Miracle-dz/UniFAR-FTUs huggingface dataset:
The Elasticity of Quantitative Investment

What is the price elasticity of demand for canonical portfolio choice methods in financial economics? Twelve models from the literature exhibit strikingly inelastic demand, in contrast to classical models. This is due to the difficulty of trading against price changes in practice, and is consistent with demand elasticity estimates. This provides a novel answer to the inelastic markets hypothesis, raises important concerns for the use of strongly elastic investors in theory models, and quantifies the difficulty of trading against potential mispricing aside from the standard limits to arbitrage frictions. Counterfactual experiments with these demand functions exhibit large and persistent alpha.
Depth and Stanley depth of powers of the path ideal of a cycle graph

Let $J_{n,m}:=(x_1x_2\cdots x_m,\; x_2x_3\cdots x_{m+1},\; \ldots,\; x_{n-m+1}\cdots x_n,\; x_{n-m+2}\cdots x_nx_1, \ldots, x_nx_1\cdots x_{m-1})$ be the $m$-path ideal of the cycle graph of length $n$, in the ring $S=K[x_1,\ldots,x_n]$. Let $d=\gcd(n,m)$. We prove that $\operatorname{depth}(S/J_{n,m}^t)\leq d-1$ for all $t\geq n-1$. We show that $\operatorname{sdepth}(S/J_{n,n-1}^t)=\operatorname{depth}(S/J_{n,n-1}^t)=\max\{n-t-1,0\}$ for all $t\geq 1$. Also, we give some bounds for $\operatorname{depth}(S/J_{n,m}^t)$ and $\operatorname{sdepth}(S/J_{n,m}^t)$, where $t\geq 1$.
Investigating Causal links from Observed Features in the first COVID-19 Waves in California (preprint)

Determining who is at risk from a disease is important in order to protect vulnerable subpopulations during an outbreak. We are currently in a SARS-COV-2 (commonly referred to as COVID-19) pandemic which has had a massive impact across the world, with some communities and individuals seen to have a higher risk of severe outcomes and death from the disease compared to others. These risks are compounded for people of lower socioeconomic status, those who have limited access to health care, higher rates of chronic diseases, such as hypertension, diabetes (type-2), obesity, likely due to the chronic stress of these types of living conditions. Essential workers are also at a higher risk of COVID-19 due to having higher rates of exposure due to the nature of their work. In this study we determine the important features of the pandemic in California in terms of cumulative cases and deaths per 100,000 of population up to the date of 5 July, 2021 (the date of analysis) using Pearson correlation coefficients between population demographic features and cumulative cases and deaths. The most highly correlated features, based on the absolute value of their Pearson Correlation Coefficients in relation to cases or deaths per 100,000, were used to create regression models in two ways: using the top 5 features and using the top 20 features filtered out to limit interactions between features. These models were used to determine a) the most significant features out of these subsets and b) features that approximate different potential forces on COVID-19 cases and deaths (especially in the case of the latter set). Additionally, co-correlations, defined as demographic features not within a given input feature set for the regression models but which are strongly correlated with the features included within, were calculated for all features.
Ensemble Latent Space Roadmap for Improved Robustness in Visual Action Planning

Planning in learned latent spaces helps to decrease the dimensionality of raw observations. In this work, we propose to leverage the ensemble paradigm to enhance the robustness of latent planning systems. We rely on our Latent Space Roadmap (LSR) framework, which builds a graph in a learned structured latent space to perform planning. Given multiple LSR framework instances, that differ either on their latent spaces or on the parameters for constructing the graph, we use the action information as well as the embedded nodes of the produced plans to define similarity measures. These are then utilized to select the most promising plans. We validate the performance of our Ensemble LSR (ENS-LSR) on simulated box stacking and grape harvesting tasks as well as on a real-world robotic T-shirt folding experiment.
Predicting the Future of the CMS Detector: Crystal Radiation Damage and Machine Learning at the LHC

The 75,848 lead tungstate crystals in CMS experiment at the CERN Large Hadron Collider are used to measure the energy of electrons and photons produced in the proton-proton collisions. The optical transparency of the crystals degrades slowly with radiation dose due to the beam-beam collisions. The transparency of each crystal is monitored with a laser monitoring system that tracks changes in the optical properties of the crystals due to radiation from the collision products. Predicting the optical transparency of the crystals, both in the short-term and in the long-term, is a critical task for the CMS experiment. We describe here the public data release, following FAIR principles, of the crystal monitoring data collected by the CMS Collaboration between 2016 and 2018. Besides describing the dataset and its access, the problems that can be addressed with it are described, as well as an example solution based on a Long Short-Term Memory neural network developed to predict future behavior of the crystals.
BlobGAN-3D: A Spatially-Disentangled 3D-Aware Generative Model for Indoor Scenes

3D-aware image synthesis has attracted increasing interest as it models the 3D nature of our real world. However, performing realistic object-level editing of the generated images in the multi-object scenario still remains a challenge. Recently, a 2D GAN termed BlobGAN has demonstrated great multi-object editing capabilities on real-world indoor scene datasets. In this work, we propose BlobGAN-3D, which is a 3D-aware improvement of the original 2D BlobGAN. We enable explicit camera pose control while maintaining the disentanglement for individual objects in the scene by extending the 2D blobs into 3D blobs. We keep the object-level editing capabilities of BlobGAN and in addition allow flexible control over the 3D location of the objects in the scene. We test our method on real-world indoor datasets and show that our method can achieve comparable image quality compared to the 2D BlobGAN and other 3D-aware GAN baselines while being able to enable camera pose control and object-level editing in the challenging multi-object real-world scenarios.
Critical Zones for Comfortable Collision Avoidance with a Leading Vehicle

This paper provides a general framework for efficiently obtaining the appropriate intervention time for collision avoidance systems to just avoid a rear-end crash. The proposed framework incorporates a driver comfort model and a vehicle model. We show that there is a relationship between driver steering manoeuvres based on acceleration and jerk, and steering angle and steering angle rate profiles. We investigate how four different vehicle models influence the time when steering needs to be initiated to avoid a rear-end collision. The models assessed were: a dynamic bicycle model (DM), a steady-state cornering model (SSCM), a kinematic model (KM) and a point mass model (PMM). We show that all models can be described by a parameter-varying linear system. We provide three algorithms for steering that use a linear system to compute the intervention time efficiently for all four vehicle models. Two of the algorithms use backward reachability simulation and one uses forward simulation. Results show that the SSCM, KM and PMM do not accurately estimate the intervention time for a certain set of vehicle conditions. Due to its fast computation time, DM with a backward reachability algorithm can be used for rapid offline safety benefit assessment, while DM with a forward simulation algorithm is better suited for online real-time usage.
On Hempel pairs and Turaev--Viro invariants

Surface bundles arising from periodic mapping classes may sometimes have non-isomorphic, but profinitely isomorphic fundamental groups. Pairs of this kind have been discovered by Hempel. This paper exhibits examples of nontrivial Hempel pairs where the mapping tori can be distinguished by some Turaev--Viro invariants, and also examples where they cannot be distinguished by any Turaev--Viro invariants.
Overleaf Example

The current study investigated possible human-robot kinaesthetic interaction using a variational recurrent neural network model, called PV-RNN, which is based on the free energy principle. Our prior robotic studies using PV-RNN showed that the nature of interactions between top-down expectation and bottom-up inference is strongly affected by a parameter, called the meta-prior, which regulates the complexity term in free energy. The current study examines how changing the meta-prior w in the interaction phase affects the counter force generated when an experimenter attempts to induce movement pattern transitions familiar to the robot through its prior training. The study also compares the counter force generated when trained transitions are induced by a human experimenter and when untrained transitions are induced. Our experimental results indicated that (1) the human experimenter needs more/less force to induce trained transitions when w is set with larger/smaller values, (2) the human experimenter needs more force to act on the robot when he attempts to induce untrained as opposed to trained movement pattern transitions. Our analysis of time development of essential variables and values in PV-RNN during bodily interaction clarified the mechanism by which gaps in actional intentions between the human experimenter and the robot can be manifested as reaction forces between them.
Revisiting Anwesha:Enhancing Personalised and Natural Search in Bangla

Bangla is a low-resource, highly agglutinative language. Thus it is challenging to facilitate an effective search of Bangla documents. We have created a gold standard dataset containing query document relevance pairs for evaluation purposes. We utilise Named Entities to improve the retrieval effectiveness of traditional Bangla search algorithms. We suggest a reasonable starting model for leveraging implicit preference feedback based on the user search behaviour to enhance the results retrieved by the Explicit Semantic Analysis (ESA) approach. We use contextual sentence embeddings obtained via Language-agnostic BERT Sentence Embedding (LaBSE) to rerank the candidate documents retrieved by the traditional search algorithms (tf-idf) based on the top sentences that are most relevant to the query. This paper presents our empirical findings across these directions and critically analyses the results.
SConE:Contextual Relevance based Significant CompoNent Extraction from Contracts

Automatic extraction of “significant” components of a legal contract, has the potential to simplify the end user’s comprehension. In essence, “significant” pieces of information have 1) information pertaining to material/practical details about a specific contract and 2) information that is novel or comes as a “surprise” for a specific type of contract. It indicates that the significance of a component may be defined at an individual contract level and at a contract-type level. A component, sentence, or paragraph, may be considered significant at a contract level if it contains contract-specific information (CSI), like names, dates, or currency terms. At a contract-type level, components that deviate significantly from the norm for the type may be considered significant (type-specific information (TSI)). In this paper, we present approaches to extract “significant” components from a contract at both these levels. We attempt to do this by identifying patterns in a pool of documents of the same kind. Consequently, in our approach, the solution is formulated in two parts: identifying CSI using a BERT-based contract-specific information extractor and identifying TSI by scoring sentences in a contract for their likelihood. In this paper, we even describe the annotated corpus of contract documents that we created as a first step toward the development of such a language-processing system. We also release a dataset of contract samples containing sentences belonging to CSI and TSI.
Named Entity Recognition for Code-Mixed Kannada-English Social Media Data

Named Entity Recognition (NER) is a critical task in the field of Natural Language Processing (NLP) and is also a sub-task of Information Extraction. There has been a significant amount of work done in entity extraction and Named Entity Recognition for resource-rich languages. Entity extraction from code-mixed social media data like tweets from twitter complicates the problem due to its unstructured, informal, and incomplete information available in tweets. Here, we present work on NER in Kannada-English code-mixed social media corpus with corresponding named entity tags referring to Organisation (Org), Person (Pers), and Location (Loc). We experimented with machine learning classification models like Conditional Random Fields (CRF), Bi-LSTM, and Bi-LSTM-CRF models on our corpus.
IAEmp: Intent-aware Empathetic Response Generation

In the domain of virtual assistants or conversational systems, it is important to empathise with the user. Being empathetic involves understanding the emotion of the ongoing dialogue and responding to the situation with empathy. We propose a novel approach for empathetic response generation, which leverages predicted intents for future response and prompts the encoder-decoder model to improve empathy in generated responses. Our model exploits the combination of dialogues and their respective emotions to generate empathetic response. As responding intent plays an important part in our generation, we also employ one or more intents to generate responses with relevant empathy. We achieve improved human and automated metrics, compared to the baselines.
BERT-based Language Identification in Code-Mix Kannada-English Text at the CoLI-Kanglish Shared Task@ICON 2022

Language identification has recently gained research interest in code-mixed languages due to the extensive use of social media among people. People who speak multiple languages tend to use code-mixed languages when communicating with each other. It has become necessary to identify the languages in such code-mixed environment to detect hate speeches, fake news, misinformation or disinformation and for tasks such as sentiment analysis. In this work, we have proposed a BERT-based approach for language identification in the CoLI-Kanglish shared task at ICON 2022. Our approach achieved 86% weighted average F-1 score and a macro average F-1 score of 57% in the test set.
Word Level Language Identification in Code-mixed Kannada-English Texts using traditional machine learning algorithms

Language Identification at the Word Level in Kannada-English Texts. This paper de- scribes the system paper of CoLI-Kanglish 2022 shared task. The goal of this task is to identify the different languages used in CoLI- Kanglish 2022. This dataset is distributed into different categories including Kannada, En- glish, Mixed-Language, Location, Name, and Others. This Code-Mix was compiled by CoLI- Kanglish 2022 organizers from posts on social media. We use two classification techniques, KNN and SVM, and achieve an F1-score of 0.58 and place third out of nine competitors.
Spellchecker for Sanskrit:The Road Less Taken

A spellchecker is essential for any language for producing error-free content. While there exist advanced computational tools for Sanskrit, such as word segmenter, morphological analyser, sentential parser, and machine translation, a fully functional spellchecker is not available. This paper presents a Sanskrit spellchecking dictionary for Hunspell, thereby creating a spellchecker that works across the numerous platforms Hunspell supports. The spellchecking rules are created based on the Paninian grammar, and the dictionary design follows the word-and-paradigm model, thus, making it easily extendible for future improvements. The paper also presents an online spellchecking interface for Sanskrit developed mainly for the platforms where Hunspell integration is not available yet.
Military occupation and testicular germ cell tumour risk among US Air Force servicemen

Objectives Testicular germ cell tumours (TGCTs) are the most commonly diagnosed malignancy among active duty US military servicemen. Occupational risk factors may play a role in TGCT aetiology, although the evidence is inconclusive. The objective of our study was to investigate associations between military occupations and TGCT risk among US Air Force (USAF) servicemen. Methods This nested case–control study among active duty USAF servicemen obtained information on military occupations for 530 histologically confirmed TGCT cases diagnosed during 1990–2018 and 530 individually matched controls. We determined military occupations using Air Force Specialty Codes ascertained at two time points: at case diagnosis and at a time point on average 6 years earlier. We computed adjusted ORs and 95% CIs from conditional logistic regression models to evaluate associations between occupations and TGCT risk. Results The mean age at TGCT diagnosis was 30 years. Increased TGCT risk was observed for pilots (OR=2.84, 95% CI: 1.20–6.74) and servicemen with aircraft maintenance jobs (OR=1.85, 95% CI: 1.03–3.31) who held those jobs at both time points. Fighter pilots (n=18) and servicemen with firefighting jobs (n=18) at the time of case diagnosis had suggestively elevated TGCT odds (OR=2.73, 95% CI: 0.96–7.72 and OR=1.94, 95% CI: 0.72–5.20, respectively). Conclusions In this matched, nested case–control study of young active duty USAF servicemen, we found that pilots and men with aircraft maintenance jobs had elevated TGCT risk. Further research is needed to elucidate specific occupational exposures underlying these associations.
Association between periodontitis and hypertension: cross-sectional survey from the Fourth National Oral Health Survey of China (2015–2016)

Objectives To evaluate the association between periodontitis severity and hypertension based on Chinese epidemiological data. Design This cross-sectional survey enrolled adults from the Fourth National Oral Health Survey of China (2015–2016). Setting The data were obtained from the Fourth National Oral Health Survey of China (2015–2016). Participants The study included individuals aged 35–44 years (n=4409), 55–64 years (n=4568) and 65–74 years (n=4218). Primary and secondary outcome measures Periodontal status, defined on the basis of the 2017 classification scheme, and periodontal parameters (eg, bleeding on probing (BOP)) were compared between individuals with hypertension and individuals with normotension. Smoothed scatterplots were constructed to demonstrate the associations of periodontal parameters and periodontal status with hypertension. Results Severe periodontitis (stages III and IV) was present in 41.4% of individuals with hypertension and 28.0% of individuals with normotension, respectively (p<0.001). The prevalence of severe periodontitis was higher in individuals with hypertension than that in individuals with normotension among participants aged 35–44 years (18.0% vs 10.1%, p<0.001) and 55–64 years (40.2% vs 36.7%, p=0.035), but not in participants aged 65–74 years (46.4% vs 45.1%, p=0.429). Therefore, the difference in periodontal status between individuals with hypertension and individuals with normotension decreased with age. There were higher prevalences of BOP, probing depth (PD) ≥4 mm and PD ≥6 mm in individuals with hypertension than in individuals with normotension (52.1% vs 49.2%, 19.6% vs 14.7% and 1.8% vs 1.1%, respectively). Periodontitis severity and the proportion of teeth with PD ≥4 or ≥6 mm were positively associated with hypertension. Conclusion Periodontitis is associated with hypertension in Chinese adults. Hypertension prevalence increased with periodontitis severity, particularly among young participants. Accordingly, it is necessary to improve the education, awareness of periodontal treatment and preventive management among individuals at risk of hypertension, particularly in the younger population.
Taming Entangled Accessibility Forum Threads for Efficient Screen Reading

Accessibility forums enable individuals with visual impairments to connect and collaboratively seek solutions to technical issues, as well as share reviews, best practices, and latest news. However, these forums are presently built on legacy systems that were primarily designed for sighted users, and are difficult to navigate with non-visual assistive technologies like screen-readers. Accessibility forum threads are “entangled”, with multiple sub-conversations interleaved with each other. This does not gel with the predominantly linear navigation of screen-readers. Screen-reader users often listen to reams of irrelevant posts while foraging for nuggets of interest. To address this and improve non-visual interaction efficiency, we present TASER, a browser extension that leverages a state-of-the-art conversation disentanglement algorithm to automatically identify and separate sub-conversations in a forum thread, and then presents these sub-conversations to the user via a custom interface specifically tailored for efficient and usable screen-reader interaction. In a user study with 11 screen-reader users, we observed that TASER significantly reduced the average user input actions and interaction times by and respectively along with a significant drop in cognitive load ( lower NASA-TLX score) compared to the status quo while performing representative information foraging tasks on accessibility forums.
An Empirical Study of Model Errors and User Error Discovery and Repair Strategies in Natural Language Database Queries

Recent advances in machine learning (ML) and natural language processing (NLP) have led to significant improvement in natural language interfaces for structured databases (NL2SQL). Despite the great strides, the overall accuracy of NL2SQL models is still far from being perfect (∼ 75% on the Spider benchmark). In practice, this requires users to discern incorrect SQL queries generated by a model and manually fix them when using NL2SQL models. Currently, there is a lack of comprehensive understanding about the common errors in auto-generated SQLs and the effective strategies to recognize and fix such errors. To bridge the gap, we (1) performed an in-depth analysis of errors made by three state-of-the-art NL2SQL models; (2) distilled a taxonomy of NL2SQL model errors; and (3) conducted a within-subjects user study with 26 participants to investigate the effectiveness of three representative interactive mechanisms for error discovery and repair in NL2SQL. Findings from this paper shed light on the design of future error discovery and repair strategies for natural language data query interfaces.
It Seems Smart, but It Acts Stupid: Development of Trust in AI Advice in a Repeated Legal Decision-Making Task

Humans increasingly interact with AI systems, and successful interactions rely on individuals trusting such systems (when appropriate). Considering that trust is fragile and often cannot be restored quickly, we focus on how trust develops over time in a human-AI-interaction scenario. In a 2x2 between-subject experiment, we test how model accuracy (high vs. low) and type of explanation (human-like vs. not) affect trust in AI over time. We study a complex decision-making task in which individuals estimate jail time for 20 criminal law cases with AI advice. Results show that trust is significantly higher for high-accuracy models. Also, behavioral trust does not decline, and subjective trust even increases significantly with high accuracy. Human-like explanations did not generally affect trust but boosted trust in high-accuracy models.