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Large Language Models (LLMs) have revolutionised the capability of AI models in comprehending and generating natural language text. They are increasingly being used to empower and deploy agents in real-world scenarios, which make decisions and take actions based on their understanding of the context. Therefore researchers, policy makers and enterprises alike are working towards ensuring that the decisions made by these agents align with human values and user expectations. That being said, human values and decisions are not always straightforward to measure and are subject to different cognitive biases. There is a vast section of literature in Behavioural Science which studies biases in human judgements. In this work we report an ongoing investigation on alignment of LLMs with human judgements affected by order bias. Specifically, we focus on a famous human study which showed evidence of order effects in similarity judgements, and replicate it with various popular LLMs. We report the different settings where LLMs exhibit human-like order effect bias and discuss the implications of these findings to inform the design and development of LLM based applications., Comment: Accepted at The First Workshop on AI Behavioral Science (AIBS 2024), held in conjunction with KDD 2024

The number of connected smart devices has been increasing exponentially for different Internet-of-Things (IoT) applications. Security has been a long run challenge in the IoT systems which has many attack vectors, security flaws and vulnerabilities. Securing billions of B connected devices in IoT is a must task to realize the full potential of IoT applications. Recently, researchers have proposed many security solutions for IoT. Machine learning has been proposed as one of the emerging solutions for IoT security and Reinforcement learning is gaining more popularity for securing IoT systems. Reinforcement learning, unlike other machine learning techniques, can learn the environment by having minimum information about the parameters to be learned. It solves the optimization problem by interacting with the environment adapting the parameters on the fly. In this paper, we present an comprehensive survey of different types of cyber-attacks against different IoT systems and then we present reinforcement learning and deep reinforcement learning based security solutions to combat those different types of attacks in different IoT systems. Furthermore, we present the Reinforcement learning for securing CPS systems (i.e., IoT with feedback and control) such as smart grid and smart transportation system. The recent important attacks and countermeasures using reinforcement learning B in IoT are also summarized in the form of tables. With this paper, readers can have a more thorough understanding of IoT security attacks and countermeasures using Reinforcement Learning, as well as research trends in this area.

We present a comprehensive study on the effects of proton irradiation at different energies (150 and 590 keV) with the fluence of 1x 1012 proton/cm2 on monolayer MoS2. This study not only improves our understanding of the influence of high-energy proton beams on MoS2 but also has implications for radiation-induced changes in device processing and engineering of devices from multilayer MoS2 starting material. Increasing defect density with decreasing proton irradiation energy was observed from photoluminescence spectroscopy study. These defects are attributed to sulfur vacancies observed through x-ray photoelectron spectroscopy analysis and confirmed by transmission electron microscope imaging. Scanning electron microscopy images showed the creation of grain boundaries after proton irradiation. A higher degree of surface deformation was detected with lower irradiation energies through atomic force microscopy. Inter-defect distance is increased with the increase in proton energy irradiation as estimated by transmission electron microscopy imaging. Raman spectroscopy reveals negligible structural changes in the crystal quality after the irradiation. These deformation damages due to proton irradiation are insignificant at the MoS2 layer. Based on the overall influence of low energy proton irradiation on the material characteristics, ML-MoS2 materials can be considered robust and reliable building blocks for 2D material based devices for space applications., Comment: Figures here are not finalized yet

Since 2004, researchers have been using the mathematical framework of Quantum Theory (QT) in Information Retrieval (IR). QT offers a generalized probability and logic framework. Such a framework has been shown capable of unifying the representation, ranking and user cognitive aspects of IR, and helpful in developing more dynamic, adaptive and context-aware IR systems. Although Quantum-inspired IR is still a growing area, a wide array of work in different aspects of IR has been done and produced promising results. This paper presents a survey of the research done in this area, aiming to show the landscape of the field and draw a road-map of future directions., Comment: Accepted for publication at ACM Computing Surveys on May 20, 2020

A large number of studies in cognitive science have revealed that probabilistic outcomes of certain human decisions do not agree with the axioms of classical probability theory. The field of Quantum Cognition provides an alternative probabilistic model to explain such paradoxical findings. It posits that cognitive systems have an underlying quantum-like structure, especially in decision-making under uncertainty. In this paper, we hypothesise that relevance judgement, being a multidimensional, cognitive concept, can be used to probe the quantum-like structure for modelling users' cognitive states in information seeking. Extending from an experiment protocol inspired by the Stern-Gerlach experiment in Quantum Physics, we design a crowd-sourced user study to show violation of the Kolmogorovian probability axioms as a proof of the quantum-like structure, and provide a comparison between a quantum probabilistic model and a Bayesian model for predictions of relevance., Comment: To be published at 42nd European Conference on Research in IR, ECIR 2020

Relevance is an underlying concept in the field of Information Science and Retrieval. It is a cognitive notion consisting of several different criteria or dimensions. Theoretical models of relevance allude to interdependence between these dimensions, where their interaction and fusion leads to the final inference of relevance. We study the interaction between the relevance dimensions using the mathematical framework of Quantum Theory. It is considered a generalised framework to model decision making under uncertainty, involving multiple perspectives and influenced by context. Specifically, we conduct a user study by constructing the cognitive analogue of a famous experiment in Quantum Physics. The data is used to construct a complex-valued vector space model of the user's cognitive state, which is used to explain incompatibility and interference between relevance dimensions. The implications of our findings to inform the design of Information Retrieval systems are also discussed., Comment: Accepted at ACM SIGIR International Conference on the Theory of Information Retrieval 2019

Relevance judgment in Information Retrieval is influenced by multiple factors. These include not only the topicality of the documents but also other user oriented factors like trust, user interest, etc. Recent works have identified these various factors into seven dimensions of relevance. In a previous work, these relevance dimensions were quantified and user's cognitive state with respect to a document was represented as a state vector in a Hilbert Space, with each relevance dimension representing a basis. It was observed that relevance dimensions are incompatible in some documents, when making a judgment. Incompatibility being a fundamental feature of Quantum Theory, this motivated us to test the Quantum nature of relevance judgments using Bell type inequalities. However, none of the Bell-type inequalities tested have shown any violation. We discuss our methodology to construct incompatible basis for documents from real world query log data, the experiments to test Bell inequalities on this dataset and possible reasons for the lack of violation., Comment: 11th Quantum Interaction Conference, Nice, France

Correlation has been widely used to facilitate various information retrieval methods such as query expansion, relevance feedback, document clustering, and multi-modal fusion. Especially, correlation and independence are important issues when fusing different modalities that influence a multi-modal information retrieval process. The basic idea of correlation is that an observable can help predict or enhance another observable. In quantum mechanics, quantum correlation, called entanglement, is a sort of correlation between the observables measured in atomic-size particles when these particles are not necessarily collected in ensembles. In this paper, we examine a multimodal fusion scenario that might be similar to that encountered in physics by firstly measuring two observables (i.e., text-based relevance and image-based relevance) of a multi-modal document without counting on an ensemble of multi-modal documents already labeled in terms of these two variables. Then, we investigate the existence of non-classical correlations between pairs of multi-modal documents. Despite there are some basic differences between entanglement and classical correlation encountered in the macroscopic world, we investigate the existence of this kind of non-classical correlation through the Bell inequality violation. Here, we experimentally test several novel association methods in a small-scale experiment. However, in the current experiment we did not find any violation of the Bell inequality. Finally, we present a series of interesting discussions, which may provide theoretical and empirical insights and inspirations for future development of this direction.

There are many examples of human decision making which cannot be modeled by classical probabilistic and logic models, on which the current AI systems are based. Hence the need for a modeling framework which can enable intelligent systems to detect and predict cognitive biases in human decisions to facilitate better human-agent interaction. We give a few examples of irrational behavior and use a generalized probabilistic model inspired by the mathematical framework of Quantum Theory to model and explain such behavior., Comment: Published at the Workshop on AI and Computational Psychology at IJCAI-ECAI 2018

There is a growing body of research which has investigated relevance judgment in IR being influenced by multiple factors or dimensions. At the same time, the Order Effects in sequential decision making have been quantitatively detected and studied in Mathematical Psychology. Combining the two phenomena, there have been some user studies carried out which investigate the Order Effects and thus incompatibility in different dimensions of relevance. In this work, we propose a methodology for carrying out such an investigation in large scale and real world data using query logs of a web search engine, and device a test to detect the presence of an irrational user behavior in relevance judgment of documents. We further validate this behavior through a Quantum Cognitive explanation of the Order and Context effects., Comment: Published at ACM ICTIR 2018

A challenging task for word embeddings is to capture the emergent meaning or polarity of a combination of individual words. For example, existing approaches in word embeddings will assign high probabilities to the words "Penguin" and "Fly" if they frequently co-occur, but it fails to capture the fact that they occur in an opposite sense - Penguins do not fly. We hypothesize that humans do not associate a single polarity or sentiment to each word. The word contributes to the overall polarity of a combination of words depending upon which other words it is combined with. This is analogous to the behavior of microscopic particles which exist in all possible states at the same time and interfere with each other to give rise to new states depending upon their relative phases. We make use of the Hilbert Space representation of such particles in Quantum Mechanics where we subscribe a relative phase to each word, which is a complex number, and investigate two such quantum inspired models to derive the meaning of a combination of words. The proposed models achieve better performances than state-of-the-art non-quantum models on the binary sentence classification task., Comment: This paper has been accepted by the 3rd Workshop on Representation Learning for NLP (RepL4NLP)

It has been shown that relevance judgment of documents is influenced by multiple factors beyond topicality. Some multidimensional user relevance models (MURM) proposed in literature have investigated the impact of different dimensions of relevance on user judgment. Our hypothesis is that a user might give more importance to certain relevance dimensions in a session which might change dynamically as the session progresses. This motivates the need to capture the weights of different relevance dimensions using feedback and build a model to rank documents for subsequent queries according to these weights. We propose a geometric model inspired by the mathematical framework of Quantum theory to capture the user's importance given to each dimension of relevance and test our hypothesis on data from a web search engine and TREC Session track.

Computational chemists typically assay drug candidates by virtually screening compounds against crystal structures of a protein despite the fact that some targets, like the $\mu$ Opioid Receptor and other members of the GPCR family, traverse many non-crystallographic states. We discover new conformational states of $\mu OR$ with molecular dynamics simulation and then machine learn ligand-structure relationships to predict opioid ligand function. These artificial intelligence models identified a novel $\mu$ opioid chemotype., Comment: 28 pages, machine learning, computational biology, GPCRs, molecular dynamics, molecular docking, molecular simulation

DXL (Diffuse X-rays from the Local Galaxy) is a sounding rocket mission designed to quantify and characterize the contribution of Solar Wind Charge eXchange (SWCX) to the Diffuse X-ray Background and study the properties of the Local Hot Bubble (LHB). Based on the results from the DXL mission, we quantified and removed the contribution of SWCX to the diffuse X-ray background measured by the ROSAT All Sky Survey (RASS). The "cleaned" maps were used to investigate the physical properties of the LHB. Assuming thermal ionization equilibrium, we measured a highly uniform temperature distributed around kT=0.097 keV+/-0.013 keV (FWHM)+/-0.006 keV (systematic). We also generated a thermal emission measure map and used it to characterize the three-dimensional (3D) structure of the LHB which we found to be in good agreement with the structure of the local cavity measured from dust and gas., Comment: 8 pages, 10 figures, Accepted for publication on ApJ

DXL (Diffuse X-ray emission from the Local Galaxy) is a sounding rocket mission designed to estimate the contribution of Solar Wind Charge eXchange (SWCX) to the Diffuse X-ray Background (DXB) and to help determine the properties of the Local Hot Bubble (LHB). The detectors are large-area thin-window proportional counters with a spectral response similar to that of the PSPC used in the ROSAT All Sky Survey (RASS). A direct comparison of DXL and RASS data for the same part of the sky viewed from quite different vantage points in the Solar system and the assumption of approximate isotropy for the Solar wind allowed us to quantify the SWCX contribution to all 6 RASS bands (R1-R7, excepting R3). We find that the SWCX contribution at l=140 deg, b=0 deg, where the DXL path crosses the Galactic plane is 33%+-6% (statistical)+-12%(systematic) for R1, 44%+-\%+-5% for R2, 18%+-12%+-11% for R4, 14%+-11%+-9% for R5, and negligible for R6 and R7 bands. Reliable models for the distribution of neutral H and He in the Solar system permit estimation of the contribution of interplanetary SWCX emission over the the whole sky and correction of the RASS maps. We find that the average SWCX contribution in the whole sky is 26%+-6%+-13% for R1, 30%+-4%+-4% for R2, 8%+-5%+-5% for R4, 6%+-4%+-4% for R5, and negligible for R6 and R7., Comment: 9 pages, 13 figures, accepted for publication on ApJ

The Solar neighborhood is the closest and most easily studied sample of the Galactic interstellar medium, an understanding of which is essential for models of star formation and galaxy evolution. Observations of an unexpectedly intense diffuse flux of easily-absorbed 1/4 keV X rays, coupled with the discovery that interstellar space within ~100 pc of the Sun is almost completely devoid of cool absorbing gas led to a picture of a "local cavity" filled with X-ray emitting hot gas dubbed the local hot bubble. This model was recently upset by suggestions that the emission could instead be produced readily within the solar system by heavy solar wind ions charge exchanging with neutral H and He in interplanetary space, potentially removing the major piece of evidence for the existence of million-degree gas within the Galactic disk. Here we report results showing that the total solar wind charge exchange contribution is 40% +/- 5% (stat) +/- 5% (sys) of the 1/4 keV flux in the Galactic plane. The fact that the measured flux is not dominated by charge exchange supports the notion of a million-degree hot bubble of order 100 pc extent surrounding the Sun., Comment: 9 pages, 6 figures, 1 table. The final version of the paper is available on Nature

To reduce tear and wear of machinery lubrication is essential. Lubricants form a layer between two surfaces preventing direct contact and reduce friction between moving parts and hence reduce wear. In this short letter the lubrication of two slider bearings with parallel and nonparallel is studied. First, we show that bearings with parallel plates cannot support any load. For bearings with nonparallel plates we are interested on how constant and temperature dependent viscosity affects the properties of the bearings. Also, a critical temperature for which the bearings would fail due to excess in temperature is found for both latter cases. If the viscosity is constant, the critical temperature is given by an explicit formula, while for the non-constant viscosity the critical temperature can be always found from a closed form formula involving Weber functions, Comment: 8 pages, 3 figures

The Diffuse X-rays from the Local galaxy (DXL) mission is an approved sounding rocket project with a first launch scheduled around December 2012. Its goal is to identify and separate the X-ray emission generated by solar wind charge exchange from that of the local hot bubble to improve our understanding of both. With 1,000 cm2 proportional counters and grasp of about 10 cm2 sr both in the 1/4 and 3/4 keV bands, DXL will achieve in a 5-minute flight what cannot be achieved by current and future X-ray satellites., Comment: 15 Pages, 5 figures. Accepted for publication on Experimental Astronomy