Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Semantics and Pragmatics of Spatial Prepositions via Hierarchical Common-Sense Primitives

Understanding spatial expressions and using them appropriately is necessary for seamless and natural human-machine interaction. However, capturing the semantics and appropriate usage of spatial prepositions is notoriously difficult, because of their vagueness and polysemy. Although modern data-driven approaches are good at capturing statistical regularities in the usage, they usually require substantial sample sizes, often do not generalize well to unseen instances and, most importantly, their structure is essentially opaque to analysis, which makes diagnosing problems and understanding their reasoning process difficult. In this work, we discuss our attempt at modeling spatial senses of prepositions in English using a combination of rule-based and statistical learning approaches. Each preposition model is implemented as a tree where each node computes certain intuitive relations associated with the preposition, with the root computing the final value of the prepositional relation itself. The models operate on a set of artificial 3D “room world” environments, designed in Blender, taking the scene itself as an input. We also discuss our annotation framework used to collect human judgments employed in the model training. Both our factored models and black-box baseline models perform quite well, but the factored models will enable reasoned explanations of spatial relation judgements.
Probabilistic Safe Adaptive Merging Control for Autonomous Vehicles under motion uncertainty

—In this work we address the safe adaptive control problem for autonomous vehicles in the highway on-ramp merging scenario. We argue that by developing a Control Barrier Function (CBF)-based method, autonomous vehicles are able to perform adaptive interactions with human drivers with safety guarantees under uncertainty. We propose a novel extension of traditional CBF to a probabilistic setting for stochastic system dynamics with provable chance-constrained safety and provide a theoretical analysis to discuss the solution feasibility guarantee and design factors reﬂecting different vehicle behaviors. This allows for adaptation to different driving strategies with a formally provable feasibility guarantee for the ego vehicle’s safe controller. The results demonstrate the enhanced safety and adaptability of our proposed approach.
LGBM Classifier based Technique for Predicting Type-2 Diabetes

In today’s world, Diabetes Mellitus is a disease, that is considered to be an extensive noncommunicable disease which has a great effect our day to day living. In the 21st century, changes in natural life style and labor culture are some of the main reasons for India to have 62 million diabetic cases as of today. Analytical Computational Techniques can be applied on clinical immense data, the enormous quantity of data produced in the healthcare schemes, there is a option to form medicinal intelligence which will initiative medical forecast and predicting in future. By advancing medical intelligence and with the help of development model, prediction and detection of diabetes disease can be done. With the increase in complexity to the problems, the accuracy percentage also varies. LGBM Light Gradient Boosting Algorithm is one such algorithm that can be used as it depends on decision tree algorithms and it can be used in predicting the accuracy to attain the desired results. With the existing PIMA Indian Dataset the accuracy is calculated as 95.20% using LGBM Algorithm . Therefore by using the LGBM classifiers, we can develop a data model for diabetes detection and prediction.
AMR-TV: Antimicrobial Resistance Transmission Visualizer

—We present the application AMR-TV, a visualization solution for identifying potential AMR transmission routes from the publicly available data in the NCBI Isolates Browser. AMR-TV uses three successive views with increasing levels of detail: an adjacency matrix view, a node link diagram view, and table view. We show how infection control healthcare workers can use these three views to elucidate the source and extent of specific AMR outbreaks, and in some cases, even hypothesize the geographic and host relationships facilitating potential AMR transmission routes. During the development of AMR-TV, we ran into some problems related to the inconsistent data
PHYSICAL REVIEW B 103, 205417 (2021) Light-induced static magnetization: Nonlinear Edelstein effect

Light can interact with magnetism in materials. Motivated by the Edelstein effect, whereby a static electric field can generate magnetization in metals, in this work we theoretically and computationally demonstrate that static magnetization can also be generated through light in semiconductors. Such an effect is essentially a second-order nonlinear response and can be considered as a generalization of the Edelstein effect. This nonlinear Edelstein effect (NLEE) applies to semiconductors under both linearly and circularly polarized light, and there are no constraints from either spatial inversion or time-reversal symmetry. With ab initio calculations, we reveal several prominent features of NLEE. We find that the light-induced orbital magnetizations can be significantly greater than the spin magnetizations, in contrast to standard intrinsic magnetism where the orbital magnetic moment is strongly quenched under crystal field. We show that in multilayer (multisublattice) materials, different ferromagnetic and ferrimagnetic structures can be realized under photon pumping, depending on the interlayer (intersublattice) symmetry. It is also possible to switch the magnetic ordering in antiferromagnetic materials. The relationship between NLEE and other magneto-optic effects, including the inverse Faraday effect and inverse Cotton-Mouton effect, is also discussed.
Bayesian Estimation of Ammunition Demand Based on Multinomial Distribution

In view of the small sample size of combat ammunition trial data and the difficulty of forecasting the demand for combat ammunition, a Bayesian inference method based on multinomial distribution is proposed. Firstly, considering the different damage grades of ammunition hitting targets, the damage results are approximated as multinomial distribution, and a Bayesian inference model of ammunition demand based on multinomial distribution is established, which provides a theoretical basis for forecasting the ammunition demand of multigrade damage under the condition of small samples. Secondly, the conjugate Dirichlet distribution of multinomial distribution is selected as a prior distribution, and Dempster–Shafer evidence theory (D-S theory) is introduced to fuse multisource previous information. Bayesian inference is made through the Markov chain Monte Carlo method based on Gibbs sampling, and ammunition demand at different damage grades is obtained by referring to cumulative damage probability. The study result shows that the Bayesian inference method based on multinomial distribution is highly maneuverable and can be used to predict ammunition demand of different damage grades under the condition of small samples.
Security Analysis of KNOT-AEAD and KNOT-Hash

KNOT is one of the 32 second-round candidates in NIST’s lightweight cryptography standardization process. To have a better understanding of the security of KNOT, in this paper, we concentrate on the search of the best differential and linear distinguishers with constraints that can be directly used to mount attacks on KNOT-AEAD and KNOT-Hash. Six attack models for KNOT-AEAD and two attack models for KNOT-Hash are considered. By studying differential/linear trails containing iterative sub-trails, we can efficiently obtain effective difference/linear propagations with constraints with respect to the 6 attack models for each KNOTAEAD member and the 2 attack models for each KNOT-Hash member. Furthermore, we investigate the accuracy of our new method in two different ways. Firstly, we apply our new method to RECTANGLE, which is an ancestor of the KNOT permutations, and compare the results obtained by our new method with those provided by the designers of RECTANGLE. Secondly, we use MILP modelling method to compute the differential and linear clustering effect of the 256-bit KNOT permutation and compare the results obtained by MILP method with those obtained by our new method. According to these comparative results, we can reasonably infer that the results using our new approach provide a quite accurate security evaluation of KNOT-AEAD and KNOT-Hash. To sum up, based on our results in this paper, considering the data limit under one key, each KNOT-AEAD member has at least 50% security margin against the 6 attack models (especially, the initialization phase has at least 72% security margin); each KNOT-Hash member has at least 80% security margin against the 2 attack models.
Liquidity Synchronization and Asset Valuation in Selected Emerging Asian Economies

This study examined the impact of liquidity synchronization on stock valuation in selected emerging economies in Asia. Empirical testing was motivated by the relevance of liquidity synchronization in asset pricing. Liquidity synchronicity is a non-diversifiable risk, which can affect the overall functioning of a market. The regulatory environments of Asian firms are different from those investigated in previous studies. Comprehensive analysis of the subject is limited in emerging economies mainly due to the small size of the markets and the constraint of data availability. A substantial knowledge gap provides the underlying foundation of this study. Three emerging economies of Asia â€“ China, Pakistan and India â€“ were selected for analysis using data from 2010 to 2019. The implied cost of equity pricing model and realized returns pricing model were employed to study the impact of liquidity synchronicity on asset valuation. Liquidity synchronization was found to have a significant impact on asset valuation in emerging Asian economies and the effect is stronger during market volatility.
An Analysis of First-Party Cookie Exfiltration due to CNAME Redirections

—DNS CNAME redirections, which can “steer” browser requests towards a domain different than the one in the request’s URI, are a simple and oftentimes effective means to obscure the source of a web object behind an alias. These redirections can be used to make third-party content appear as ﬁrst-party content. The practice of evading browser security mechanisms through misuse of CNAMEs, referred to as CNAME cloaking, has been recently growing in popularity among advertisers/trackers to bypass blocklists and privacy policies. While CNAME cloaking has been reported in past measurement studies, its impact on browser cookie policies has not been analyzed. We close this gap by presenting an in-depth characterization of how CNAME redirections affect cookie propagation. Our analysis uses two distinct data collection samples (June and December 2020). Beyond conﬁrming that CNAME cloaking continues to be popular, our analysis identiﬁes a number of websites transmitting sensitive cookies to cloaked third-parties, thus breaking browser cookie policies. Manual review of such cases identiﬁes exﬁltration of authentication cookies to advertising/tracking domains, which raises serious security concerns.
Rebels without a cause ? A study about generations , gender , diversity climate and work attitudes

Stefano Basaglia Osservatorio sul Diversity Management SDA Bocconi School of Management – Via Bocconi, 8 – 20136 Milano; e-mail address: stefano.basaglia@sdabocconi.it Chiara Paolino Osservatorio sul Diversity Management SDA Bocconi School of Management – Via Bocconi, 8 – 20136 Milano; e-mail address: chiara.paolino@unibocconi.it Zenia Simonella Osservatorio sul Diversity Management SDA Bocconi School of Management – Via Bocconi, 8 – 20136 Milano; e-mail address: zenia.simonella@sdabocconi.it
Reducing energy cost of in situ nitrogen fixation in water using an arc-DBD combination

In this study, a sustainable air plasma-based nitrogen fixation into reactive nitrogen species in water (e.g. nitrate and nitrite) is presented, which mimics the lightning-based nitrogen fixation that occurs in nature. We report a hybrid plasma system that combines a dielectric barrier discharge (DBD) reactor (as an efficient ozone source) and an arc plasma reactor (as an efficient NO x source). This hybrid plasma system completely converts gaseous NO x and O3 into NOx− in water with almost zero emissions (NO x and O3). Gaseous outlets, in-liquid species, and electrical parameters were investigated. The energy cost for nitrate production via a single-pass reaction was extremely reduced to 8 MJ mol−1, which enhanced the applicability of the direct one-step nitrogen fixation into NOx− in water for distributed fertilizer production to decrease the dependence on the Haber–Bosh process toward sustainable agricultural development.
Semiconductors Used in Photovoltaic and Photocatalytic Devices: Assessing Fundamental Properties from DFT

The photovoltaic and photocatalytic systems generally use at least one semiconductor in their architecture which role is to absorb the light or to transport the charge carriers. Despite the large variety of working principles encountered in these systems, they share some fundamental steps such as light absorption, exciton dissociation, and charge carrier diffusion. These phenomena are governed by fundamental properties of the semiconductor like the bandgap, the dielectric constant, the charge carrier effective masses, and the exciton binding energy. The ability of density functional theory to compute all of these properties is evaluated. From the particularly good results obtained with the HSE06 functional, it can be concluded that DFT is a reliable tool for the evaluation and prediction of these key properties which open nice perpectives for in silico design of improved semiconductors for solar energy application. In the light of these calculations, some experimental observations on the difference of eff.
Microscale Processes Determining Macroscale Evolution of Magnetic Flux Tubes along Earth’s Magnetopause

An important process affecting solar wind–Earth’s magnetosphere coupling is nonsteady dayside magnetic reconnection, observationally evidenced by a flux transfer event (FTE) that shows a bipolar variation of the magnetic field component normal to the magnetopause. FTEs often consist of two interlinked flux tubes, but, local kinetic processes between the flux tubes are not understood in the context of the FTE structuring, evolution, and impact. An FTE observed by the Magnetospheric Multiscale mission on 2017 December 18 consisted of two flux tubes of different topology. One includes field lines with ends connected to the northern and southern hemispheres while the other includes field lines with both ends connected to the magnetosheath. Reconnection occurring at the flux-tube interface indicates how interacting flux tubes evolve into a flux rope with helical magnetic topology that is either closed or open. This study demonstrates a new aspect of how micro- to meso-scale dynamics occurring within FTEs determines their macroscale characteristics and evolution.
The future evolution of energy-water-agriculture interconnectivity across the US

Energy, water, and agricultural resources across the globe are highly interconnected. This interconnectivity poses science challenges, such as understanding and modeling interconnections, as well as practical challenges, such as efficiently managing interdependent resource systems. Using the US as an example, this study seeks to define and explore how interconnectivity evolves over space and time under a range of influences. Concepts from graph theory and input–output analysis are used to visualize and quantify key intersectoral linkages using two new indices: the ‘Interconnectivity Magnitude Index’ and the ‘Interconnectivity Spread Index’. Using the Global Change Analysis Model (GCAM-USA), we explore the future evolution of these indices under four scenarios that explore a range of forces, including socioeconomic and technological change. Analysis is conducted at both national and state level spatial scales from 2015 to 2100. Results from a Reference scenario show that resource interconnectivity in the US is primarily driven by water use amongst different sectors, while changes in interconnectivity are driven by a decoupling of the water and electricity systems, as power plants become more water-efficient over time. High population and GDP growth results in relatively more decoupling of sectors, as a larger share of water and energy is used outside of interconnected sector feedback loops. Lower socioeconomic growth results in the opposite trend. Transitioning to a low-carbon economy increases interconnectivity because of the expansion of purpose-grown biomass, which strengthens the connections between water and energy. The results highlight that while some regions may experience similar sectoral stress projections, the composition of the intersectoral connectivity leading to that sectoral stress may call for distinctly different multi-sector co-management strategies. The methodology we introduce here can be applied in diverse geographical and sectoral contexts to enable better understanding of where, when, and how coupling or decoupling between sectors could evolve and be better managed.
Interactive Mechanical Systems Using Mathematica

This work shows a new proposal for teaching mechanical systems (SM) modelled through differential equations (SED) using the Mathematica package. The adopted strategy considers four stages. In the first, simple SMS, such as the mass-spring system on a plane or the simple pendulum, are analysed. The Lagrangian of each system is determined and the Euler-Lagrange equations are used to obtain the equations of motion. In other cases, the Hamiltonian of the systems is determined and the Hamilton equations are used. Afterwards, the resulting EDS are solved analytically. During the second stage, SED solution programs in Mathematica are developed by using classical numerical methods such as a fourth order Runge-Kutta method (Rk4) and a Runge-Kutta-Feldberg method of order 4-5 (RKF45). The programs are tested with the examples from the previous stage. In the next stage, a set of programs is elaborated in order to determine the equations of motion for any system by using the Euler-Lagrange equations (Hamilton) from the Lagrangian (Hamiltonian) of the system. In the last stage, complex problems such as the movement of planets in the solar system or the movement of a particle over a conical surface are solved. The programs developed during stages 2 and 3 are used in order to obtain a numerical solution to these examples. Finally, interactive graphical interfaces are constructed for each proposed system, and are used to study and analyse the physical phenomena. As a result, engineering students who use and build graphical interfaces have improved their understanding of SED and their use in classical mechanics. In addition, they change their previous ideas about the scope of mechanics and SEDs, they gain confidence in their knowledge and they are able to find solutions to dynamical systems through the use of simple numerical techniques.
Accelerated Policy Search

Policy search is among the most successful approaches to learning on real robots. While impressive results were achieved in the past, policy search methods are limited in their applicability as they are generally sample-inefficient and are known to suffer from local optima. A reasonable approach to tackle these problems emerges from advanced acceleration concepts in mathematical optimization. Acceleration techniques are proven to enhance the convergence properties of gradient-based optimization algorithms if certain mathematical conditions are satisfied. However, it is unclear how these concepts transfer to policy search. In this thesis, acceleration of policy search is studied on the basis of a particular acceleration scheme applicable to mirror descent. Inspired by Gaussian accelerated mirror descent search (G-AMDS), we derive two practical mirror descent variants operating in an infinite-dimensional setting: distributional mirror descent (DMD) and accelerated distributional mirror descent (ADMD), where the latter incorporates the acceleration scheme at hand. Based on these two algorithms the effects of accelerating policy search are researched in an experimental study on continuous control problems. Further, ADMD is compared to state-of-the-art policy search methods both practically and theoretically. Our results suggest, that while acceleration in policy search generally enhances convergence properties, acceleration techniques are more valuable in different optimization settings.
Regional Hegemonic Power, Integration and Governance: An Appraisal of Post-Democratization South Africa

This article addresses the question of what drives the character and orientation of South Africa’s foreign policy post the apartheid era. The shift from apartheid to democracy in 1994 brought with it a new paradigm shift in both South Africa’s domestic and foreign policy agenda. This was also driven by the need to redress the destabilization policies of the apartheid regime. The demise of apartheid in 1994 brought with it immense jubilation both continentally and abroad and South Africa was now for the first time in years reintegrated into the global economy. By undertaking this enquiry, the study attempts to draw a nuanced evaluation of South Africa’s foreign policy, particularly in Southern Africa since its transition to democracy in 1994.
An Inquiry Into Achieving Social Sustainability From Boutique Hotels in Sri Lanka

Achieving social sustainability is a challenge while managing the internal and external pressure in a hotel. In Sri Lanka majority of the boutique hotels has not paid attention to the importance of social sustainability and to its impact on economic sustainability. There is no proper guiding mechanism to link the society toward the boutique hotels to drive to social sustainability in Sri Lanka as a win-win situation. This paper aims to develop a good practice framework for boutique hotels in Sri Lanka to drive them toward achieving social sustainability and to explore the internal and external factors that should consider implementing the initiatives to achieve social sustainability. This paper used case study methodology to implement the deductive approach. The author used qualitative data collected from the interviews with the senior management, employees, guests and community leaders. Data contributed to developing the good guidance framework. The selected case is an award-winning group owned only registered boutique hotel. Research carried over a period of one month and analysed using within data through coding. The findings revealed the internal and external elements that should be considered when it comes to successful implementation to achieve social sustainability. In addition, the most applicable key elements to drive toward social sustainability were identified. All these identified elements were integrated to the proposed good practice framework. The paper suggests although boutique hotels implement activities to achieve social sustainability a well-placed framework should place a leading role in achieving social sustainability successfully while extending the benefits to society.