The problem of computing minimally sparse solutions of under-determined linear systems is $NP$ hard in general. Subsets with extra properties, may allow efficient algorithms, most notably problems with the restricted isometry property (RIP) can be solved by convex $\ell_1$-minimization. While these classes have been very successful, they leave out many practical applications. In this paper, we consider adaptable classes that are tractable after training on a curriculum of increasingly difficult samples. The setup is intended as a candidate model for a human mathematician, who may not be able to tackle an arbitrary proof right away, but may be successful in relatively flexible subclasses, or areas of expertise, after training on a suitable curriculum.

This paper proposes a new variable stiffness soft gripper that enables high-performance grasping tasks in industrial applications. The design of the proposed monolithic soft gripper includes a middle bellow and two side bellows (i.e., fingers). The positions of the fingers are regulated by adjusting the negative pressure in the middle bellow actuator via an on-off controller. The stiffness of the soft gripper is modulated by controlling the positive pressure in the fingers through the use of a proportional air-pressure regulator. It is experimentally shown that the proposed soft gripper can modulate its stiffness by 125% within 250ms. It is also shown that the variable stiffness soft gripper can help improve the safety and performance of grasping tasks in industrial applications.

Officials had to close Mullaloo Beach in Perth, Western Australia, for the second time in a week on Monday, January 9, after a tiger shark was spotted swimming close to the shore. Check out this video, taken from a drone. A 16-year-old girl died after a shark mauled her while swimming in the Swan River in Australia, with only a teen diving in to save her as others watched in horror. "A female received injuries after being bitten by an unknown species of shark at approximately 3.35pm on Feb. 4 2023," the Department of Primary Industries and Regional Development (DPIRD) said of the incident. "DPIRD is working with WA Police and local authorities to coordinate responses. A DPIRD Fisheries vessel is on the water monitoring the area, and DPIRD officers are conducting land-based patrols."

According to the White House, the informal Quad alliance of Australia, India, Japan, and the United States has decided to deploy machine learning and other cutting-edge technology to improve cyber security. During a meeting of the Quad Senior Cyber Group on January 30 and 31, representatives from Australia, India, Japan, and the United States reaffirmed their commitment to advancing an inclusive, free, and open Indo-Pacific region, according to the statement. The Group pledged to use machine learning and related cutting-edge technologies in the long run to improve cyber security and create secure channels for private sector threat information sharing and Computer Emergency Response Teams (CERT), according to a statement released by the White House on Thursday. It added that these goals are a key component of the group's forward-thinking, cutting-edge work plan. The group also committed to developing a framework and methodology for ensuring supply chain security and resilience for information communication technologies (ICT) and operational technology (OT) systems of critical sectors.

This summer, a coding class offered by a private school in Austin, Texas, was led by an unusual teacher. The PreK-8 school, Paragon Prep, offered a series of optional, self-paced, video lessons that were automatically generated from a textbook. In them, an animated avatar made to look like the 19th-century computing pioneer Ada Lovelace taught the basics of the Python programming language. "We'll also look at basic concepts of data analysis, using NumPy as well as Pandas," said the avatar in a female computer voice that sounds more like the iPhone's Siri than like a 19th-century British mathematician, her mouth moving clumsily as she speaks. "If you have no idea what any of that means, that's perfectly fine, good and normal. This course was meant for anyone interested in becoming a future software engineer or data scientist, not someone who is already one."

Data science research into fake news has gathered much momentum in recent years, arguably facilitated by the emergence of large public benchmark datasets. While it has been well-established within media studies that gender bias is an issue that pervades news media, there has been very little exploration into the relationship between gender bias and fake news. In this work, we provide the first empirical analysis of gender bias vis-a-vis fake news, leveraging simple and transparent lexicon-based methods over public benchmark datasets. Our analysis establishes the increased prevalance of gender bias in fake news across three facets viz., abundance, affect and proximal words. The insights from our analysis provide a strong argument that gender bias needs to be an important consideration in research into fake news.

A New cross-domain strategy based XAI models for fake news detection v0.1.1 ABSTRACT The Advancement in technology and rapid usage of social media has made communication easier and faster than ever before. Fake news threatens the community, democracy, egalitarianism and people's trust. Cross-domain text classification is a task of a model adopting a target domain by using the knowledge of the source domain. Natural Language Processing and Deep Learning models are used to identify misleading information. Explainability is crucial in understanding the behaviour of these complex models. In this study, we propose a four-level cross-domain strategy to study the impact of explainability on cross-domain models. The latest findings in the natural language process, the "Bidirectional Encoder Representations from Transformers" (BERT) model published by Devlin et al. (2018) google used to implement the concept of transfer learning. A fine-tune BERT model is used to perform cross-domain classification. Using this model, we conducted four experiments using datasets from different domains. Explanatory models like Anchor, ELI5, LIME and SHAP are used to design a novel explainable approach to cross-domain levels. The experimental analysis has given an ideal pair of XAI models on different levels of cross-domain. INTRODUCTION Nowadays, social media has become a potential influencing tool. According to the statistics published by Datareportal in July 2022, there is exponential growth in social media platforms, declaring that more than half of the world's population (59 per cent) is using them. Consequently, these platforms have deterministic effects on people's lives and the integrity of societies and local communities. Groups of people forming social media clusters use, unfortunately, these tools to spread speculation - so-called "fake news". In 2008, a journalist posted a report about Steve jobs medical condition. It has created massive confusion and controversy within societies and led to fluctuations in the stock price of Apple Inc. Rubin (2017). During the Covid-19 pandemic, fake news was largely spread among people and has created panic within societies. Recent statistics published by the United States support receiving reports from 80 per cent of consumers about the fake news outbreak. Insufficient data is one of the reasons behind unreliable communication, making it difficult to distinguish fake from real news. In 2016, fake news was popular mainly during the United States elections. They have created a great source of influence on people's opinions about two constants.

In mining, grade control generally focuses on blast hole sampling and the estimation of ore control block models with little or no attention given to how the materials are being excavated from the ground. In the process of loading trucks, the underlying variability of the individual bucket load will determine the variability of truck payload. Hence, accurate material movement demands a good knowledge of the excavation process and the buckets interaction with the environment. However, equipment frequently goes into off nominal states due to unexpected delays, disturbances or faults. The large amount of such disturbances causes information loss that reduces the statistical power and biases estimates, leading to increased uncertainty in the production. A reliable method that inferences the missing knowledge about the interaction between the machine and the environment from the available data sources, is vital to accurately model the material movement. In this study, a twostep method was implemented that performed unsupervised clustering and then predicted the missing information. The first method is DBSCAN based spatial clustering which divides the diggers and buckets positional data into connected loading segments. Clear patterns of segmented bucket dig positions were observed. The second model utilized Gaussian process regression which was trained with the clustered data and the model was then used to infer the mean locations of the test clusters. Bucket dig locations were then simulated at the inferred mean locations for different durations and compared against the known bucket dig locations. This method was tested at an open pit mine in the Pilbara of Western Australia. The results demonstrate the advantage of the proposed method in inferencing the missing information of bucket environment interactions and therefore enables miners to continuously track the material movement.

The application of graph representation learning techniques to the area of financial risk management (FRM) has attracted significant attention recently. However, directly modeling transaction networks using graph neural models remains challenging: Firstly, transaction networks are directed multigraphs by nature, which could not be properly handled with most of the current off-the-shelf graph neural networks (GNN). Secondly, a crucial problem in FRM scenarios like anti-money laundering (AML) is to identify risky transactions and is most naturally cast into an edge classification problem with rich edge-level features, which are not fully exploited by the prevailing GNN design that follows node-centric message passing protocols. In this paper, we present a systematic investigation of design aspects of neural models over directed multigraphs and develop a novel GNN protocol that overcomes the above challenges via efficiently incorporating directional information, as well as proposing an enhancement that targets edge-related tasks using a novel message passing scheme over an extension of edge-to-node dual graph. A concrete GNN architecture called GRANDE is derived using the proposed protocol, with several further improvements and generalizations to temporal dynamic graphs. We apply the GRANDE model to both a real-world anti-money laundering task and public datasets. Experimental evaluations show the superiority of the proposed GRANDE architecture over recent state-of-the-art models on dynamic graph modeling and directed graph modeling.

We propose a learning framework for calibrating predictive models to make loss-controlling prediction for exchangeable data, which extends our recently proposed conformal loss-controlling prediction for more general cases. By comparison, the predictors built by the proposed loss-controlling approach are not limited to set predictors, and the loss function can be any measurable function without the monotone assumption. To control the loss values in an efficient way, we introduce transformations preserving exchangeability to prove finite-sample controlling guarantee when the test label is obtained, and then develop an approximation approach to construct predictors. The transformations can be built on any predefined function, which include using optimization algorithms for parameter searching. This approach is a natural extension of conformal loss-controlling prediction, since it can be reduced to the latter when the set predictors have the nesting property and the loss functions are monotone. Our proposed method is applied to selective regression and high-impact weather forecasting problems, which demonstrates its effectiveness for general loss-controlling prediction.