PearSAN is a machine learning-assisted optimization algorithm applicable to inverse design problems with large design spaces, where traditional optimizers struggle. The algorithm leverages the latent space of a generative model for rapid sampling and employs a Pearson correlated surrogate model to predict the figure of merit of the true design metric. As a showcase example, PearSAN is applied to thermophotovoltaic (TPV) metasurface design by matching the working bands between a thermal radiator and a photovoltaic cell. PearSAN can work with any pretrained generative model with a discretized latent space, making it easy to integrate with VQ-VAEs and binary autoencoders. Its novel Pearson correlational loss can be used as both a latent regularization method, similar to batch and layer normalization, and as a surrogate training loss. We compare both to previous energy matching losses, which are shown to enforce poor regularization and performance, even with upgraded affine parameters. PearSAN achieves a state-of-the-art maximum design efficiency of 97%, and is at least an order of magnitude faster than previous methods, with an improved maximum figure-of-merit gain.

Long video understanding poses unique challenges due to their temporal complexity and low information density. Recent works address this task by sampling numerous frames or incorporating auxiliary tools using LLMs, both of which result in high computational costs. In this work, we introduce a curiosity-driven video agent with self-exploration capability, dubbed as VCA. Built upon VLMs, VCA autonomously navigates video segments and efficiently builds a comprehensive understanding of complex video sequences. Instead of directly sampling frames, VCA employs a tree-search structure to explore video segments and collect frames. Rather than relying on external feedback or reward, VCA leverages VLM's self-generated intrinsic reward to guide its exploration, enabling it to capture the most crucial information for reasoning. Experimental results on multiple long video benchmarks demonstrate our approach's superior effectiveness and efficiency.

The vanishing ideal of a set of points $X\subseteq \mathbb{R}^n$ is the set of polynomials that evaluate to $0$ over all points $\mathbf{x} \in X$ and admits an efficient representation by a finite set of polynomials called generators. To accommodate the noise in the data set, we introduce the pairwise conditional gradients approximate vanishing ideal algorithm (PCGAVI) that constructs a set of generators of the approximate vanishing ideal. The constructed generators capture polynomial structures in data and give rise to a feature map that can, for example, be used in combination with a linear classifier for supervised learning. In PCGAVI, we construct the set of generators by solving constrained convex optimization problems with the pairwise conditional gradients algorithm. Thus, PCGAVI not only constructs few but also sparse generators, making the corresponding feature transformation robust and compact. Furthermore, we derive several learning guarantees for PCGAVI that make the algorithm theoretically better motivated than related generator-constructing methods.

Combinatorial optimization problems can be solved by heuristic algorithms such as simulated annealing (SA) which aims to find the optimal solution within a large search space through thermal fluctuations. The algorithm generates new solutions through Markov-chain Monte Carlo techniques. This sampling scheme can result in severe limitations, such as slow convergence and a tendency to stay within the same local search space at small temperatures. To overcome these shortcomings, we use the variational classical annealing (VCA) framework [1] that combines autoregressive recurrent neural networks (RNNs) with traditional annealing to sample solutions that are uncorrelated. In this paper, we demonstrate the potential of using VCA as an approach to solving real-world optimization problems. We explore VCA's performance in comparison with SA at solving three popular optimization problems: the maximum cut problem (Max-Cut), the nurse scheduling problem (NSP), and the traveling salesman problem (TSP). For all three problems, we find that VCA outperforms SA on average in the asymptotic limit by one or more orders of magnitude in terms of relative error. Interestingly, we reach large system sizes of up to 256 cities for the TSP. We also conclude that in the best case scenario, VCA can serve as a great alternative when SA fails to find the optimal solution.

Vanishing component analysis (VCA) computes approximate generators of vanishing ideals of samples, which are further used for extracting nonlinear features of the samples. Recent studies have shown that normalization of approximate generators plays an important role and different normalization leads to generators of different properties. In this paper, inspired by recent self-supervised frameworks, we propose a contrastive normalization method for VCA, where we impose the generators to vanish on the target samples and to be normalized on the transformed samples. We theoretically show that a contrastive normalization enhances the discriminative power of VCA, and provide the algebraic interpretation of VCA under our normalization. Numerical experiments demonstrate the effectiveness of our method. This is the first study to tailor the normalization of approximate generators of vanishing ideals to obtain discriminative features.

Many important challenges in science and technology can be cast as optimization problems. When viewed in a statistical physics framework, these can be tackled by simulated annealing, where a gradual cooling procedure helps search for groundstate solutions of a target Hamiltonian. While powerful, simulated annealing is known to have prohibitively slow sampling dynamics when the optimization landscape is rough or glassy. Here we show that by generalizing the target distribution with a parameterized model, an analogous annealing framework based on the variational principle can be used to search for groundstate solutions. Modern autoregressive models such as recurrent neural networks provide ideal parameterizations since they can be exactly sampled without slow dynamics even when the model encodes a rough landscape. We implement this procedure in the classical and quantum settings on several prototypical spin glass Hamiltonians, and find that it significantly outperforms traditional simulated annealing in the asymptotic limit, illustrating the potential power of this yet unexplored route to optimization.

Twenty-five percent of customer service and support operations will integrate virtual customer assistant (VCA) or chatbot technology across engagement channels by 2020, up from less than two percent in 2017, according to Gartner, Inc. Speaking at the Gartner Customer Experience Summit in Tokyo today, Gene Alvarez, managing vice president at Gartner, said more than half of organizations have already invested in VCAs for customer service, as they realize the advantages of automated self-service, together with the ability to escalate to a human agent in complex situations. "As more customers engage on digital channels, VCAs are being implemented for handling customer requests on websites, mobile apps, consumer messaging apps and social networks," Mr. Alvarez said. "This is underpinned by improvements in natural-language processing, machine learning and intent-matching capabilities." Organizations report a reduction of up to 70 percent in call, chat and/or email inquiries after implementing a VCA, according to Gartner research. They also report increased customer satisfaction and a 33 percent saving per voice engagement.

Approximate vanishing ideal, which is a new concept from computer algebra, is a set of polynomials that almost takes a zero value for a set of given data points. The introduction of approximation to exact vanishing ideal has played a critical role in capturing the nonlinear structures of noisy data by computing the approximate vanishing polynomials. However, approximate vanishing has a theoretical problem, which is giving rise to the spurious vanishing problem that any polynomial turns into an approximate vanishing polynomial by coefficient scaling. In the present paper, we propose a general method that enables many basis construction methods to overcome this problem. Furthermore, a coefficient truncation method is proposed that balances the theoretical soundness and computational cost. The experiments show that the proposed method overcomes the spurious vanishing problem and significantly increases the accuracy of classification.

Chatbots, or virtual customer assistants (VCAs), will be used in 25 percent of customer service and support operations by 2020, up from just 2 percent in 2017. According to news from Gartner, a leading research and advisory company, more than half of organizations have already invested in VCAs for customer service so they can utilize all the advantages of automated self-service technology. "As more customers engage on digital channels, VCAs are being implemented for handling customer requests on websites, mobile apps, consumer messaging apps and social networks," said Gene Alvarez, managing vice president at Gartner, at the Gartner Customer Experience Summit in Tokyo. According to Gartner's research, companies reported a reduction of up to 70 percent in call, chat and/or email inquiries after implementing a VCA, as well as a boost in customer satisfaction and a 33 percent savings per voice engagement. "A great VCA offers more than just information," said Alvarez.

Recent initiatives by the Singapore Tourism Board (STB) are spurring technology take-up in the hospitality sector at an unprecedented rate, with hotels in the city increasingly adopting new solutions ranging from service delivery automation to integrated mobile applications and virtual concierges. With the revamping of Bayview Hotel into 30 Bencoolen, the hotel's 131 guest rooms are now fitted with smart room control units that allow the operations team to monitor room statuses and be alerted by any room faults in real time. Kevin Peeris, regional head, business development, Bayview International Hotels & Resorts, said: "(The use of technology) generates exciting opportunities to enhance productivity, increase effectiveness and create exceptional experiences for both staff and guests." Conrad Centennial Singapore uses eConnect, a similar system that "drastically reduces the time taken to solve issues by eliminating middle steps," said Heinrich Grafe, the hotel's general manager. Both hotels are also looking at introducing automation to traditional hospitality processes.