Sampling-Based Decomposition Algorithms for Arbitrary Tensor Networks

We show how to develop sampling-based alternating least squares (ALS) algorithms for decomposition of tensors into any tensor network (TN) format. Provided the TN format satisfies certain mild assumptions, resulting algorithms will have input sublinear per-iteration cost. Unlike most previous works on sampling-based ALS methods for tensor decomposition, the sampling in our framework is done according to the exact leverage score distribution of the design matrices in the ALS subproblems. We implement and test two tensor decomposition algorithms that use our sampling framework in a feature extraction experiment where we compare them against a number of other decomposition algorithms.Comment: 20 pages, 8 figure

Ayurvedic Management of Atrophie Blanche - A Case Study

Atrophie Blanche (AB) is typically described as a variable dimensioned, smooth, ivory-white plaque stippled with telangiectases and is surrounded by hyper pigmentation. AB commonly occurs in middleaged women on the lower legs or feet, often associated with ulcerations and chronic venous insufficiency (CVI). The ulcers are slow to heal and painful. We report the case of an Atrophie Blanche (Livedoid Vasculopathy) which inadequately treated for more than 8 years. We review the pathogenesis (Samprapti), typical clinical presentation (Purva Roopa and Roopa), diagnostic workup and treated through various Panchakarma procedure and Shamana Yogas

Taxonomic redescription of subfamily Scymninae (Coleoptera: Coccinellidae) from Haryana, India

The ladybird beetles of Scymninae, a subfamily of Coccinellidae are efficient biocontrol agents. Many earlier studies document their protective role associated with many crops. This subfamily is represented by 5 tribes,15 genera and 138 species in the Indian subcontinent. However there is no scientific record of taxonomic description of the subfamily Scymninae within the Coccinellidae family of beetles (Coccinellidae: Coleoptera)  in Haryana, India. In the present study, seven species from three genera and two tribes of sub-family Scymninae i.e. Nephus (Bipunctatus) bipunctatus (Kugelann,1794), Nephus regularis (Sicard), Scymnus (Pullus) coccivora Ramakrishna Ayyar, 1925, Scymnus (Pullus) latemaculatus Motschulsky, Scymnus (Scymnus) nubilus Mulsant, Scymnus (Pullus) posticalis Sicard and Stethorus aptus Kapur were identified. It included the generation of keys to the tribes, genera, subgenera and species found during the study. It provided detailed taxonomy of the identified species based on various morphological characteristics such as coxal lines, antennae, mandibles, and male and female genitalia. This study marks the first taxonomic exploration of species within the Scymninae subfamily in Haryana, India. It will significantly contribute to understanding the biodiversity of beetles in the region and will lay the groundwork for further research and conservation initiatives.

Efficient Leverage Score Sampling for Tensor Train Decomposition

Tensor Train~(TT) decomposition is widely used in the machine learning and quantum physics communities as a popular tool to efficiently compress high-dimensional tensor data. In this paper, we propose an efficient algorithm to accelerate computing the TT decomposition with the Alternating Least Squares (ALS) algorithm relying on exact leverage scores sampling. For this purpose, we propose a data structure that allows us to efficiently sample from the tensor with time complexity logarithmic in the tensor size. Our contribution specifically leverages the canonical form of the TT decomposition. By maintaining the canonical form through each iteration of ALS, we can efficiently compute (and sample from) the leverage scores, thus achieving significant speed-up in solving each sketched least-square problem. Experiments on synthetic and real data on dense and sparse tensors demonstrate that our method outperforms SVD-based and ALS-based algorithms

Avian diversity and conservation status in Bhindawas Bird Sanctuary, Jhajjar (Haryana), India

Bhindawas Bird Sanctuary is a Ramsar site located in Haryana, India, which falls in the Central Asian flyway zone of the migratory birds. Its diverse ecological resources sustain a rich diversity of migratory and threatened birds.The species diversity, threat status, population trend and feeding guild of the avifauna in Bhindawas Bird Sanctuary, Haryana, India, was explored from October 2021 to October 2023.The data was collected every fortnightly using the line transects method. A total of 129 bird species belonging to 98 genera, 47 families and 17 orders were recorded. Order Passeriformes, with 45 species in 20 families, dominated the avifauna, followed by Anseriformes with 16 species, Charadriformes with 12 species and the rest of 15 orders. Anatidae was the most dominant family representing 12.40% (n=16). Among the reported species, 81 were residents, 36 were winter migrants and, 10 were summer migrants and 2 were passage migrants. One species was endangered and vulnerable in the threat status, while six were classified as near threatened as per the International Union for Conservation of Nature (INUC) Red List, 2022. The bird sanctuary also supported 35 bird species with a declining population trend globally. The omnivorous and carnivorous feeding habits were equally dominant, followed by insectivorous and, nectarivorous and herbivorous birds, which were the least numerous. The presence of both resident and migrant birds of global conservation priority confirms the importance and conservation of Bhindawas Bird Sanctuary as a rich avifauna diversity habitat.

Quality or quantity? On data scale and diversity in adapting large language models for low-resource translation

Despite the recent popularity of Large Language Models (LLMs) in Machine Translation (MT), their performance in low-resource translation still lags significantly behind Neural Machine Translation (NMT) models. In this paper, we explore what it would take to adapt LLMs for low-resource settings. In particular, we re-examine the role of two factors: a) the importance and application of parallel data, and b) diversity in Supervised Fine-Tuning (SFT). Recently, parallel data has been shown to be less important for MT using LLMs than in previous MT research. Similarly, diversity during SFT has been shown to promote significant transfer in LLMs across languages and tasks. However, for low-resource LLM-MT, we show that the opposite is true for both of these considerations: a) parallel data is critical during both pretraining and SFT, and b) diversity tends to cause interference, not transfer. Our experiments, conducted with 3 LLMs across 2 low-resourced language groups - indigenous American and North-East Indian - reveal consistent patterns in both cases, underscoring the generalizability of our findings. We believe these insights will be valuable for scaling to massively multilingual LLM-MT models that can effectively serve lower-resource languages

Fast Exact Leverage Score Sampling from Khatri-Rao Products with Applications to Tensor Decomposition

We present a data structure to randomly sample rows from the Khatri-Rao product of several matrices according to the exact distribution of its leverage scores. Our proposed sampler draws each row in time logarithmic in the height of the Khatri-Rao product and quadratic in its column count, with persistent space overhead at most the size of the input matrices. As a result, it tractably draws samples even when the matrices forming the Khatri-Rao product have tens of millions of rows each. When used to sketch the linear least squares problems arising in CANDECOMP / PARAFAC tensor decomposition, our method achieves lower asymptotic complexity per solve than recent state-of-the-art methods. Experiments on billion-scale sparse tensors validate our claims, with our algorithm achieving higher accuracy than competing methods as the decomposition rank grows.Comment: To appear at the 37th Conference on Neural Information Processing Systems (Neurips'23). 28 pages, 10 figures, 6 table

Exploring very low-resource translation with LLMs:The University of Edinburgh’s submission to AmericasNLP 2024 translation task

This paper describes the University of Edinburgh’s submission to the AmericasNLP 2024 shared task on the translation of Spanish into 11 indigenous American languages. We explore the ability of multilingual Large Language Models (LLMs) to model low-resource languages by continued pre-training with LoRA, and conduct instruction fine-tuning using a variety of datasets, demonstrating that this improves LLM performance. Furthermore, we demonstrate the efficacy of checkpoint averaging alongside decoding techniques like beam search and sampling, resulting in further improvements. We participate in all 11 translation directions. Our models are released here: https://tinyurl.com/edi-amnlp24.<br/