Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. India reversed a laptop licensing policy after behind-the-scenes lobbying by U.S. officials, who however remain concerned about New Delhi's compliance with WTO obligations and new rules it may issue, according to U.S. trade officials and government emails seen by Reuters. In August, India imposed rules requiring firms like Apple, Dell and HP to obtain licences for all shipments of imported laptops, tablets, personal computers and servers, raising fears that the process could slow down sales. But New Delhi rolled back the policy within weeks, saying it will only monitor the imports and decide on next steps a year later.

Discovering a suitable coordinate transformation for nonlinear systems enables the construction of simpler models, facilitating prediction, control, and optimization for complex nonlinear systems. To that end, Koopman operator theory offers a framework for global linearization for nonlinear systems, thereby allowing the usage of linear tools for design studies. In this work, we focus on the identification of global linearized embeddings for canonical nonlinear Hamiltonian systems through a symplectic transformation. While this task is often challenging, we leverage the power of deep learning to discover the desired embeddings. Furthermore, to overcome the shortcomings of Koopman operators for systems with continuous spectra, we apply the lifting principle and learn global cubicized embeddings. Additionally, a key emphasis is paid to enforce the bounded stability for the dynamics of the discovered embeddings. We demonstrate the capabilities of deep learning in acquiring compact symplectic coordinate transformation and the corresponding simple dynamical models, fostering data-driven learning of nonlinear canonical Hamiltonian systems, even those with continuous spectra.

We present a framework for learning Hamiltonian systems using data. This work is based on the lifting hypothesis, which posits that nonlinear Hamiltonian systems can be written as nonlinear systems with cubic Hamiltonians. By leveraging this, we obtain quadratic dynamics that are Hamiltonian in a transformed coordinate system. To that end, for given generalized position and momentum data, we propose a methodology to learn quadratic dynamical systems, enforcing the Hamiltonian structure in combination with a symplectic auto-encoder. The enforced Hamiltonian structure exhibits long-term stability of the system, while the cubic Hamiltonian function provides relatively low model complexity. For low-dimensional data, we determine a higher-order transformed coordinate system, whereas, for high-dimensional data, we find a lower-order coordinate system with the desired properties. We demonstrate the proposed methodology by means of both low-dimensional and high-dimensional nonlinear Hamiltonian systems.

The propensity of abstractive summarization models to make factual errors has been studied extensively, including design of metrics to detect factual errors and annotation of errors in current systems' outputs. However, the ever-evolving nature of summarization systems, metrics, and annotated benchmarks makes factuality evaluation a moving target, and drawing clear comparisons among metrics has become increasingly difficult. In this work, we aggregate factuality error annotations from nine existing datasets and stratify them according to the underlying summarization model. We compare performance of state-of-the-art factuality metrics, including recent ChatGPT-based metrics, on this stratified benchmark and show that their performance varies significantly across different types of summarization models. Critically, our analysis shows that much of the recent improvement in the factuality detection space has been on summaries from older (pre-Transformer) models instead of more relevant recent summarization models. We further perform a finer-grained analysis per error-type and find similar performance variance across error types for different factuality metrics. Our results show that no one metric is superior in all settings or for all error types, and we provide recommendations for best practices given these insights.

Sanskrit is a classical language with about 30 million extant manuscripts fit for digitisation, available in written, printed or scannedimage forms. However, it is still considered to be a low-resource language when it comes to available digital resources. In this work, we release a post-OCR text correction dataset containing around 218,000 sentences, with 1.5 million words, from 30 different books. Texts in Sanskrit are known to be diverse in terms of their linguistic and stylistic usage since Sanskrit was the 'lingua franca' for discourse in the Indian subcontinent for about 3 millennia. Keeping this in mind, we release a multi-domain dataset, from areas as diverse as astronomy, medicine and mathematics, with some of them as old as 18 centuries. Further, we release multiple strong baselines as benchmarks for the task, based on pre-trained Seq2Seq language models. We find that our best-performing model, consisting of byte level tokenization in conjunction with phonetic encoding (Byt5+SLP1), yields a 23% point increase over the OCR output in terms of word and character error rates. Moreover, we perform extensive experiments in evaluating these models on their performance and analyse common causes of mispredictions both at the graphemic and lexical levels. Our code and dataset is publicly available at https://github.com/ayushbits/pe-ocr-sanskrit.

Learning dynamical models from data plays a vital role in engineering design, optimization, and predictions. Building models describing dynamics of complex processes (e.g., weather dynamics, or reactive flows) using empirical knowledge or first principles are onerous or infeasible. Moreover, these models are high-dimensional but spatially correlated. It is, however, observed that the dynamics of high-fidelity models often evolve in low-dimensional manifolds. Furthermore, it is also known that for sufficiently smooth vector fields defining the nonlinear dynamics, a quadratic model can describe it accurately in an appropriate coordinate system, conferring to the McCormick relaxation idea in nonconvex optimization. Here, we aim at finding a low-dimensional embedding of high-fidelity dynamical data, ensuring a simple quadratic model to explain its dynamics. To that aim, this work leverages deep learning to identify low-dimensional quadratic embeddings for high-fidelity dynamical systems. Precisely, we identify the embedding of data using an autoencoder to have the desired property of the embedding. We also embed a Runge-Kutta method to avoid the time-derivative computations, which is often a challenge. We illustrate the ability of the approach by a couple of examples, arising in describing flow dynamics and the oscillatory tubular reactor model.

Discovering dynamical models to describe underlying dynamical behavior is essential to draw decisive conclusions and engineering studies, e.g., optimizing a process. Experimental data availability notwithstanding has increased significantly, but interpretable and explainable models in science and engineering yet remain incomprehensible. In this work, we blend machine learning and dictionary-based learning with numerical analysis tools to discover governing differential equations from noisy and sparsely-sampled measurement data. We utilize the fact that given a dictionary containing huge candidate nonlinear functions, dynamical models can often be described by a few appropriately chosen candidates. As a result, we obtain interpretable and parsimonious models which are prone to generalize better beyond the sampling regime. Additionally, we integrate a numerical integration framework with dictionary learning that yields differential equations without requiring or approximating derivative information at any stage. Hence, it is utterly effective in corrupted and sparsely-sampled data. We discuss its extension to governing equations, containing rational nonlinearities that typically appear in biological networks. Moreover, we generalized the method to governing equations that are subject to parameter variations and externally controlled inputs. We demonstrate the efficiency of the method to discover a number of diverse differential equations using noisy measurements, including a model describing neural dynamics, chaotic Lorenz model, Michaelis-Menten Kinetics, and a parameterized Hopf normal form.

If Facebook has an unofficial slogan, an equivalent to Google's "Don't Be Evil" or Apple's "Think Different," it is "Move Fast and Break Things." It means, at least in theory, that one should iterate to try news things and not be afraid of the possibility of failure. In 2021, however, with social media currently being blamed for a plethora of societal ills, the phrase should, perhaps, be modified to: "Move Fast and Fix Things." One of the many areas social media, not just Facebook, has been pilloried for is its spreading of certain images online. It's a challenging problem by any stretch of the imagination: Some 4,000 photo uploads are made to Facebook every single second.

With the rapid development in sensor and measurement technology, time-series data of processes have become available in large amounts with high accuracy. Machine learning and data science play an important role in analyzing and perceiving information of the underlying process dynamics from these data. Building a model describing the dynamics is vital in designing and optimizing various processes, as well as predicting their long-term transient behavior. Inferring a dynamic process model from data, often called system identification, has a rich history; see, e.g., [30,46]. While linear system identification is well established, nonlinear system identification is still far from being as good understood as for linear systems, despite having a similarly long research history, see, e.g., [25, 44]. Nonlinear system identification often relies on a good hypothesis of the model; thus, it is not entirely a black-box technology. Fortunately, there are several scenarios where one can hypothesize a model structure based on a good understanding of the underlying dynamic behavior using expert knowledge or experience. Towards nonlinear system identification, a promising approach based on a symbolic regression was proposed [4] to determine the potential structure of a nonlinear system.

Conversational AI agency VERSA is to partner Mogaé in a joint venture. Announcing a Diwali India launch, VERSA said its expansion into this country is to capitalise on demand for specialised conversational strategy and design in a market with a population of more than 1.3 billion people, and an installed base of nearly a billion mobile phones. VERSA India will be a 50/50 joint venture between VERSA (Headquartered in Melbourne, Australia; with US operations out of Seattle) and Mogaé Consultants, owned by Sandeep & Tanya Goyal. Dr. Sandeep Goyal, is a well-known advertising & media veteran who has been a past President of Rediffusion, ex-Group CEO of Zee Telefilms and former Founder Chairman of Dentsu India. Tanya Goyal has been a six-term member of the Governing Council of the Advertising Agencies Association of India (AAAI) and was Executive Director of Dentsu India.