Simulated tempering is a widely used strategy for sampling from multimodal distributions. In this paper, we consider simulated tempering combined with an arbitrary local Markov chain Monte Carlo sampler and present a new decomposition theorem that provides a lower bound on the restricted spectral gap of the algorithm for sampling from mixture distributions. By working with the restricted spectral gap, the applicability of our results is extended to broader settings such as when the usual spectral gap is difficult to bound or becomes degenerate. We demonstrate the application of our theoretical results by analyzing simulated tempering combined with random walk Metropolis--Hastings for sampling from mixtures of Gaussian distributions. We show that in fixed-dimensional settings, the algorithm's complexity scales polynomially with the separation between modes and logarithmically with $1/\varepsilon$, where $\varepsilon$ is the target accuracy in total variation distance.

G-Quadruplexes are the four-stranded non-canonical nucleic acid secondary structures, formed by the stacking arrangement of the guanine tetramers. They are involved in a wide range of biological roles because of their exceptionally unique and distinct structural characteristics. After the completion of the human genome sequencing project, a lot of bioinformatic algorithms were introduced to predict the active G4s regions \textit{in vitro} based on the canonical G4 sequence elements, G-\textit{richness}, and G-\textit{skewness}, as well as the non-canonical sequence features. Recently, sequencing techniques like G4-seq and G4-ChIP-seq were developed to map the G4s \textit{in vitro}, and \textit{in vivo} respectively at a few hundred base resolution. Subsequently, several machine learning approaches were developed for predicting the G4 regions using the existing databases. However, their prediction models were simplistic, and the prediction accuracy was notably poor. In response, here, we propose a novel convolutional neural network with Bi-LSTM and attention layers, named G4-attention, to predict the G4 forming sequences with improved accuracy. G4-attention achieves high accuracy and attains state-of-the-art results in the G4 prediction task. Our model also predicts the G4 regions accurately in the highly class-imbalanced datasets. In addition, the developed model trained on the human genome dataset can be applied to any non-human genome DNA sequences to predict the G4 formation propensities.

Vinton G. Cerf wonders "whether there is any possibility of establishing'watcher networks'" in his October 2022 Communications "Cerf's Up" column. I must point out to all who have the same concern about "who will watch the watchers" that Philip K. Dick describes this problem in his story The Minority Report (see wikipedia https://bit.ly/2XlQcSA) It works often, but not always. So the question arises: How much authority are we willing to provide for AI and is the concept of three AIs working independently on the same problem a feasible solution. I agree with Cerf that we need to come up with a solution before the problem overwhelms us. In the December 2022 Communications, there is a compelling column by Vinton G. Cerf, "On Truth and Belief," which exemplifies the growing worry about agreement, polarization, and the nature of truth.

The Synthetic Control method has pioneered a class of powerful data-driven techniques to estimate the counterfactual reality of a unit from donor units. At its core, the technique involves a linear model fitted on the pre-intervention period that combines donor outcomes to yield the counterfactual. However, linearly combining spatial information at each time instance using time-agnostic weights fails to capture important inter-unit and intra-unit temporal contexts and complex nonlinear dynamics of real data. We instead propose an approach to use local spatiotemporal information before the onset of the intervention as a promising way to estimate the counterfactual sequence. To this end, we suggest a Transformer model that leverages particular positional embeddings, a modified decoder attention mask, and a novel pre-training task to perform spatiotemporal sequence-to-sequence modeling. Our experiments on synthetic data demonstrate the efficacy of our method in the typical small donor pool setting and its robustness against noise. We also generate actionable healthcare insights at the population and patient levels by simulating a state-wide public health policy to evaluate its effectiveness, an in silico trial for asthma medications to support randomized controlled trials, and a medical intervention for patients with Friedreich's ataxia to improve clinical decision-making and promote personalized therapy.

Deep learning (DL) models have enjoyed tremendous success across application domains within the broader umbrella of artificial intelligence (AI) technologies. However, their "black-box" nature, coupled with their extensive use across application sectors--including safety-critical and risk-sensitive ones such as healthcare, finance, aerospace, law enforcement, and governance--has elicited an increasing need for explainability, interpretability, and transparency of decision-making in these models.11,14,18,24 With the recent progression of legal and policy frameworks that mandate explaining decisions made by AI-driven systems (for example, the European Union's GDPR Article 15(1)(h) and the Algorithmic Accountability Act of 2019 in the U.S.), explainability has become a cornerstone of responsible AI use and deployment. In the Indian context, NITI Aayog recently released a two-part strategy document on envisioning and operationalizing Responsible AI in India,15,16 which puts significant emphasis on the explainability and transparency of AI models. Explainability of DL models lies at the human-machine interface, and different users may expect different explanations in different contexts.

Nigeria and India are moving to strengthen ties in areas of fintech, artificial intelligence, scientific development and solar energy, according to Gangadharan Balasubramanian, Indian high commissioner to Nigeria. The newly appointed envoy, who disclosed this during the commemoration of India's 76th Independence in Abuja on Monday, said the partnership between would further strengthen bilateral ties between the two countries. Balasubramanian noted that the trade and economic relations between India and Nigeria have been very strong, with over 135 Indian companies operating in Nigeria. He also said the volume of trade between both countries has increased as well as improved on both sides after the COVID-19 pandemic. "The trade volume between India and Nigeria was $14.95 billion in 2021. The trade volume has increased substantially after COVID-19, both ways," Balasubramanian said.

Despite the recent advances in the power conversion efficiency of organic solar cells, insights into the processing-driven thermo-mechanical stability of bulk heterojunction active layers are helping to advance the field. Lehigh University engineer Ganesh Balasubramanian, like many others, wondered if there were ways to improve the design of solar cells to make them more efficient? Balasubramanian, an associate professor of Mechanical Engineering and Mechanics, studies the basic physics of the materials at the heart of solar energy conversion – the organic polymers passing electrons from molecule to molecule so they can be stored and harnessed – as well as the manufacturing processes that produce commercial solar cells. Using the Frontera supercomputer at the Texas Advanced Computing Center (TACC) – one of the most powerful on the planet – Balasubramanian and his graduate student Joydeep Munshi have been running molecular models of organic solar cell production processes, and designing a framework to determine the optimal engineering choices. "When engineers make solar cells, they mix two organic molecules in a solvent and evaporate the solvent to create a mixture which helps with the exciton conversion and electron transport," Balasubramanian said.

On Dec. 1 at re:Invent 2020, Amazon Web Services (AWS) announced Amazon Lookout for Vision, an anomaly detection solution that uses machine learning to process thousands of images an hour to spot manufacturing defects and anomalies -- with no machine learning experience required. The new offering means manufacturers can send camera images to Lookout for Vision to identify defects, such as a crack in a machine part, a dent in a panel, an irregular shape, or an incorrect product color. Lookout for Vision also utilizes few shot learning, so customers can assess machine parts or manufactured products by providing small batches -- sometimes as few as 30 images (10 images of defects or anomalies plus 20 "normal" images). Lookout for Vision then reports the images that differ from baselines so that appropriate action can be taken -- quickly. Because modern manufacturing systems are so finely tuned, defect rates are often 1% or less.

The appearance of the double-descent risk phenomenon has received growing interest in the machine learning and statistics community, as it challenges well-understood notions behind the U-shaped train-test curves. Motivated through Rissanen's minimum description length (MDL), Balasubramanian's Occam's Razor, and Amari's information geometry, we investigate how the logarithm of the model volume: $\log V$, works to extend intuition behind the AIC and BIC model selection criteria. We find that for the particular model classes of isotropic linear regression and statistical lattices, the $\log V$ term may be decomposed into a sum of distinct components, each of which assist in their explanations of the appearance of this phenomenon. In particular they suggest why generalization error does not necessarily continue to grow with increasing model dimensionality.

Artificial Intelligence models and programmes mimic the functioning of human brains, helping machines learn make decisions in a more-like manner. But how exactly they arrive at decisions is unknown. In order to understand this, a group of researchers at the Indian Institute of Technology (IIT-Hyderabad) have developed a method by which the inner workings of Artificial Intelligence models can be understood in terms of causal attributes. This finding assumes significance in the wake of emergence of regulations such as General Data Protection Regulation (GDPR) that requires organisations to explain the decisions made by machine learning methods. Modern Artificial Neural Networks, also called Deep Learning (DL), have increased tremendously in complexity such that machines can train themselves to process and learn from data that has been fed to them.