Miracle-dz/UniFAR-FTUs huggingface dataset:
Sustainable Questions Determining the Expiration Date of Answers

Community question answering platforms have large repositories of previously answered questions. Reusing the answers for new questions is tempting. However, not all stored answers will still be relevant. We define a new and challenging problem concerning the sustainability of questions, and present metrics aimed at distin- guishing between sustainable and non-sustainable questions. We find that an intuitive approach to sustainability of questions is not sufficient, but that simple properties can already distinguish between sustainable and non-sustainable questions. are defined to be sustainable when the answer to that question is independent of the point in time the question is asked. In this study, we operationalize sustainability in terms of semantic similarity over time. In this study, we investigate the patterns exhibited by sustain- able questions, and whether these patterns can be used to distinguish sustainable from non-sustainable questions. In order to be able to do so, we first explore three approaches to finding similar questions. We find that our best performing approach based on latent semantic analysis does not perform satisfactory causing us to create a manually labeled set of similar questions which we use for further analysis. We consider two properties of questions to determine sustainabil- ity: the rate at which answers to similar questions change over time, and the time between asking a question and receiving answers. We find that (a) clustering questions with similar semantics is a daunting task; (b) an intuitive approach to sustainability in questions by modeling change in their answers is not sufficient; and (c) very simple question properties can already distinguish sustainable ques- tions from others in a reasonable manner. Our contributions are (a) the definition of this new problem concerning sustainability of questions; (b) our conclusion that the new problem is not trivially solved; (c) insight in factors that play a role by the definition of the problem; and (d) metrics aimed at distinguishing between sustain- able and non-sustainable questions and answers.
House price expectations and inflation expectations: Evidence from survey data

There is an increased interest in understanding the determinants for households ’ inflation expectations. Recent literature on salience has found that individuals focus disproportionately more on frequently observed prices and large price changes when forming their inflation expectations, even if those items have low weights in the official inflation measure. The impact of gas and grocery prices in this regard has been well - established in the literature. In our work, we find a novel channel through house prices. We find that households tend to overweight house price expectations when forming their inflation expectations. This result is robust across several specifications and two survey data sets for the United States. We also observe a significant effect of the cognitive abilities of households as more sophisticated households overweight house price inflation less. We model this household behaviour in a two - sector New Keynesian model and examine its monetary policy implications. In this setup, we show that to gauge the correct interest rate response, the central bank needs to be aware that some sectors are overweighted by households and that movements in expected inflation in such sectors are important for monetary policy.
Red Teaming Generative AI/NLP, the BB84 quantum cryptography protocol and the NIST-approved Quantum-Resistant Cryptographic Algorithms

In the contemporary digital age, Quantum Computing and Artificial Intelligence (AI) convergence is reshaping the cyber landscape, introducing unprecedented opportunities and potential vulnerabilities.This research, conducted over five years, delves into the cybersecurity implications of this convergence, with a particular focus on AI/Natural Language Processing (NLP) models and quantum cryptographic protocols, notably the BB84 method and specific NIST-approved algorithms. Utilising Python and C++ as primary computational tools, the study employs a"red teaming"approach, simulating potential cyber-attacks to assess the robustness of quantum security measures. Preliminary research over 12 months laid the groundwork, which this study seeks to expand upon, aiming to translate theoretical insights into actionable, real-world cybersecurity solutions. Located at the University of Oxford's technology precinct, the research benefits from state-of-the-art infrastructure and a rich collaborative environment. The study's overarching goal is to ensure that as the digital world transitions to quantum-enhanced operations, it remains resilient against AI-driven cyber threats. The research aims to foster a safer, quantum-ready digital future through iterative testing, feedback integration, and continuous improvement. The findings are intended for broad dissemination, ensuring that the knowledge benefits academia and the global community, emphasising the responsible and secure harnessing of quantum technology.
A probable case of multiple

Paleopathological evidence of cancer from past populations is rare, especially outside of Europe and North Africa. To begin to fill the gaps in the geographical distribution of neoplastic disease, this study presents a probable case of multiple myeloma in an adult male from the Qijia culture horizon (1750-1400 BCE) of the Bronze Age cemetery of Mogou ( 磨 磨 ), located in Gansu Province, Northwest China. Multiple ovoid-shaped osteolytic lesions with sharply demarcated margins were assessed macroscopically and radiographically. The axial skeletal had the greatest involvement, specifically the vertebrae, ribs, and sternum. Radiographic imaging revealed more extensive destruction of hematogenous than cortical bone, indicating that the marrow was the focal point of the disease. Based on the nature, distribution, and radiographic appearance of the lesions, the most likely diagnosis is multiple myeloma. This is one of the only cases of cancer identified in archaeological human skeletal remains in East Asia. By placing case studies such as this into a temporal and spatial framework, it is possible for future research to begin to interrogate possible underlying causes of cancer in ancient populations within the context of changing environmental conditions and subsistence strategies.
PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval

Point cloud based retrieval for place recognition is an emerging problem in vision field. The main challenge is how to find an efficient way to encode the local features into a discriminative global descriptor. In this paper, we propose a Point Contextual Attention Network (PCAN), which can predict the significance of each local point feature based on point context. Our network makes it possible to pay more attention to the task-relevent features when aggregating local features. Experiments on various benchmark datasets show that the proposed network can provide outperformance than current state-of-the-art approaches.
Trek – Tourism and Travel Destinations Promoting System with AR based Rewards and Health Monitoring Device

The tourism industry has been severely impacted by a series of challenges in recent years, including the COVID-19 pandemic, the Russia-Ukraine War, and supply chain disruptions. These adversities have led to economic downturns and high inﬂation rates, making it diﬃcult for the industry to recover and thrive. The existing situation is characterized by decreased travel demand, reduced revenue for tourism businesses
Impact of urban land use on mean and heavy rainfall during the Indian summer monsoon

Abstract. Northern India has undergone intense urbanization since the middle of the 20th century. The impact of such drastic land-use change on the regional weather and climate remains to be assessed. In this work, we study the impact of the modification of land use – from vegetation to urban – on the Indian summer monsoon rainfall as well as on other meteorological variables. We use the regional Meso-scale Non-Hydrostatic (Meso-NH) model coupled with an urban module (the Town Energy Balance model) to perform monthlong sensitivity simulations centered around Kolkata, the most urbanized area in northeastern India. Paired simulations, one with and another without urban settings, have been performed to identify the impacts related to urbanization through both thermodynamic and kinetic effects. We find that the perturbation induced by urban land use enhances the mean rainfall over the model domain, principally by intensifying the convective activity through thermodynamic perturbation, leading to a 14.4 % increase in the monthly mean rainfall. The urban area also induces a 15.0 % rainfall increase during two modeled periods of heavy precipitation caused by low-pressure systems. In addition, the modeling results demonstrate that the urban area not only generally acts as a rainfall enhancer, particularly during nighttime, but also induces the generation of a specific storm in one modeled case that would not have formed in the absence of the urban area. The initiation of this storm over the city was primarily due to the urban terrain's disturbance of the near-surface wind flow, leading to a surge in dynamically produced turbulent kinetic energy (TKE). The thermal production of TKE over the nighttime urban boundary layer, on the other hand, serves as a contributing factor to the storm formation.
Towards Full Stack Acceleration of Deep Convolutional Neural Networks on FPGAs

—Due to the huge success and rapid development of convolution neural networks (CNNs), there is a growing demand of hardware accelerators that accommodate a variety of CNNs to improve the inference latency and energy efﬁciency, in order to enable their deployment in real-time applications. Among popular platforms, Field-Programmable Gate Arrays (FPGAs) have been widely adopted for CNN acceleration because of their capability to provide superior energy efﬁciency and low-latency processing, while supporting high reconﬁgurability, making them favourable for accelerating rapidly evolving CNN algorithms. This paper introduces a highly customized and streaming hardware architecture which focuses on improving the compute efﬁciency for streaming applications by providing full stack acceleration of CNNs on FPGAs. The proposed accelerator maps the most computational functions, i.e. convolutional and deconvolutional layers in one uniﬁed module, and implements the residual and concatenative connections with high efﬁciency, in support to the inference of mainstream CNNs with different topologies. This architecture is further optimized through exploiting different level of parallelism, layer fusion and fully leveraging DSPs. The proposed accelerator has been implemented on Intel’s Arria 10 GX1150 hardware and evaluated with a wide range of benchmark models. The results demonstrate a high performance of over 1.3 TOP/s of throughput, up to 97% of compute (MAC) efﬁciency, which outperforms the state-of-the-art FPGA accelerators.
Nonsingular boundary integral method for deformable drops in viscous flows

A three-dimensional boundary integral method for deformable drops in viscous flows at low Reynolds numbers is presented. The method is based on a new nonsingular contour-integral representation of the single and double layers of the free-space Green's function. The contour integration overcomes the main difficulty with boundary-integral calculations: the singularities of the kernels. It also improves the accuracy of the calculations as well as the numerical stability. A new element of the presented method is also a higher-order interface approximation, which improves the accuracy of the interface-to-interface distance calculations and in this way makes simulations of polydispersed foam dynamics possible. Moreover, a multiple time-step integration scheme, which improves the numerical stability and thus the performance of the method, is introduced. To demonstrate the advantages of the method presented here, a number of challenging flow problems is considered: drop deformation and breakup at high viscosity ratios for zero and finite surface tension; drop-to-drop interaction in close approach, including film formation and its drainage; and formation of a foam drop and its deformation in simple shear flow, including all structural and dynamic elements of polydispersed foams.
Perception of HIV risk and the quantity and quality of children : The case of rural Malawi

The empirical literature on HIV and the quality (Q) and quantity (N) of children generally reports negative associations for Q and unclear associations for N. We focus our analysis on the effects of HIV, as a predictor of mother and child mortality, on investments in child Q and N. We develop a Q-N model within which higher mothers’ mortality predicts lower N while higher child mortality predicts lower Q. Those effects together make reasonable the expectation of negative influences of higher HIV likelihood on child Q and N. Based on longitudinal micro data on mothers and their children in rural Malawi we find that variation in mothers’ reported HIV risk reduces both child quality, as reflected in children’s schooling and health, and child quantity, when the perceived risk is already moderate or high. The effects are sizable, and, in the case of Q (schooling and health) are found in children and teenagers, as well as boys and girls, while in the case of N are found for young and mature women.
New frontiers in CRISPR/Cas9 delivery systems delivery for gene editing

Since its discovery in early 1980s to date, our knowledge about CRISPR has evoloved so much. CRISPR/Cas system confers the immunization ability against phages to the bacteria and is comprised of three stages: acquisition, biogenesis, and interference. It was ﬁrst in 2012 when Charpentier and Doudna proposed Cas9 enzyme as a novel gene editing tool. They opened a new gate on the way of gene editing which led to new discoveries as well as novel treatments for diseases. It must be taken into consideration that the delivery of CRISPR/Cas9 remains a great barrier on the way of mass clinical exploition. The delivery of CRISPR/Cas9 requires a cargo and a vehicle. There are three types of cargos: plasmids, mRNAs, and enzymes. There two main types of vehicles as well: viral and non-viral. Non-viral delivery can be subdivided into physical and various nanoparticle mediated delivery. In this systematic review, we aimed to give an overall insight to the all delivery methods used for the delivery of CRISPR/Cas9 used for gene editing both in-vitro and in-vivo. We conducted a thorough search from two databases: Medline and scopus. After the screening Process we found 77 eligible papers and the results are illustrated in table 1,2, and 3.
How and why can explicit instruction about L1 reduce the negative effects of crosslinguistic influence? Evidence from accuracy and reaction time signatures in L1 comprehension

This study revisits and extends McManus and Marsden (2019a) to better understand how and why providing additional explicit information (EI) about learners’ first language (L1) alongside EI and comprehension practice in the second language (L2) improved the accuracy, speed, and stability of L2 comprehension of the French Imparfait, a functionally complex and late-acquired target feature. Building on previous L2 research that has provided instruction about L1 without analyzing L1 knowledge/use, the current study examines learners’ item-by-item comprehension of L1 sentences that was completed during the L2 instruction to better understand the learning mechanisms at play in McManus and Marsden’s (2019a) findings. Two groups of English-speaking learners (L2+L1, L2+L1prac; N = 36) received the same EI about aspect in French, followed by comprehension practice of French and English sentences. The L2+L1 group received additional EI about aspect in English. Results showed no impact of the instruction on L1 accuracy in either group, but the speed of L1 comprehension changed over time in the L2+L1 group. It is argued that the L2+L1 instruction directed attention to and clarified crosslinguistic differences in how L2 and L1 express the same meanings, which rendered the L2 practice more effective for noticing Imparfait and developing appropriate strategies for processing L2 forms. Implications for theory-building and instructional design are discussed.
The impact of climate change on phytoplankton - bacterioplankton interactions

Global warming has already and is continuing to impact the global oceans. Half of the global primary production is performed by phytoplankton in the oceans and heterotrophic marine bacteria channel a substantial amount of primary organic carbon through the microbial loop. Understanding the influence of climate change on these important processes is therefore essential for an assessment of the vulnerability of the carbon cycle and possible feedbacks. This thesis reports results from investigations on the temperature dependent coupling between phytoplankton and bacterioplankton, with respect to additional effects of light intensity and inorganic nutrient concentrations. During four consecutive years, mesocosm experiments with natural Kiel Fjord winter plankton communities investigated the influences of increasing water temperatures of up to ΔT +6°C and different light intensities between 16 and 100% of natural incident light. In an additional microcosm experiment with a single algal species and the natural bacterial community, two inorganic nutrient concentrations were used, in order to evaluate the combined effects of temperature and substrate on the algal-bacterial coupling. Summarising the results from all experiments it can be concluded, that increasing temperatures generally led to an increased heterotrophic bacterial organic substrate utilisation relative to primary production. In combination with a further brightening, the supplemental promotion of primary production would increase the absolute amounts of cycled organic matter. Future increasing P-limitation in coastal waters would lead not only to an enhanced absolute amount of cycled carbon, but additionally to an increased relative amount of remineralised organic carbon through the microbial loop. An enhanced organic matter transfer through the microbial loop has the potential to alter the whole structure and functioning of the marine food web and the biological sequestration of carbon to depth. Additionally, a substantial rise of CO2 emissions through enhanced respiration represents a positive feedback loop to the global climate change problem.
The sectoral drivers of economic growth: a long-term view of Latin American economic perfomance

This paper explores the long-term relationship between economic growth and the allocation of resources among sectors for a panel of 18 Latin American countries over the period 19502006. Using as high a level of disaggregation as the data allows, we use dynamic panel data analysis to calculate the elasticities of sectoral growth to overall output. This captures the strength of the linkages between sectors and gives some indication of which sectors can be considered to be ‘drivers of growth’. While it is traditionally believed that the manufacturing sector is the ‘engine of growth’, empirical results show that with the rise of the so-called ‘new economy’, a more nuanced reply is required, and that other sectors can also serve as catalysts for faster growth. In particular, we find support for the proposition that certain groups of services can also play the role of ‘leading sectors’. Pointedly, however, the results show a consistently low or negligible relationship between primary resource sectors and economic growth. These results are put in perspective of the debates on ‘deindustrialisation’ and ‘premature de-industrialisation’. The causes and the implications of this process for policies to enhance long-term growth and technological acquisition are discussed.
Identiﬁcation, prevalence, and genetic diversity analysis of pathogens causing black pepper anthracnose in Hainan, China

The fungal species in Colletotrichum genus are important pathogens of black pepper (Piper nigrum) to cause dieback of leaves and branches. The black pepper anthracnose has been observed frequently in Hainan, which is the main production area of black pepper in China. Thirty-six isolates from diseased leaves were collected through diﬀerent ﬁelds located in six main production areas of Hainan. Twenty-three isolates were found related to black pepper anthracnose and were identiﬁed as Colletotrichum spp. through morphological characteristics and ITS information via BLAST search. All isolates collected in this study belonged to Colletotrichumgloeosporioides complex. Six genes including ITS, TUB2, CHS-1, ACT, GAPDH and ApMat were ampliﬁed and sequenced. C. siamense,C. fructicola, C. arecicola, and C. kahawae were identiﬁed as the main pathogens based on the multi-gene phylogenetic analysis and morphological characters. This is the ﬁrst report of these four isolates associated with black pepper anthracnose in Hainan, China.
PrivLBS : Local Differential Privacy for Location-Based Services with Staircase Randomized Response

Location-based services (LBS) have been significantly developed and widely deployed in mobile devices. It is also well-known that LBS applications may result in severe privacy concerns by collecting sensitive locations. A strong privacy model “local differential privacy” (LDP) has been recently deployed in many different applications (e.g., Google RAPPOR, iOS, and Microsoft Telemetry) but not effective for LBS applications due to the low utility of existing LDP mechanisms. To address such deficiency, we propose the first LDP framework for a variety of location-based services (namely “ PrivLBS ”), which privately collects and analyzes user locations with high utility. Specifically, we design a novel randomization mechanism “Staircase Randomized Response” ( SRR ) and extend the empirical estimation to significantly boost the utility for PrivLBS in different LBS applications (e.g., traffic density estimation, and k nearest POIs). We have conducted extensive experiments on four real LBS datasets by benchmarking with other LDP schemes in practical applications. The experimental results demonstrate that PrivLBS significantly outperforms them.
Self-Normalized Off-Policy Estimators for Ranking

We propose two new estimators for off-policy evaluation of ranking policies, based on the idea of self-normalization. Importantly, these estimators are parameter-free and asymptotically unbiased. Experiments with synthetic data demonstrate that our estimators can be more accurate than other importance weighting estimators, owing to their ability to control variance, while adding minimal bias. From this, we conclude that self-normalization offers an optimal balance of accuracy and practicality for off-policy ranker evaluation.