"Look with thine ears," Lear tells poor blind Gloucester, and that is exactly what the rest of us should do now we know that the majority of New York theatres will not open their doors until, at the most optimistic guesstimate, the middle of next year. Zoom fatigue set in months ago, but audio is stepping into the breach to take us places that glazed screen-gazing can't. The eyes tend toward the literal, while what we only hear can bloom, the way a novel does, in the privacy of the mind, as is the case with two new productions--one radical, one retro--that use audio to light a path forward for performance in the COVID era and beyond. "A Thousand Ways" (produced by the Brooklyn-based ArKtype) was created by the duo Abigail Browde and Michael Silverstone, who go by the moniker 600 Highwaymen and are known for devising inventive, sincere theatre of a kind that makes urbane audiences fatted on cynicism feel wonder afresh. In "This Great Country," from 2012, seventeen performers, some experienced, some green, acted out scenes from "Death of a Salesman," transforming that classic into something rich and strange; "Employee of the Year," staged in 2014, had five girls under the age of eleven tell the story of one woman's adulthood.

Mining is a traditionally analogue business. After all, the industry's symbol worldwide is a hammer and pick. Yet, despite the sector's antiquated reputation, some major mining companies are taking a progressive stance and proving digitisation and automation can achieve much better operational outcomes. Known as Mine 4.0, the industry is seeing digital transformation creep into everything from trucks, drills and trains to back-office processes, such as procurement and supply chain logistics. Miners have very little control over the revenue side of their business, as the global commodities crash of 2014 to 2015, when prices plunged by more than 30 per cent, and indeed the coronavirus epidemic demonstrate.

Transfer learning is a useful machine learning framework that allows one to build task-specific models (student models) without significantly incurring training costs using a single powerful model (teacher model) pre-trained with a large amount of data. The teacher model may contain private data, or interact with private inputs. We investigate if one can leak or infer such private information without interacting with the teacher model directly. We describe such inference attacks in the context of face recognition, an application of transfer learning that is highly sensitive to personal privacy. Under black-box and realistic settings, we show that existing inference techniques are ineffective, as interacting with individual training instances through the student models does not reveal information about the teacher. We then propose novel strategies to infer from aggregate-level information. Consequently, membership inference attacks on the teacher model are shown to be possible, even when the adversary has access only to the student models. We further demonstrate that sensitive attributes can be inferred, even in the case where the adversary has limited auxiliary information. Finally, defensive strategies are discussed and evaluated. Our extensive study indicates that information leakage is a real privacy threat to the transfer learning framework widely used in real-life situations.

Boolean tensor has been broadly utilized in representing high dimensional logical data collected on spatial, temporal and/or other relational domains. Boolean Tensor Decomposition (BTD) factorizes a binary tensor into the Boolean sum of multiple rank-1 tensors, which is an NP-hard problem. Existing BTD methods have been limited by their high computational cost, in applications to large scale or higher order tensors. In this work, we presented a computationally efficient BTD algorithm, namely \textit{Geometric Expansion for all-order Tensor Factorization} (GETF), that sequentially identifies the rank-1 basis components for a tensor from a geometric perspective. We conducted rigorous theoretical analysis on the validity as well as algorithemic efficiency of GETF in decomposing all-order tensor. Experiments on both synthetic and real-world data demonstrated that GETF has significantly improved performance in reconstruction accuracy, extraction of latent structures and it is an order of magnitude faster than other state-of-the-art methods.

El-Sisi called for upgrading post offices nationwide, as well as automating the litigation system as per the "Digital Egypt Justice" strategy, presidential spokesman Bassam Rady said in a statement following the meeting. El-Sisi demanded the "immediate" start of the "Digital Egypt" initiative, which he said would directly contribute to the country's efforts in digital transformation, and enhance the digital skills of human resources to provide Egyptians with quality digitised services. Minister Talaat reviewed the latest developments concerning launching a platform for urban community models that are meant to govern the construction system in Egypt and deter building violations and land transgressions. The meeting also touched upon the latest domestic projects in electronic design and manufacturing, which is part of the state's vision to assist companies that work in the field to reach international markets, attract direct foreign investments and enhance electronic trade, according to the statement. The meeting also discussed the situation of other national projects in the communications and information technology sectors.

Network pruning is widely used to lighten and accelerate neural network models. Structured network pruning discards the whole neuron or filter, leading to accuracy loss. In this work, we propose a novel concept of neuron merging applicable to both fully connected layers and convolution layers, which compensates for the information loss due to the pruned neurons/filters. Neuron merging starts with decomposing the original weights into two matrices/tensors. One of them becomes the new weights for the current layer, and the other is what we name a scaling matrix, guiding the combination of neurons. If the activation function is ReLU, the scaling matrix can be absorbed into the next layer under certain conditions, compensating for the removed neurons. We also propose a data-free and inexpensive method to decompose the weights by utilizing the cosine similarity between neurons. Compared to the pruned model with the same topology, our merged model better preserves the output feature map of the original model; thus, it maintains the accuracy after pruning without fine-tuning. We demonstrate the effectiveness of our approach over network pruning for various model architectures and datasets. As an example, for VGG-16 on CIFAR-10, we achieve an accuracy of 93.16% while reducing 64% of total parameters, without any fine-tuning. The code can be found here: https://github.com/friendshipkim/neuron-merging

Rapid advances in technology, connectivity and telecommunications are conspiring to make Africa's large, rapidly growing population a valuable asset for the automation revolution. It is imperative that Africa quickly develop agency in data and artificial intelligence and it will be lucrative for investors who support them by financing Africa's telecom and data backbone. Africa must urgently develop cogent digital strategy. This at first seems fanciful, or even superfluous, given the continent's relative lack of more basic development. Indeed, there are myriad other challenges to which most would assign primacy.

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A new research Titled "Global Artificial Intelligence Platform Market 2020 Research Report" provides the Professional and In-depth evaluation of scope of current and future market and review of Product Specification, market trend, product type and production analysis considering major factors such as Facts and figure, revenue generated from the sales of this Report, market share and growth rate for each type and application, Gross Margin, key factors driving to the market. The Artificial Intelligence Platform market will reach Volume Million USD in 2019 and CAGR xx% 2015-2019. The report Primarly enlists the basic details of industry based on the fundamental overview of Artificial Intelligence Platform market chain structure, and describes industry surroundings, the development of the market through upstream & downstream, industry overall, investment analysis, manufacturing cost structure, industry policies, plans and development, key players will drive key business decisions and makes a scientific prediction for the development industry prospects on the basis of past, present and forecast data related to the Artificial Intelligence Platform market from 2020-2024. The Scope of the global Artificial Intelligence Platform market mainly focuses on globally, it primarily covers the Artificial Intelligence Platform Market in USA, Canada and Mexico, Artificial Intelligence Platform Market in Germany, France, UK, Russia and Italy, global Artificial Intelligence Platform market in China, Japan, Korea, India and Southeast Asia, global Artificial Intelligence Platform market in Brazil, Argentina, Columbia, Global market in Saudi Arabia, UAE, Egypt, Nigeria and South Africa. The firstly global Artificial Intelligence Platform market describes the market overview, Upstream, Technology, Cost Structure.

Computing technology has become pervasive and with it the expectation for its ready availability when needed, thus basically at all times. Dependability is the set of techniques to build, configure, operate, and manage computer systems to ensure that they are reliable, available, safe, and secure.1 But alas, faults seem to be inherent to computer systems. Components can simply crash or produce incorrect output due to hardware or software bugs or can be invaded by impostors that orchestrate their behavior. Fault tolerance is the ability to enable a system as a whole to continue operating correctly and with acceptable performance, even if some of its components are faulty.3 Fault tolerance is not new; von Neumann himself designed techniques for computers to survive faults.4