}. 2) Neural dependencies exist not only within a single model, but even between two independently learned models, regardless of their architectures. Towards a theoretical analysis of such phenomena, we demonstrate that identifying neural dependencies is equivalent to solving the Covariance Lasso (CovLasso) regression problem proposed in this paper. Through investigating the properties of the problem solution, we confirm that neural dependency is guaranteed by a redundant logit covariance matrix, which condition is easily met given massive categories, and that neural dependency is highly sparse, implying that one category correlates to only a few others. We further empirically show the potential of neural dependencies in understanding internal data correlations, generalizing models to unseen categories, and improving model robustness with a dependency-derived regularizer. Code for this work will be made publicly available.

Biofouling is a major challenge for sustainable shipping, filter membranes, heat exchangers, and medical devices. The development of fouling-resistant coatings requires the evaluation of their effectiveness. Such an evaluation is usually based on the assessment of fouling progression after different exposure times to the target medium (e.g., salt water). The manual assessment of macrofouling requires expert knowledge about local fouling communities due to high variances in phenotypical appearance, has single-image sampling inaccuracies for certain species, and lacks spatial information. Here we present an approach for automatic image-based macrofouling analysis. We created a dataset with dense labels prepared from field panel images and propose a convolutional network (adapted U-Net) for the semantic segmentation of different macrofouling classes. The establishment of macrofouling localization allows for the generation of a successional model which enables the determination of direct surface attachment and in-depth epibiotic studies.

Studying under-represented music traditions under the MIR scope is crucial, not only for developing novel analysis tools, but also for unveiling musical functions that might prove useful in studying world musics. This paper presents a dataset for Greek Traditional and Folk music that includes 1570 pieces, summing in around 80 hours of data. The dataset incorporates YouTube timestamped links for retrieving audio and video, along with rich metadata information with regards to instrumentation, geography and genre, among others. The content has been collected from a Greek documentary series that is available online, where academics present music traditions of Greece with live music and dance performance during the show, along with discussions about social, cultural and musicological aspects of the presented music. Therefore, this procedure has resulted in a significant wealth of descriptions regarding a variety of aspects, such as musical genre, places of origin and musical instruments. In addition, the audio recordings were performed under strict production-level specifications, in terms of recording equipment, leading to very clean and homogeneous audio content. In this work, apart from presenting the dataset in detail, we propose a baseline deep-learning classification approach to recognize the involved musicological attributes. The dataset, the baseline classification methods and the models are provided in public repositories. Future directions for further refining the dataset are also discussed.

Heat waves are projected to increase in frequency and severity with global warming. Improved warning systems would help reduce the associated loss of lives, wildfires, power disruptions, and reduction in crop yields. In this work, we explore the potential for deep learning systems trained on historical data to forecast extreme heat on short, medium and subseasonal timescales. To this purpose, we train a set of neural weather models (NWMs) with convolutional architectures to forecast surface temperature anomalies globally, 1 to 28 days ahead, at $\sim200~\mathrm{km}$ resolution and on the cubed sphere. The NWMs are trained using the ERA5 reanalysis product and a set of candidate loss functions, including the mean squared error and exponential losses targeting extremes. We find that training models to minimize custom losses tailored to emphasize extremes leads to significant skill improvements in the heat wave prediction task, compared to NWMs trained on the mean squared error loss. This improvement is accomplished with almost no skill reduction in the general temperature prediction task, and it can be efficiently realized through transfer learning, by re-training NWMs with the custom losses for a few epochs. In addition, we find that the use of a symmetric exponential loss reduces the smoothing of NWM forecasts with lead time. Our best NWM is able to outperform persistence in a regressive sense for all lead times and temperature anomaly thresholds considered, and shows positive regressive skill compared to the ECMWF subseasonal-to-seasonal control forecast after two weeks.