Novel class discovery (NCD) aims to infer novel categories in an unlabeled dataset leveraging prior knowledge of a labeled set comprising disjoint but related classes. Existing research focuses primarily on utilizing the labeled set at the methodological level, with less emphasis on the analysis of the labeled set itself. Thus, in this paper, we rethink novel class discovery from the labeled set and focus on two core questions: (i) Given a specific unlabeled set, what kind of labeled set can best support novel class discovery? (ii) A fundamental premise of NCD is that the labeled set must be related to the unlabeled set, but how can we measure this relation? For (i), we propose and substantiate the hypothesis that NCD could benefit more from a labeled set with a large degree of semantic similarity to the unlabeled set. Specifically, we establish an extensive and large-scale benchmark with varying degrees of semantic similarity between labeled/unlabeled datasets on ImageNet by leveraging its hierarchical class structure. As a sharp contrast, the existing NCD benchmarks are developed based on labeled sets with different number of categories and images, and completely ignore the semantic relation. For (ii), we introduce a mathematical definition for quantifying the semantic similarity between labeled and unlabeled sets. In addition, we use this metric to confirm the validity of our proposed benchmark and demonstrate that it highly correlates with NCD performance. Furthermore, without quantitative analysis, previous works commonly believe that label information is always beneficial. However, counterintuitively, our experimental results show that using labels may lead to sub-optimal outcomes in low-similarity settings.

In medical genetics, each genetic variant is evaluated as an independent entity regarding its clinical importance. However, in most complex diseases, variant combinations in specific gene networks, rather than the presence of a particular single variant, predominates. In the case of complex diseases, disease status can be evaluated by considering the success level of a team of specific variants. We propose a high dimensional modelling based method to analyse all the variants in a gene network together. To evaluate our method, we selected two gene networks, mTOR and TGF-Beta. For each pathway, we generated 400 control and 400 patient group samples. mTOR and TGF-? pathways contain 31 and 93 genes of varying sizes, respectively. We produced Chaos Game Representation images for each gene sequence to obtain 2-D binary patterns. These patterns were arranged in succession, and a 3-D tensor structure was achieved for each gene network. Features for each data sample were acquired by exploiting Enhanced Multivariance Products Representation to 3-D data. Features were split as training and testing vectors. Training vectors were employed to train a Support Vector Machines classification model. We achieved more than 96% and 99% classification accuracies for mTOR and TGF-Beta networks, respectively, using a limited amount of training samples.

Technology has always had a significant impact on supply chains. In 2023, it is going to be more important than ever as the "Great Supply Chain Disruption" continues to challenge organisations around the world, according to SAPICS (The Professional Body for Supply Chain Management in Southern Africa). War, raw materials shortages, rising energy costs and extreme weather conditions are just some of the factors that will disrupt global supply chains in 2023, warns the non-profit organisation that aims to elevate, educate and empower the community of supply chain professionals across Africa. "In South Africa, the electricity crisis will continue to challenge businesses across all sectors. The negative impact on energy-intensive and irrigation dependent agricultural industries in particular will resonate through the entire supply chain – from the farm to consumers, who will have to pay more and have fewer competitive options available on supermarket shelves," says SAPICS president MJ Schoemaker.

The Global Machine Learning as a Service (MLaaS) Market 2032 Industry Report is a professional and in-depth study on the current state of the Machine Learning as a Service (MLaaS) Market by QMI. The Machine Learning as a Service (MLaaS) Market is supposed to demonstrate a considerable growth during the forecast period of 2023 – 2032. The company profiles of all the key players and brands that are dominating the market have been given in this report. Their moves like product launches, joint ventures, mergers and acquisitions and the respective effect on the sales, import, export, revenue and CAGR values have been studied completely in the report. The scope of this Machine Learning as a Service (MLaaS) Market report can be expanded from market scenarios to comparative pricing between major players.

It made you realise how, despite all the sophistication of modern society, we're still reliant on water falling from the sky." Sam Alfred, the lead designer at Cape Town-based video game studio Free Lives, vividly remembers his city nearly running out of water. During 2018, the area surrounding South Africa's second largest city suffered months of dwindling rainfall. Dams were unable to replenish themselves at the rate its inhabitants required. The situation even called for its own grim version of the Doomsday Clock: hour by hour, the city ticked ever closer to Day Zero, marking the end of its fresh water supply. Terra Nil, the video game that Alfred has been developing since 2019, is a response to these terrifying events. Dubbed a "city-builder in reverse", it foregoes the consumption and expansion of genre classics such as Civilisation and SimCity to paint a picture of environmental restoration. At light-speed, and with eye-massaging flushes of emerald green and azure blue, the environment transforms into lush vegetation. Terra Nil's simplicity is as beautiful as its visuals, offering the satisfaction of a colouring book while doling out a clear-eyed critique of environment-wrecking extraction. With Terra Nil's story of "climate positivity", Alfred is part of a burgeoning wave of game makers attempting to both educate players on the dangers of the climate crisis while stretching perceptions of what is possible in response to it. Niantic, the maker of Pokémon GO, has used the real-world setting of its augmented reality game to spearhead a tree-planting initiative. Ubisoft, meanwhile, staged an in-game climate march for Riders Republic players, and is set to unleash a virtual forest fire to demonstrate the devastating real-world effects of such arboreal disasters. The idea with each of these ventures is to use video games as tools of moral instruction. For the past three years, a United Nations project called Playing for the Planet has catalysed these efforts with its annual Green Game Jam. Deborah Mensah-Bonsu, founder of partner organisation Games for Good and organiser of the jams, believes video games are perfectly placed to encourage changes in mindset and behaviour. "The idea of player agency is a really big piece [of the picture]," she says. With games, you get to be part of a story – you have a say in its outcome."

Since its founding in 1998, Vecna Technologies has developed a number of ways to help hospitals care for patients. The company has produced intake systems to respond to Covid-19 patient surges, prediction systems to manage health complications in maternity wards, and telepresence robots that have allowed sick people to stay connected with friends and loved ones. The differences among those products have also led to a number of transformations and spinoffs, including material handling company Vecna Robotics and the health care nonprofit VecnaCares. Vecna Technologies co-founders Deborah Noel Theobald '95 and Daniel Theobald '95, SM '98 say each of those pivots has been driven by a desire to build a robotics company that makes a positive impact on the world. "We knew we wanted to do robotics and do something good in the world," Deborah says of the team's mindset.

We introduce MusicLM, a model generating high-fidelity music from text descriptions such as "a calming violin melody backed by a distorted guitar riff". MusicLM casts the process of conditional music generation as a hierarchical sequence-to-sequence modeling task, and it generates music at 24 kHz that remains consistent over several minutes. Our experiments show that MusicLM outperforms previous systems both in audio quality and adherence to the text description. Moreover, we demonstrate that MusicLM can be conditioned on both text and a melody in that it can transform whistled and hummed melodies according to the style described in a text caption. To support future research, we publicly release MusicCaps, a dataset composed of 5.5k music-text pairs, with rich text descriptions provided by human experts.

One of the most challenging and consequential problems in climate modeling is to provide probabilistic projections of sea level rise. A large part of the uncertainty of sea level projections is due to uncertainty in ice sheet dynamics. At the moment, accurate quantification of the uncertainty is hindered by the cost of ice sheet computational models. In this work, we develop a hybrid approach to approximate existing ice sheet computational models at a fraction of their cost. Our approach consists of replacing the finite element model for the momentum equations for the ice velocity, the most expensive part of an ice sheet model, with a Deep Operator Network, while retaining a classic finite element discretization for the evolution of the ice thickness. We show that the resulting hybrid model is very accurate and it is an order of magnitude faster than the traditional finite element model. Further, a distinctive feature of the proposed model compared to other neural network approaches, is that it can handle high-dimensional parameter spaces (parameter fields) such as the basal friction at the bed of the glacier, and can therefore be used for generating samples for uncertainty quantification. We study the impact of hyper-parameters, number of unknowns and correlation length of the parameter distribution on the training and accuracy of the Deep Operator Network on a synthetic ice sheet model. We then target the evolution of the Humboldt glacier in Greenland and show that our hybrid model can provide accurate statistics of the glacier mass loss and can be effectively used to accelerate the quantification of uncertainty.

Brain tumors are one of the life-threatening forms of cancer. Previous studies have classified brain tumors using deep neural networks. In this paper, we perform the later task using a collaborative deep learning technique, more specifically split learning. Split learning allows collaborative learning via neural networks splitting into two (or more) parts, a client-side network and a server-side network. The client-side is trained to a certain layer called the cut layer. Then, the rest of the training is resumed on the server-side network. Vertical distribution, a method for distributing data among organizations, was implemented where several hospitals hold different attributes of information for the same set of patients. To the best of our knowledge this paper will be the first paper to implement both split learning and vertical distribution for brain tumor classification. Using both techniques, we were able to achieve train and test accuracy greater than 90\% and 70\%, respectively.

How best to incorporate historical data to "warm start" bandit algorithms is an open question: naively initializing reward estimates using all historical samples can suffer from spurious data and imbalanced data coverage, leading to computational and storage issues $\unicode{x2014}$ particularly salient in continuous action spaces. We propose Artificial Replay, a meta-algorithm for incorporating historical data into any arbitrary base bandit algorithm. Artificial Replay uses only a fraction of the historical data compared to a full warm-start approach, while still achieving identical regret for base algorithms that satisfy independence of irrelevant data (IIData), a novel and broadly applicable property that we introduce. We complement these theoretical results with experiments on $K$-armed and continuous combinatorial bandit algorithms, including a green security domain using real poaching data. We show the practical benefits of Artificial Replay, including for base algorithms that do not satisfy IIData.