We provide details on the implementation of a machine-learning based particle flow algorithm for CMS. The standard particle flow algorithm reconstructs stable particles based on calorimeter clusters and tracks to provide a global event reconstruction that exploits the combined information of multiple detector subsystems, leading to strong improvements for quantities such as jets and missing transverse energy. We have studied a possible evolution of particle flow towards heterogeneous computing platforms such as GPUs using a graph neural network. The machine-learned PF model reconstructs particle candidates based on the full list of tracks and calorimeter clusters in the event. For validation, we determine the physics performance directly in the CMS software framework when the proposed algorithm is interfaced with the offline reconstruction of jets and missing transverse energy. We also report the computational performance of the algorithm, which scales approximately linearly in runtime and memory usage with the input size.

Multimodal sentiment analysis has attracted increasing attention and lots of models have been proposed. However, the performance of the state-of-the-art models decreases sharply when they are deployed in the real world. We find that the main reason is that real-world applications can only access the text outputs by the automatic speech recognition (ASR) models, which may be with errors because of the limitation of model capacity. Through further analysis of the ASR outputs, we find that in some cases the sentiment words, the key sentiment elements in the textual modality, are recognized as other words, which makes the sentiment of the text change and hurts the performance of multimodal sentiment models directly. To address this problem, we propose the sentiment word aware multimodal refinement model (SWRM), which can dynamically refine the erroneous sentiment words by leveraging multimodal sentiment clues. Specifically, we first use the sentiment word position detection module to obtain the most possible position of the sentiment word in the text and then utilize the multimodal sentiment word refinement module to dynamically refine the sentiment word embeddings. The refined embeddings are taken as the textual inputs of the multimodal feature fusion module to predict the sentiment labels. We conduct extensive experiments on the real-world datasets including MOSI-Speechbrain, MOSI-IBM, and MOSI-iFlytek and the results demonstrate the effectiveness of our model, which surpasses the current state-of-the-art models on three datasets. Furthermore, our approach can be adapted for other multimodal feature fusion models easily. Data and code are available at https://github.com/albertwy/SWRM.

A legal challenge is being prepared to overturn the Intellectual Property Office's (IPONZ) decision not to recognise a machine as an inventor. It is being led by University of Surrey law professor Ryan Abbott, who has been testing patent law around the world, including New Zealand, to see if an invention created by an artificial intelligence (AI) programme could receive a patent. The test case centres around a "creativity machine" or AI inventor programme, known as DABUS, which was developed by US-based physicist Stephen Thaler. Abbott approached Thaler about using the AI as the basis of the case and with a team of lawyers, all working pro bono, they filed patent applications in more than a dozen countries listing DABUS as the inventor of a beverage container it created. New Zealand's Assistant Commissioner of Patents rejected the initial application in January, ruling that the term "inventor" intrinsically refers to a natural person.

February may be the shortest and coldest month of the year. But for many, it is a time to give special recognition to often overlooked aspects of world history (Black History Month) and recognize what may be the single greatest threat to health in the world. For many, February is also known as Heart Health Month, and 2022 will be the 58th consecutive year it is recognized. Cardiovascular disease is a global problem that claims the lives of more people a year than cancer, strokes, or other prevalent diseases. Luckily, advanced research is leading to effective solutions for improving cardiovascular health.

Maëva Ghonda is a scientist born in Kinshasa, the great capital city of the Democratic Republic of Congo (DRC). Maëva is the editor-in-chief of the IEEE Quantum Computing Newsletter, the host of the Quantum AI Series Podcast, and the chair of the Quantum AI Institute. As a research scientist, her work is centered on technological innovations -- i.e. Quantum Computing, Artificial Intelligence and Machine Learning -- to tackle challenges in Pharma and Healthcare (e.g. Maëva Ghonda's passion for quantum computing ignited while working as Joint Quantum Institute Scholar.

We consider increasingly complex models of matrix denoising and dictionary learning in the Bayes-optimal setting, in the challenging regime where the matrices to infer have a rank growing linearly with the system size. This is in contrast with most existing literature concerned with the low-rank (i.e., constant-rank) regime. We first consider a class of rotationally invariant matrix denoising problems whose mutual information and minimum mean-square error are computable using techniques from random matrix theory. Next, we analyze the more challenging models of dictionary learning. To do so we introduce a novel combination of the replica method from statistical mechanics together with random matrix theory, coined spectral replica method. This allows us to derive variational formulas for the mutual information between hidden representations and the noisy data of the dictionary learning problem, as well as for the overlaps quantifying the optimal reconstruction error. The proposed method reduces the number of degrees of freedom from $\Theta(N^2)$ matrix entries to $\Theta(N)$ eigenvalues (or singular values), and yields Coulomb gas representations of the mutual information which are reminiscent of matrix models in physics. The main ingredients are a combination of large deviation results for random matrices together with a new replica symmetric decoupling ansatz at the level of the probability distributions of eigenvalues (or singular values) of certain overlap matrices and the use of HarishChandra-Itzykson-Zuber spherical integrals.

In machine learning, crowdsourcing is an economical way to label a large amount of data. However, the noise in the produced labels may deteriorate the accuracy of any classification method applied to the labelled data. We propose an enhanced nearest neighbor classifier (ENN) to overcome this issue. Two algorithms are developed to estimate the worker quality (which is often unknown in practice): one is to construct the estimate based on the denoised worker labels by applying the $k$NN classifier to the expert data; the other is an iterative algorithm that works even without access to the expert data. Other than strong numerical evidence, our proposed methods are proven to achieve the same regret as its oracle version based on high-quality expert data. As a technical by-product, a lower bound on the sample size assigned to each worker to reach the optimal convergence rate of regret is derived.

If I'd known a movie version of Uncharted was soon coming out, I would have been a bit more guarded about admitting I'm a huge fan of the game to my editor. I am a fan, but I'm also a parent now, and I can't just leave the house on a whim for some entertainment. I need to hire a nanny to watch my kid, and like ordering popcorn and Twizzlers, that factors into the cost of catching a flick. This was this the first movie we've seen in a theater since Musa was born. I'm still waiting for the new Spider-Man to make it to streaming platforms, which is the only way my wife and I get to scratch our movie itch these days.

Dr. Dorel Iosif is a Board Director and CEO with Cognisium, a tech executive marketplace headquartered in Australia. He held senior executive roles with KBR, WorleyParsons, PwC and Advisian Management Consulting. Dr Iosif started his career in Israel with the Technion Institute of Technology and continued in Australia with BHPBilliton and the University of Melbourne. He holds a Ph.D in applied mathematics from the University of Melbourne and studied Corporate Level Strategy - Executive Program at Harvard Business School. Dorel worked in Australia, USA, Europe and the Middle East.

Data Scientists to join the Data Insights team at Zapier. Data Insights is responsible for driving impactful insight, experimentation, and quantitative research at Zapier. We work across Product, Growth & Revenue, Marketing, and Support, steering our business stakeholders to make data-informed decisions and deepening business understanding of opportunities and risks. Our Data Scientists are semi-embedded into different business zones, developing tight-knit thought partnerships with key stakeholders. We're hiring for a range of zones, so if you are a creative quantitative analyst interested in helping to grow a product that helps the world automate their work so they can get back to living, this may be the right challenge for you!