Abstract

Foundation models for time series forecasting are highly sensitive to configuration parameters such as context length and patch size, which substantially influence predictive performance. These parameters are typically chosen via static defaults or per-series hyperparameter search using AutoML, the latter requiring repeated evaluations of a large model. We reinterpret configuration selection as an amortized learning problem: instead of optimizing configurations independently for each new series, we learn a lightweight learning-to-rank model that predicts high-performing configurations. Using the Moirai 1.1-R-small foundation model on the Uber TLC and Electricity benchmarks, Tune-as-Inference achieves accuracy within 3-5% of 20-trial Bayesian optimization while reducing per-series configuration time by approximately 20 times. These results suggest that configuration adaptation for time series foundation models are better treated as inference rather than iterative search.

Abstract

The National Institutional Ranking Framework (NIRF) provides a comprehensive evaluation of colleges across India based on various parameters such as teaching, learning, resources, research, and outreach. It integrates these parameters by giving a fixed priority to each of them. In today's competitive academic landscape, choosing the right college is a crucial decision for students seeking higher education. This depends on several factors specific to each student such as their social background, location, economic status and rank in the qualifying examination, etc.This research introduces a novel approach to enhance the utility of NIRF data by creating user-specific rankings tailored to individual preferences. By incorporating user-defined criteria such as median salary, number of students placed, facilities for Physically Challenged Students (PCS), faculty strength, research funding, the proposed system generates personalized rankings that align with the unique needs and aspirations of each student.Our system utilizes a novel skyline-based approach and analyzes the NIRF data alongside user preferences to generate dynamic rankings that adapt to evolving student priorities. We show multiple use-cases to demonstrate the utility of our approach in real-world scenarios, and study the way colleges are ranked for different user preferences. Our study shows that the college rankings so obtained can be quite different from the standard NIRF ranking.

Abstract

Current algorithms for Maximal Frequent Subgraph (MFS) mining do not scale to large databases with more than 300k graphs. Frequency computation is commonly done using numerous subgraph isomorphism operations, which are computationally expensive. This paper explores a partition-based secondary memory algorithm, PMCS, that can make MFS mining for large databases viable. PMCS intelligently avoids redundant computations to perform frequency computation operations optimally. Its intermediate results are also small enough to fit in the main memory. Our results demonstrate that PMCS scales to databases of up to 1000k graphs with an average of 25-30 edges per graph.

Abstract

With the rapid growth of Wikipedia, regular
maintenance of its content requires correspond-
ing growth in the laborious work of Wikipedi-
ans. We present a system that filters out most of
the content and recommends reliable and wor-
thy information that is a strong candidate for
Wikipedia updation. Preliminary results show
that our model achieves close to 98% sensitivity.

Abstract

Computer tomography (CT) have been routinely used for the diagnosis of lung diseases and recently, during the pandemic, for detecting the infectivity and severity of COVID-19 disease. One of the major concerns in using machine learning (ML) approaches for automatic processing of CT scan images in clinical setting is that these methods are trained on limited and biased subsets of publicly available COVID-19 data. This has raised concerns regarding the generalizability of these models on external datasets, not seen by the model during training. To address some of these issues, in this work CT scan images from confirmed COVID-19 data obtained from one of the largest public repositories, COVIDx CT 2A were used for training and internal validation of machine learning models. For the external validation we generated Indian-COVID-19 CT dataset, an open-source repository containing 3D CT volumes and 12096 chest CT images from 288 COVID-19 patients from India. Comparative performance evaluation of four state-of-the-art machine learning models, viz., a lightweight convolutional neural network (CNN), and three other CNN based deep learning (DL) models such as VGG-16, ResNet-50 and Inception-v3 in classifying CT images into three classes, viz., normal, non-covid pneumonia, and COVID-19 is carried out on these two datasets. Our analysis showed that the performance of all the models is comparable on the hold-out COVIDx CT 2A test set with 90%-99% accuracies (96% for CNN), while on the external Indian-COVID-19 CT dataset a drop in the performance is observed for all the models (8%-19%). The traditional machine learning model, CNN performed the best on the external dataset (accuracy 88%) in comparison to the deep learning models, indicating that a lightweight CNN is better generalizable on unseen data. The data and code are made available at https://github.com/aleesuss/c19.

Abstract

Climate classification plays a significant role in the development of building codes and standards. It guides the design of buildings' envelope and systems by considering their location's climate conditions. Various methods, such as ASHRAE Standard 169, Köppen, Trewartha utilize climate parameters such as temperature, humidity, solar radiation, precipitation, etc., to classify climates. When establishing requirements in building codes and standards, it is crucial to validate the classification based on the building's thermal loads. This paper introduces a novel methodology for classifying cities based on the number of similar days between them. It calculates similarity using daily mean temperature, relative humidity, and solar radiation by applying threshold values. A matrix of similar days is analyzed through agglomerative hierarchical clustering with different thresholds. A scoring system based on building thermal load, where lower scores signify better classification, is employed to select the best method. The method was tested using U.S. weather data, yielding a lower score of 54.5 compared to ASHRAE Standard 169's score of 63.09. This suggests that the new approach results in less

Abstract

Computer tomography (CT) have been routinely used for the diagnosis of lung diseases and recently, during the pandemic, for detecting the infectivity and severity of COVID-19 disease. One of the major concerns in using ma-chine learning (ML) approaches for automatic processing of CT scan images in clinical setting is that these methods are trained on limited and biased sub-sets of publicly available COVID-19 data. This has raised concerns regarding the generalizability of these models on external datasets, not seen by the model during training. To address some of these issues, in this work CT scan images from confirmed COVID-19 data obtained from one of the largest public repositories, COVIDx CT 2A were used for training and internal vali-dation of machine learning models. For the external validation we generated Indian-COVID-19 CT dataset, an open-source repository containing 3D CT volumes and 12096 chest CT images from 288 COVID-19 patients from In-dia. Comparative performance evaluation of four state-of-the-art machine learning models, viz., a lightweight convolutional neural network (CNN), and three other CNN based deep learning (DL) models such as VGG-16, ResNet-50 and Inception-v3 in classifying CT images into three classes, viz., normal, non-covid pneumonia, and COVID-19 is carried out on these two datasets. Our analysis showed that the performance of all the models is comparable on the hold-out COVIDx CT 2A test set with 90% - 99% accuracies (96% for CNN), while on the external Indian-COVID-19 CT dataset a drop in the performance is observed for all the models (8% - 19%). The traditional ma-chine

Abstract

In this paper, we address the problem of auto- matic academic curriculum generation. A curricu- lum outlines definitive topics with their sub-topics and enables teachers and students to form an over- all idea of the course outcomes and goals, and a plan of what to teach and learn to achieve those goals. Automatic curriculum generation is rel- evant in modern times with the ever increasing, rapidly changing, digitally-available academic con- tent, that is too large for manual processing by human teams. Using Wikipedia as an external knowledge-base, along with a pipeline of standard components, we show that it is possible to generate human-interpretable 2-level topic hierarchies. We show that our approach works on publicly available textbooks, by first removing their title-structure, and then automatically regenerating a 2-level title structure that is on-par.

Abstract

We leverage pre-trained language models to solve the task of complex NER for two lowresource languages: Chinese and Spanish. We use the technique of Whole Word Masking (WWM) to boost the performance of masked language modeling objective on large and unsupervised corpora. We experiment with multiple neural network architectures, incorporating CRF, BiLSTMs, and Linear Classifiers on top of a fine-tuned BERT layer. All our models outperform the baseline by a significant margin and our best performing model obtains a competitive position on the evaluation leaderboard for the blind test set

Abstract

The paper aims to identify cited text spans in the reference paper related to the given citance in the citing paper. We refer to it as cited text span retrieval (CTSR). Most current methods attempt this task by relying on pre-trained offthe-shelf deep learning models like SciBERT. Though these models are pre-trained on large datasets, they underperform in out-of-domain settings. We introduce CitRet, a novel hybrid model for CTSR that leverages unique semantic and syntactic structural characteristics of scientific documents. This enables us to use significantly less data for finetuning. We use only 1040 documents for finetuning. Our model augments mildly-trained SBERT-based contextual embeddings with pre-trained non-contextual Word2Vec embeddings to calculate semantic textual similarity. We demonstrate the performance of our model on the CLSciSumm shared tasks. It improves the state-of-the-art results by over 15% on the F1 score evaluation