First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Paper For paper 1180: Deep Recursive Neural Networks for Compositionality in Language This paper introduces a new architecture -- deep recursive neural network (deep RNN) which is constructed by stacking multiple recursive layers. The authors evaluate the proposed model on the task of fine-grained sentiment classification. Clarity - In general, this paper is well written and pleasant to read. Quality - The paper seems technically sound.

Well, it's official: Apple is finally nixing support for its old Home architecture, meaning those relying on the previous version of Apple's Home framework have some decisions to make. In a revision to a support article, Apple says that it will end support for the previous version of its Home app starting in fall 2025, right around the time when iOS 19 is expected to drop, MacRumors reports. If you don't update, you face "interruptions with your accessories and automations," Apple warns. In other words, you'll lose control of your Apple HomeKit-connected smart devices. Apple's move to ditch its old Home app has been a long time in coming.

When Italian philosopher and essayist Andrea Colamedici released Ipnocrazia: Trump, Musk e La Nuova Architettura Della Realtà (Hypnocracy: Trump, Musk, and the New Architecture of Reality), he wanted to make a statement about the existence of truth in the digital age. The book, published in December, was described as "a crucial book for understanding how control is currently exercised not by repressing truth but by multiplying narratives, making it impossible to locate any fixed point," according to a description by Tlon, a publishing house Colamedici cofounded. While the book attracted buzz in philosophy circles, Italian magazine L'Espresso revealed in April that the book's purported author, Jianwei Xun, did not exist, after one of its editors tried and failed to interview him. Initially described as a Hong Kong–born philosopher based in Berlin, it turned out that Xun was actually a hybrid human-algorithmic creation. Colamedici, listed on the book as translator, used AI to generate concepts and then critique those concepts.

The paper presents a new architecture for encoder/decoder models for sequence-to-sequence modeling that is solely based on (multi-layered) attention networks combined with standard Feed-Forward networks as opposed to the common scheme of using recurrent or convolutional neural networks. The paper presents two main advantages of this new architecture: (1) Reduced training time due to reduced complexity of the architecture, and (2) new State-of-the-Art result on standard WMT data sets, outperforming previous work by about 1 BLEU point. Strengths: - The paper argues well that (1) can be achieved by avoiding recurrent or convolutional layers and the complexity analysis in Table 1 strengthens the argument. The main strengths of the paper are that it proposes an entirely novel architecture without recurrence or convolutions, and advances state of the art. Weaknesses: - While the general architecture of the model is described well and is illustrated by figures, architectural details lack mathematical definition, for example multi-head attention.

We develop a deep reinforcement learning framework for tactical decision making in an autonomous truck, specifically for Adaptive Cruise Control (ACC) and lane change maneuvers in a highway scenario. Our results demonstrate that it is beneficial to separate high-level decision-making processes and low-level control actions between the reinforcement learning agent and the low-level controllers based on physical models. In the following, we study optimizing the performance with a realistic and multi-objective reward function based on Total Cost of Operation (TCOP) of the truck using different approaches; by adding weights to reward components, by normalizing the reward components and by using curriculum learning techniques.

We present the latest release of PANNA 2.0 (Properties from Artificial Neural Network Architectures), a code for the generation of neural network interatomic potentials based on local atomic descriptors and multilayer perceptrons. Built on a new back end, this new release of PANNA features improved tools for customizing and monitoring network training, better GPU support including a fast descriptor calculator, new plugins for external codes and a new architecture for the inclusion of long-range electrostatic interactions through a variational charge equilibration scheme. We present an overview of the main features of the new code, and several benchmarks comparing the accuracy of PANNA models to the state of the art, on commonly used benchmarks as well as richer datasets.

In a market still filling with fledging silicon chips, Ceremorphic, Inc. has exited stealth and is telling the world about what it calls its patented new ThreadArch multi-thread processor technology that is intended to help improve new supercomputers. Venkat Mattela, the company's founder and CEO of Ceremorphic, calls his latest chip design a Hierarchical Learning Processor (HLP), even though several technology analysts said they recognize it as a system on a chip (SoC) design. The goal of the company is to design, benchmark and market a new kind of ultra-low-power AI training chip. "What we are trying to solve is today – everybody knows how to do higher performance – you can buy an Nvidia machine," Mattela told HPCwire. "Can we have the highest performance in a reliable way? Architecture is how we achieve it," using multiple processors, a multiple logic design and mixing and matching it all.

The architecture of data is not just a technical architecture but is also an organizational structure, therefore, making it a key factor for building any data empire. Over time there have been introduced different types of architectures, always with the aim of covering the gaps with the ideal solution: we started with data warehouses which were mainly focused on creating structured datasets for reporting, then expanded to data lakes with the aim of having centralized access to the data wherever they are and in whichever form and to remove the pain points present in the data lake a new architecture called data mesh was introduced about 2 years ago by Zhamak Dehghani. We all know that this is a field full of buzzwords so whenever something new comes out it takes a while (if ever) to establish what it precisely means; the data mesh is not an exception. However, the simplest explanation is that it is a domain-oriented structuring of the data with a focus on data and data product ownership, driving towards a well-governed data usage as well as offering self-serve data infrastructure. But what do these keywords mean precisely?

The effectiveness of shortcut/skip-connection has been widely verified, which inspires massive explorations on neural architecture design. This work attempts to find an effective way to design new network architectures. it is discovered that the main difference between network architectures can be reflected in their recursion formulas. Based on this, a methodology is proposed to design novel network architectures from the perspective of mathematical formulas. Afterwards, a case study is provided to generate an improved architecture based on ResNet. Furthermore, the new architecture is compared with ResNet and then tested on ResNet-based networks. Massive experiments are conducted on CIFAR and ImageNet, which witnesses the significant performance improvements provided by the architecture.

Predictor-based algorithms have achieved remarkable performance in the Neural Architecture Search (NAS) tasks. However, these methods suffer from high computation costs, as training the performance predictor usually requires training and evaluating hundreds of architectures from scratch. Previous works along this line mainly focus on reducing the number of architectures required to fit the predictor. In this work, we tackle this challenge from a different perspective - improve search efficiency by cutting down the computation budget of architecture training. We propose NOn-uniform Successive Halving (NOSH), a hierarchical scheduling algorithm that terminates the training of underperforming architectures early to avoid wasting budget. To effectively leverage the non-uniform supervision signals produced by NOSH, we formulate predictor-based architecture search as learning to rank with pairwise comparisons. The resulting method - RANK-NOSH, reduces the search budget by ~5x while achieving competitive or even better performance than previous state-of-the-art predictor-based methods on various spaces and datasets.