Adapting speaker recognition systems to new environments is a widely-used technique to improve a well-performing model learned from large-scale data towards a task-specific small-scale data scenarios. However, previous studies focus on single domain adaptation, which neglects a more practical scenario where training data are collected from multiple acoustic domains needed in forensic scenarios. Audio analysis for forensic speaker recognition offers unique challenges in model training with multi-domain training data due to location/scenario uncertainty and diversity mismatch between reference and naturalistic field recordings. It is also difficult to directly employ small-scale domain-specific data to train complex neural network architectures due to domain mismatch and performance loss. Fine-tuning is a commonly-used method for adaptation in order to retrain the model with weights initialized from a well-trained model. Alternatively, in this study, three novel adaptation methods based on domain adversarial training, discrepancy minimization, and moment-matching approaches are proposed to further promote adaptation performance across multiple acoustic domains. A comprehensive set of experiments are conducted to demonstrate that: 1) diverse acoustic environments do impact speaker recognition performance, which could advance research in audio forensics, 2) domain adversarial training learns the discriminative features which are also invariant to shifts between domains, 3) discrepancy-minimizing adaptation achieves effective performance simultaneously across multiple acoustic domains, and 4) moment-matching adaptation along with dynamic distribution alignment also significantly promotes speaker recognition performance on each domain, especially for the LENA-field domain with noise compared to all other systems.

In recent years, social networking platforms have gained significant popularity among the masses like connecting with people and propagating ones thoughts and opinions. This has opened the door to user-specific advertisements and recommendations on these platforms, bringing along a significant focus on Influence Maximisation (IM) on social networks due to its wide applicability in target advertising, viral marketing, and personalized recommendations. The aim of IM is to identify certain nodes in the network which can help maximize the spread of certain information through a diffusion cascade. While several works have been proposed for IM, most were inefficient in exploiting community structures to their full extent. In this work, we propose a community structures-based approach, which employs a K-Shell algorithm in order to generate a score for the connections between seed nodes and communities for low-budget scenarios. Further, our approach employs entropy within communities to ensure the proper spread of information within the communities. We choose the Independent Cascade (IC) model to simulate information spread and evaluate it on four evaluation metrics. We validate our proposed approach on eight publicly available networks and find that it significantly outperforms the baseline approaches on these metrics, while still being relatively efficient.

Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, neural network architectures, learning methodologies, and evaluations schemes. However, deciphering promising predominant and emerging trends is difficult due to the variety of explored methods and lack of standardized framework for comparing drug response prediction models. To obtain a comprehensive landscape of deep learning methods, we conducted an extensive search and analysis of deep learning models that predict the response to single drug treatments. A total of 60 deep learning-based models have been curated and summary plots were generated. Based on the analysis, observable patterns and prevalence of methods have been revealed. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.

Abstract-- A wide variety of robotic hands have been designed to date. Yet, we do not know how users perceive these hands and feel about interacting with them. To inform hand design for social robots, we compiled a dataset of 73 robot hands and ran an online study, in which 160 users rated their impressions of the hands using 17 rating scales. Next, we developed 17 regression models that can predict user ratings (e.g., humanlike) from the design features of the hands (e.g., number of fingers). The models have less than a 10-point error in predicting the user ratings on a 0-100 scale. The shape of the fingertips, color scheme, and size of the hands influence the user ratings the most. We present simple guidelines to improve user impression of robot hands and outline remaining questions for future work. Figure 1: A collage of the 73 existing robotic hands that we evaluated in an online study.

With the appropriate AI application, you may quit performing tedious and time-consuming business chores, such as email marketing and analytics, and instead focus on the critical aspects of growing your business. Artificial intelligence (AI)-based technologies have created a slew of new prospects for businesses of all kinds all around the world. AI is lighting insights and altering numerous business processes. Various AI solutions on the market can help you cut operating costs, increase employee and company productivity, and increase stakeholder satisfaction. In this week's edition of Tech Tuesday, we have assembled a selection of AI tools you can use for your business needs.

Researchers at Carnegie Mellon University's School of Computer Science and the University of California, Berkeley, have designed a robotic system that enables a low-cost and relatively small legged robot to climb and descend stairs nearly its height; traverse rocky, slippery, uneven, steep and varied terrain; walk across gaps; scale rocks and curbs; and even operate in the dark. "Empowering small robots to climb stairs and handle a variety of environments is crucial to developing robots that will be useful in people's homes as well as search-and-rescue operations," said Deepak Pathak, an assistant professor in the Robotics Institute. "This system creates a robust and adaptable robot that could perform many everyday tasks." The team put the robot through its paces, testing it on uneven stairs and hillsides at public parks, challenging it to walk across stepping stones and over slippery surfaces, and asking it to climb stairs that for its height would be akin to a human leaping over a hurdle. The researchers trained the robot with 4,000 clones of it in a simulator, where they practiced walking and climbing on challenging terrain.

Prime Minister Narendra Modi inaugurated the 25th edition of Bengaluru Tech Summit 2022, at a glittering ceremony in the presence of a galaxy of leaders from the IT, BT and start-up sectors. The event was organized by The Department of Electronics, IT, Bt, S&T, Government of Karnataka along with Software Technology Parks of India (STPI), and is the first full-fledged on ground version of BTS post the pandemic. The central theme of BTS this year is'Tech4NexGen' and will focus on Electronics, IT, Deep Tech, Biotech, and Startups. The inaugural ceremony was graced by the Guests of Honor H.E Mr. Petri Honkonen, Minister of Science & Culture, Finland, H.E. Mr. Omar Bin Sultan Al Olama, Minister of State for Artificial Intelligence, Digital Economy & Remote Work Applications, United Arab Emirates, H.E. Mr. Tim Watts, Assistant Minister for Foreign Affairs, Australia. The ceremony was presided over by Shri Basavaraj S. Bommai, Chief Minister of Karnataka.

Tuvalu's foreign minister, Simon Kofe, told the COP27 climate summit yesterday that Tuvalu would look to the metaverse to preserve its culture and history. With global temperatures expected to rise as much as 2.8 degrees Celsius by the end of the century, the Pacific island nation is particularly vulnerable to rising sea levels. At last year's COP26 summit, Kofe addressed the conference while standing knee-deep in seawater to highlight the climate change threat. Climate scientists anticipate the entire country will be underwater by the end of the 21st century. Addressing the climate summit, Kofe said: "As our land disappears, we have no choice but to become the world's first digital nation. Our land, our ocean, our culture are the most precious assets of our people. And to keep them safe from harm, no matter what happens in the physical world, we'll move them to the cloud."

In 2015, I visited Fiji, Kiribati, and Tuvalu, which had just been hit by a cyclone. There, I learned a slogan -- "1.5 to stay alive" -- which refers to the 1.5 degrees Celsius (2.7 degrees Fahrenheit) threshold for global warming that, in theory, would avoid disastrous consequences. People living on the Pacific islands are well aware of the grave threat to humanity posed by climate change. Six months later, I met these new comrades again at climate negotiations in Paris. While speaking at an event, I referred to "1.5 to stay alive".

Relational triple extraction is challenging for its difficulty in capturing rich correlations between entities and relations. Existing works suffer from 1) heterogeneous representations of entities and relations, and 2) heterogeneous modeling of entity-entity interactions and entity-relation interactions. Therefore, the rich correlations are not fully exploited by existing works. In this paper, we propose UniRel to address these challenges. Specifically, we unify the representations of entities and relations by jointly encoding them within a concatenated natural language sequence, and unify the modeling of interactions with a proposed Interaction Map, which is built upon the off-the-shelf self-attention mechanism within any Transformer block. With comprehensive experiments on two popular relational triple extraction datasets, we demonstrate that UniRel is more effective and computationally efficient. The source code is available at https://github.com/wtangdev/UniRel.