arXiv subjects

Saikat Mondal

Publications and source records attributed to Saikat Mondal.

48 records · Page 3Linked to original sources

arXiv2022

Automatic Prediction of Rejected Edits in Stack Overflow

The content quality of shared knowledge in Stack Overflow (SO) is crucial in supporting software developers with their programming problems. Thus, SO allows its users to suggest edits to improve the quality of a post (i.e., question and answer). However, existing research shows that many suggested edits in SO are rejected due to undesired contents/formats or violating edit guidelines. Such a scenario frustrates or demotivates users who would like to conduct good-quality edits. Therefore, our research focuses on assisting SO users by offering them suggestions on how to improve their editing of posts. First, we manually investigate 764 (382 questions + 382 answers) rejected edits by rollbacks and produce a catalog of 19 rejection reasons. Second, we extract 15 texts and user-based features to capture those rejection reasons. Third, we develop four machine learning models using those features. Our best-performing model can predict rejected edits with 69.1% precision, 71.2% recall, 70.1% F1-score, and 69.8% overall accuracy. Fourth, we introduce an online tool named EditEx that works with the SO edit system. EditEx can assist users while editing posts by suggesting the potential causes of rejections. We recruit 20 participants to assess the effectiveness of EditEx. Half of the participants (i.e., treatment group) use EditEx and another half (i.e., control group) use the SO standard edit system to edit posts. According to our experiment, EditEx can support SO standard edit system to prevent 49% of rejected edits, including the commonly rejected ones. However, it can prevent 12% rejections even in free-form regular edits. The treatment group finds the potential rejection reasons identified by EditEx influential. Furthermore, the median workload suggesting edits using EditEx is half compared to the SO edit system.

cs.SE↗

arXiv2022

Periodically driven many-body quantum battery

We explore the charging of a quantum battery based on spin systems through periodic modulation of a transverse-field like Ising Hamiltonian. In the integrable limit, we find that resonance tunneling can lead to a higher transfer of energy to the battery and better stability of the stored energy at specific drive frequencies. When the integrability is broken in the presence of an additional longitudinal field, we find that the effective Floquet Hamiltonian contains terms which may lead to a global charging of the battery. However, we do not find any quantum advantage in the charging power, thus demonstrating that global charging is only a necessary and not sufficient condition for achieving quantum advantage.

quant-ph↗

arXiv2022

Robust Portfolio Design and Stock Price Prediction Using an Optimized LSTM Model

Accurate prediction of future prices of stocks is a difficult task to perform. Even more challenging is to design an optimized portfolio with weights allocated to the stocks in a way that optimizes its return and the risk. This paper presents a systematic approach towards building two types of portfolios, optimum risk, and eigen, for four critical economic sectors of India. The prices of the stocks are extracted from the web from Jan 1, 2016, to Dec 31, 2020. Sector-wise portfolios are built based on their ten most significant stocks. An LSTM model is also designed for predicting future stock prices. Six months after the construction of the portfolios, i.e., on Jul 1, 2021, the actual returns and the LSTM-predicted returns for the portfolios are computed. A comparison of the predicted and the actual returns indicate a high accuracy level of the LSTM model.

q-fin.PM↗

arXiv2022

Precise Stock Price Prediction for Optimized Portfolio Design Using an LSTM Model

Accurate prediction of future prices of stocks is a difficult task to perform. Even more challenging is to design an optimized portfolio of stocks with the identification of proper weights of allocation to achieve the optimized values of return and risk. We present optimized portfolios based on the seven sectors of the Indian economy. The past prices of the stocks are extracted from the web from January 1, 2016, to December 31, 2020. Optimum portfolios are designed on the selected seven sectors. An LSTM regression model is also designed for predicting future stock prices. Five months after the construction of the portfolios, i.e., on June 1, 2021, the actual and predicted returns and risks of each portfolio are computed. The predicted and the actual returns indicate the very high accuracy of the LSTM model.

q-fin.PM↗

arXiv2022

Hierarchical Risk Parity and Minimum Variance Portfolio Design on NIFTY 50 Stocks

Portfolio design and optimization have been always an area of research that has attracted a lot of attention from researchers from the finance domain. Designing an optimum portfolio is a complex task since it involves accurate forecasting of future stock returns and risks and making a suitable tradeoff between them. This paper proposes a systematic approach to designing portfolios using two algorithms, the critical line algorithm, and the hierarchical risk parity algorithm on eight sectors of the Indian stock market. While the portfolios are designed using the stock price data from Jan 1, 2016, to Dec 31, 2020, they are tested on the data from Jan 1, 2021, to Aug 26, 2021. The backtesting results of the portfolios indicate while the performance of the CLA algorithm is superior on the training data, the HRP algorithm has outperformed the CLA algorithm on the test data.

q-fin.PM↗

arXiv2022

Portfolio Optimization on NIFTY Thematic Sector Stocks Using an LSTM Model

Portfolio optimization has been a broad and intense area of interest for quantitative and statistical finance researchers and financial analysts. It is a challenging task to design a portfolio of stocks to arrive at the optimized values of the return and risk. This paper presents an algorithmic approach for designing optimum risk and eigen portfolios for five thematic sectors of the NSE of India. The prices of the stocks are extracted from the web from Jan 1, 2016, to Dec 31, 2020. Optimum risk and eigen portfolios for each sector are designed based on ten critical stocks from the sector. An LSTM model is designed for predicting future stock prices. Seven months after the portfolios were formed, on Aug 3, 2021, the actual returns of the portfolios are compared with the LSTM-predicted returns. The predicted and the actual returns indicate a very high-level accuracy of the LSTM model.

q-fin.PM↗

arXiv2022

Detecting topological phase transitions through entanglement between disconnected partitions in a Kitaev chain with long-range interactions

We explore the behaviour of the disconnected entanglement entropy (DEE) across the topological phases of a long range interacting Kitaev chain where the long range interactions decay as a power law with an exponent $α$. We show that while the DEE may not remain invariant deep within the topologically non-trivial phase when $α<1$, it nevertheless shows a quantized discontinuous jump at the quantum critical point and can act as a strong marker for the detection of topological phase transition. We also study the time evolution of the DEE after a sudden quench of the chemical potential within the same phase. In the short range limit of a finite chain, the DEE is expected to remain constant upto a critical time after the quench, which diverges in the thermodynamic limit. However, no such critical time is found to exist when the long range interactions dominate (i.e., $α<1$).

cond-mat.stat-mech↗

arXiv2021

The Reproducibility of Programming-Related Issues in Stack Overflow Questions

Software developers often look for solutions to their code-level problems using the Stack Overflow Q&A website. To receive help, developers frequently submit questions containing sample code segments and the description of the programming issue. Unfortunately, it is not always possible to reproduce the issues from the code segments that may impede questions from receiving prompt and appropriate solutions. We conducted an exploratory study on the reproducibility of issues discussed in 400 Java and 400 Python questions. We parsed, compiled, executed, and carefully examined the code segments from these questions to reproduce the reported programming issues. The outcomes of our study are three-fold. First, we found that we can reproduce approximately 68% of Java and 71% of Python issues, whereas we were unable to reproduce approximately 22% of Java and 19% of Python issues using the code segments. Of the issues that were reproducible, approximately 67% of the Java code segments and 20% of the Python code segments required minor or major modifications to reproduce the issues. Second, we carefully investigated why programming issues could not be reproduced and provided evidence-based guidelines for writing effective code examples for Stack Overflow questions. Third, we investigated the correlation between the issue reproducibility status of questions and the corresponding answer meta-data, such as the presence of an accepted answer. According to our analysis, a reproducible question has at least two times higher chance of receiving an accepted answer than an irreproducible question. Besides, the median time delay in receiving accepted answers is double if the issues reported in questions could not be reproduced. We also investigate the confounding factors (e.g., reputation) and find that confounding factors do not hurt the correlation between reproducibility status and answer meta-data.

cs.SE↗

arXiv2021

Reproducibility Challenges and Their Impacts on Technical Q&A Websites: The Practitioners' Perspectives

Software developers often look for solutions to their code-level problems by submitting questions to technical Q&A websites like Stack Overflow (SO). They usually include example code segments with questions to describe the programming issues. SO users prefer to reproduce the reported issues using the given code segments when they attempt to answer the questions. Unfortunately, such code segments could not always reproduce the issues due to several unmet challenges (e.g., external library not found) that might prevent questions from receiving prompt and appropriate solutions. A previous study produced a catalog of potential challenges that hinder the reproducibility of issues reported at SO questions. However, it is unknown how the practitioners (i.e., developers) perceive the challenge catalog. Understanding the developers' perspective is inevitable to introduce interactive tool support that promotes reproducibility. We thus attempt to understand developers' perspectives by surveying 53 users of SO. In particular, we attempt to -- (1) see developers' viewpoints on the agreement to those challenges, (2) find the potential impact of those challenges, (3) see how developers address them, and (4) determine and prioritize tool support needs. Survey results show that about 90% of participants agree to the already exposed challenges. However, they report some additional challenges (e.g., error log missing) that might prevent reproducibility. According to the participants, too short code segment and absence of required Class/Interface/Method from code segments severely prevent reproducibility, followed by missing important part of code. To promote reproducibility, participants strongly recommend introducing tool support that interacts with question submitters with suggestions for improving the code segments if the given code segments fail to reproduce the issues.

cs.SE↗

arXiv2021

Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model

Predictive model design for accurately predicting future stock prices has always been considered an interesting and challenging research problem. The task becomes complex due to the volatile and stochastic nature of the stock prices in the real world which is affected by numerous controllable and uncontrollable variables. This paper presents an optimized predictive model built on long-and-short-term memory (LSTM) architecture for automatically extracting past stock prices from the web over a specified time interval and predicting their future prices for a specified forecast horizon, and forecasts the future stock prices. The model is deployed for making buy and sell transactions based on its predicted results for 70 important stocks from seven different sectors listed in the National Stock Exchange (NSE) of India. The profitability of each sector is derived based on the total profit yielded by the stocks in that sector over a period from Jan 1, 2010 to Aug 26, 2021. The sectors are compared based on their profitability values. The prediction accuracy of the model is also evaluated for each sector. The results indicate that the model is highly accurate in predicting future stock prices.

cs.LG↗

arXiv2020

Exploring the role of asymmetric-pulse modulation in quantum thermal machines and quantum thermometry

We explore the consequences of periodically modulating a quantum two-level system (TLS) with an asymmetric pulse when the system is in contact with thermal baths. By adopting the Floquet-Lindblad formalism for our analysis, we find that the unequal "up" and "down" time duration of the pulse has two main ramifications. First, the energy gap of the multiple sidebands or photon sectors created as a result of the periodic modulation are renormalized by a term which is dependent on both the modulation strength as well as the fraction of up (or down) time duration. Second, the weights of the different sidebands are no longer symmetrically distributed about the central band or zero photon sector. We illustrate the advantages of these findings in the context of applications in quantum thermal machines and thermometry. For a thermal machine constructed by coupling the TLS to two thermal baths, we demonstrate that the asymmetric pulse provides an extra degree of control over the mode of operation of the thermal machine. Further, by appropriately tuning the weight of the subbands, we also show that an asymmetric pulse may provide superior optimality in a recently proposed protocol for quantum thermometry, where dynamical control has been shown to enhance the precision of measurement.

quant-ph↗

arXiv2020

MOTS in Schwarzschild: multiple self-intersections and extreme mass ratio mergers

We study the open and closed axisymmetric marginally outer trapped surfaces contained in leaves of constant Painlevé-Gullstrand time for Schwarzschild spacetimes. We identify a family of closed MOTS in the black hole interior characterized by an arbitrary number of self-intersections. This suggests that the self-intersecting behaviour reported in [Phys.Rev.D 100, 084044 (2019)] may be a far more generic phenomenon than expected. We also consider open surfaces, finding that their behaviour is highly constrained but includes surfaces with multiple self-intersections inside the horizon. We argue that the behaviour of open MOTS identifies and constrains the possible local behaviour of MOTS during extreme mass ratio mergers.

gr-qc↗