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Sources: Google reduces its Google TV and Android TV budget by 10% and cuts Android TV and Google TV's ~300 employees by up to 25%, as it focuses on YouTube (Sahil Patel/The Information)
https://www.theinformation.com/articles/google-cuts-sm…

Google Cuts Smart TV Budget as It Doubles Down on YouTube
Google has cut its smart TV budget and trimmed its team—marking the most concrete signs yet of the company’s waning interest competing with the likes of Samsung and Roku as it centers its TV business around YouTube. The company has reduced the budget for Google TV and Android TV 10%, according ...

Spannende Veranstaltung heute von der @… :
Feministische Antworten auf digitale Gewalt und rechte Strategien
https:…

Effective bounds for polynomial systems defined over the rationals
Teresa Krick
https://arxiv.org/abs/2506.18144 https://arxiv.org/pd…

Effective bounds for polynomial systems defined over the rationals
This survey paper was primarily written as as the support for a course pesented at the JNCF2025: it aims to present some material that illustrates the kind of estimates one can obtain in effective algebraic geometry, for affine polynomial equation systems defined over the rational numbers Q, and focuses on the case of finite varieties.

Team LA at SCIDOCA shared task 2025: Citation Discovery via relation-based zero-shot retrieval
Trieu An, Long Nguyen, Minh Le Nguyen
https://arxiv.org/abs/2506.18316

Team LA at SCIDOCA shared task 2025: Citation Discovery via relation-based zero-shot retrieval
The Citation Discovery Shared Task focuses on predicting the correct citation from a given candidate pool for a given paragraph. The main challenges stem from the length of the abstract paragraphs and the high similarity among candidate abstracts, making it difficult to determine the exact paper to cite. To address this, we develop a system that first retrieves the top-k most similar abstracts based on extracted relational features from the given paragraph. From this subset, we leverage a Large…

An Analytical Framework for the Linear Best-Worst Method and its Application to Achieve Sustainable Development Goals--Oriented Agri-Food Supply Chains
Harshit M. Ratandhara, Mohit Kumar
https://arxiv.org/abs/2506.17666

An Analytical Framework for the Linear Best-Worst Method and its Application to Achieve Sustainable Development Goals--Oriented Agri-Food Supply Chains
The Best-Worst Method (BWM) has emerged as a prominent multi-criteria decision-making method for determining the weights of the decision criteria. Among various BWM models, this research focuses on the linear model of the BWM. This model calculates weights by solving an optimization problem, necessitating optimization software. In this article, we present a novel framework that solves this optimization model mathematically, yielding an analytical expression for the resultant weights, thus elimi…

Advancing Fact Attribution for Query Answering: Aggregate Queries and Novel Algorithms
Omer Abramovich, Daniel Deutch, Nave Frost, Ahmet Kara, Dan Olteanu
https://arxiv.org/abs/2506.16923

Advancing Fact Attribution for Query Answering: Aggregate Queries and Novel Algorithms
In this paper, we introduce a novel approach to computing the contribution of input tuples to the result of the query, quantified by the Banzhaf and Shapley values. In contrast to prior algorithmic work that focuses on Select-Project-Join-Union queries, ours is the first practical approach for queries with aggregates. It relies on two novel optimizations that are essential for its practicality and significantly improve the runtime performance already for queries without aggregates. The first op…

Multi-Domain Optimization Framework for ISAC: From Electromagnetic Shaping to Network Cooperation
Rang Liu, Ming Li, Mehdi Zafari, Bjorn Ottersten, A. Lee Swindlehurst
https://arxiv.org/abs/2506.16011

Multi-Domain Optimization Framework for ISAC: From Electromagnetic Shaping to Network Cooperation
Integrated sensing and communication (ISAC) has emerged as a key feature for sixth-generation (6G) networks, providing an opportunity to meet the dual demands of communication and sensing. Existing ISAC research primarily focuses on baseband optimization at individual access points, with limited attention to the roles of electromagnetic (EM) shaping and network-wide coordination. The intricate interdependencies between these domains remain insufficiently explored, leaving their full potential f…

EDNet: A Distortion-Agnostic Speech Enhancement Framework with Gating Mamba Mechanism and Phase Shift-Invariant Training
Doyeop Kwak, Youngjoon Jang, Seongyu Kim, Joon Son Chung
https://arxiv.org/abs/2506.16231

EDNet: A Distortion-Agnostic Speech Enhancement Framework with Gating Mamba Mechanism and Phase Shift-Invariant Training
Speech signals in real-world environments are frequently affected by various distortions such as additive noise, reverberation, and bandwidth limitation, which may appear individually or in combination. Traditional speech enhancement methods typically rely on either masking, which focuses on suppressing non-speech components while preserving observable structure, or mapping, which seeks to recover clean speech through direct transformation of the input. Each approach offers strengths in specifi…

RAGentA: Multi-Agent Retrieval-Augmented Generation for Attributed Question Answering
Ines Besrour, Jingbo He, Tobias Schreieder, Michael F\"arber
https://arxiv.org/abs/2506.16988

RAGentA: Multi-Agent Retrieval-Augmented Generation for Attributed Question Answering
We present RAGentA, a multi-agent retrieval-augmented generation (RAG) framework for attributed question answering (QA). With the goal of trustworthy answer generation, RAGentA focuses on optimizing answer correctness, defined by coverage and relevance to the question and faithfulness, which measures the extent to which answers are grounded in retrieved documents. RAGentA uses a multi-agent architecture that iteratively filters retrieved documents, generates attributed answers with in-line cita…

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