Improved Beam Search for Hallucination Mitigation in Abstractive Summarization

Advancement in large pretrained language models has significantly improved their performance for conditional language generation tasks including summarization albeit with hallucinations. To reduce hallucinations, conventional methods proposed improving beam search or using a fact checker as a postprocessing step. In this paper, we investigate the use of the Natural Language Inference (NLI) entailment metric to detect and prevent hallucinations in summary generation. We propose an NLI-assisted beam re-ranking mechanism by computing entailment probability scores between the input context and summarization model-generated beams during saliency-enhanced greedy decoding. Moreover, a diversity metric is introduced to compare its effectiveness against vanilla beam search. Our proposed algorithm significantly outperforms vanilla beam decoding on XSum and CNN/DM datasets.Comment: 8 pages, 2 figure

Pattern of skin diseases and common drugs prescribed in dermatology OPD of an Indian tertiary care hospital

Background: The pattern of skin diseases varies from one country to another and across different parts within the same country. The prevalence of skin disease in the general population varies from 11.16 % to 63 %. Patients in their second and third decades of age form the largest group of population. A proper treatment is essential for cure and control of these diseases. The aim of the study was to find the pattern of skin diseases in Meerut district of western UP and common group of drugs prescribed for them.Methods: It was a prospective, observational, single center study. The relevant data was collected from OPD prescriptions by taking photographs of the prescriptions and details were filled in the predesigned proforma.Results: A total of 500 OPD prescriptions were collected and analyzed for demographic profile, disease incidence and drug prescription. Three most common conditions observed were tinea (15.25%), acne (12.36%), drug induced cutaneous reactions (10.11%). Antifungals (19.4%), Antibiotics (17.6%), Antihistamines (15.9%) and Corticosteroids (9.4%) were the most common class of drugs prescribed. Dosage forms prescribed were mostly topical (51.2%) in the form of ointments, creams, lotions, shampoo, powders.Conclusions: From our study we found out that tinea (15.25%) and acne (12.36%) were more common in this region and antifungals were most the commonly prescribed group of drugs (19.4%). Number of drugs/prescription was much higher (4.1) than the recommended limit of 2 approved by WHO and practice of polypharmacy was also commonly seen

Calibration and Optimization of Computer Models with Applications to Acoustics and Materials Discovery

In the past, physical systems/processes/phenomena were studied using expensive and time-consuming physical experiments. However, with the advancement of computational resources, computer models are now used extensively to minimize the need for such experimentation. A computer model provides a cheap alternative to explore the behavior of physical processes in desired scenarios and make inferences. This thesis begins with the topic of model calibration, a method to estimate unknown model parameters, and adjust the model to mimic reality. This is followed by a couple of applications of computer models in the fi eld of material informatics and acoustic metasurface design. Novel machine learning algorithms are developed that leverage the computer models for efficient exploration of the physical processes, optimization of parameters of interest, and making inferences. In Chapter 1, we propose a novel methodology to obtain a robust experimental design for model calibration. A computer model can be used for predicting an output only after specifying the values of some unknown physical constants known as calibration parameters. The unknown calibration parameters can be estimated from real data by conducting physical experiments. This chapter presents an approach to optimally design such a physical experiment. The problem of optimally designing a physical experiment, using a computer model, is similar to the problem of finding an optimal design for fi tting nonlinear models. However, the problem is more challenging than the existing work on nonlinear optimal design because of the possibility of model discrepancy, that is, the computer model may not be an accurate representation of the true underlying model. Therefore, we propose an optimal design approach that is robust to potential model discrepancies. We show that our designs are better than the commonly used physical experimental designs that do not make use of the information contained in the computer model and other nonlinear optimal designs that ignore potential model discrepancies. We illustrate our approach using a toy example and a real example from the Procter & Gamble company. In Chapter 2, we present a novel machine learning algorithm for discovering new materials crystal structure. A material is (thermodynamically) stable and exists naturally when its building blocks, i.e., the constituent atoms, are arranged so that the potential energy is (globally) minimized. We aim to find such minimum energy con figurations, to discover a new crystal structure. We leverage density functional theory (DFT) to compute the potential energy for a given confi guration of the atoms. The problem is challenging because there are infi nitely large number of con figurations, the DFT code for computing the energy is expensive, and the potential energy surface is highly non-linear and multi-modal. We propose a novel expansion-exploration-exploitation framework to nd the global minimum. The space spanned by a few known crystal structure confi gurations is expanded to obtain a candidate set of con figurations. A key feature of this step is that it tends to generate a space-filling design without the knowledge of the boundaries of the domain space. Once a candidate set of con figurations is obtained, it is explored and exploited simultaneously, using Bayesian optimization, to nd the global minimum of potential energy. Gaussian Process modeling along with the Expected Improvement algorithm is used to iteratively update the model and guide the search towards the global minimum. We show the effectiveness of our methodology on toy examples and a real problem of predicting the crystal structure con figuration of Al8. In Chapter 3, we address the problem of designing acoustic metasurfaces for independent amplitude and phase control of acoustic waves. Acoustic metasurfaces are material structures of subwavelength thickness that are used for modulating propagating sound waves. Several applications of acoustic metasurfaces, such as non-invasive biomedical treatments, require independent phase and amplitude modulation of the reflected and transmitted waves. These reflection and transmission outputs (or acoustic outputs) are governed by the geometry of the acoustic metasurface. We model the geometry of the metasurface as a unit cell with mn equal-sized square shaped elements, or a grid-size of mxn. Each element can either be empty or fi lled with solid material leading to a total of 2^mn unique geometries! This makes it challenging to identify the relevant geometries for obtaining the desired range of acoustic outputs, which are simulated using the COMSOL Multiphysics software. We leverage the expansion algorithm developed in Chapter 2 to start with a few geometries and iteratively add geometries to the set such that they span the entire range of acoustic outputs using only a small fraction of the total number of possible geometries.Ph.D

Activity report analysis with automatic single or multispan answer extraction

In the era of loT (Internet of Things) we are surrounded by a plethora of Al enabled devices that can transcribe images, video, audio, and sensors signals into text descriptions. When such transcriptions are captured in activity reports for monitoring, life logging and anomaly detection applications, a user would typically request a summary or ask targeted questions about certain sections of the report they are interested in. Depending on the context and the type of question asked, a question answering (QA) system would need to automatically determine whether the answer covers single-span or multi-span text components. Currently available QA datasets primarily focus on single span responses only (such as SQuAD[4]) or contain a low proportion of examples with multiple span answers (such as DROP[3]). To investigate automatic selection of single/multi-span answers in the use case described, we created a new smart home environment dataset comprised of questions paired with single-span or multi-span answers depending on the question and context queried. In addition, we propose a RoBERTa[6]-based multiple span extraction question answering (MSEQA) model returning the appropriate answer span for a given question. Our experiments show that the proposed model outperforms state-of-the-art QA models on our dataset while providing comparable performance on published individual single/multi-span task datasets

Covid-19 Disaster relief projects management: an exploratory study of critical success factors

The COVID-19 pandemic has caused unprecedented socio-economic devastation. With widespread displacement of population/ migrants, considerable destruction of property, increase in mortality, morbidity, and poverty, infectious disease outbreaks and epidemics have become global threats requiring a collective response. Project Management is, however, a relatively less explored discipline in the Third Sector, particularly in the domain of humanitarian assistance or exploratory projects. Via a systematic literature review and experts' interviews, this paper explores the essence of humanitarian projects in terms of the challenges encountered and the factors that facilitate or hinder project success during crises like Covid-19. Additionally, the general application of project management in international assistance projects is analysed to determine how project management can contribute to keeping the project orientation humane during a crisis. The analysis reveals that applying project management tools and techniques are beneficial to achieve success in humanitarian assistance projects. However, capturing, codifying, and disseminating the knowledge generated in the process and placing the end-users at the centre of the project life cycle is a prerequisite. While the latter can seem obvious, the findings demonstrate that the inadequate inclusion of beneficiaries is one of the main reasons that prevent positive project outcomes leading to unsustainable outcomes. The key finding of this paper is that the lack of human-centred approaches in project management for humanitarian assistance and development projects is the main reason such projects fail to achieve desired outcomes.submittedVersionpublishedVersio

Modelling and Simulation of Current Source Converter based Dynamic Voltage Restorer for Voltage Regulation cum Harmonics Mitigation

The modern electric power distribution system faces lots of power quality (PQ) problems such as short and long duration voltage variations, voltage imbalance, waveform distortions, impulsive and oscillatory transients and voltage flicker etc. The custom power devices (CPD) have been designed and developed with the aim of mitigating these problems. The major CPDs introduced so far include distribution-static synchronous compensator (D-STATCOM), dynamic voltage restorer (DVR), unified power quality conditioners (UPQC), active power filters (APF) etc.  However, most of these CPDs are designed and implemented using voltage source converter (VSC) topology. Although lots of research works have been carried out for realizing DVR system based on VSC topology, not much research work has been reported on the application of CSC topology in DVR system over the last one decade. Through this paper, it has been attempted to develop a model of DVR system based on CSC topology capable of performing dual power quality enhancement tasks viz. voltage regulation and harmonics mitigation at a power distribution system catering power to a variety of loads. The DVR system has been modeled and simulated in the MATLAB / Simulink platform and the simulation results reveal the effectiveness and validity of the proposed model for use in power distribution system

Exact and Heuristic Methods for the Weapon Target Assignment Problem

The Weapon Target Assignment (WTA) problem is a fundamental problem arising in defense-related applications of operations research. This problem consists of optimally assigning n weapons to m targets so that the total expected survival value of the targets after all the engagements is minimum. The WTA problem can be formulated as a nonlinear integer programming problem and is known to be NP-complete. There do not exist any exact methods for the WTA problem which can solve even small size problems (for example, with 20 weapons and 20 targets). Though several heuristic methods have been proposed to solve the WTA problem, due to the absence of exact methods, no estimates are available on the quality of solutions produced by such heuristics. In this paper, we suggest linear programming, integer programming, and network flow based lower bounding methods using which we obtain several branch and bound algorithms for the WTA problem. We also propose a network flow based construction heuristic and a very large-scale neighborhood (VLSN) search algorithm. We present computational results of our algorithms which indicate that we can solve moderately large size instances (up to 80 weapons and 80 targets) of the WTA problem optimally and obtain almost optimal solutions of fairly large instances (up to 200 weapons and 200 targets) within a few second