Miracle-dz/UniFAR-FTUs huggingface dataset:
Education and the Pandemic: Engaging in Epistemic Humility to Question Assumptions, Institutions, and Knowledges

Education systems are the formal institutionalisations of the knowledges and values our societies privilege, who they privilege, how, and on what terms. They are imbued with assumptions. These assumptions inform how systems are structured. Assumptions frame collective and individual interactions within education systems, which ultimately determine how individuals, and particular groups of individuals, are inserted therein. The pandemic has exposed existing global and local inequalities, non-binary dynamics of inclusion and exclusion, and dysfunctions of education systems. What has been revealed is as applicable to education systems that were widely assumed to be ‘educationally secure’ prior to the pandemic – usually signified as those of the ‘West’ – as it is to systems that were largely characterised as precarious or dysfunctional. The paper argues that the global scale and severity of the education disruption challenges taken-for-granted distinctions that privilege systems of the ‘West’ as referential for ‘the Rest’. It argues that the existing overarching technicist knowledge regime is inadequate for recovery, and proposes an alternative approach. 1 The author can be contacted at prachi.srivastava@uwo.ca May 2022 EDUCATION AND THE PANDEMIC: Engaging in epistemic humility to question assumptions, institutions, and knowledges EDUCATION RESEARCH AND FORESIGHT
Large-scale brain-wide neural recording in nonhuman primates

High-density, integrated silicon electrodes have begun to transform systems neuroscience, by enabling large-scale neural population recordings with single cell resolution. Existing technologies, however, have provided limited functionality in nonhuman primate species such as macaques, which oﬀer close models of human cognition and behavior. Here, we report the design, fabrication, and performance of Neuropixels 1.0-NHP, a high channel count linear electrode array designed to enable large-scale simultaneous recording in superﬁcial and deep structures within the macaque or other large animal brain. These devices were fabricated in two versions: 4416 electrodes along a 45 mm shank, and 2496 along a 25 mm shank. For both versions, users can programmably select 384 channels, enabling simultaneous multi-area recording with a single probe. We demonstrate recording from over 3000 single neurons within a session, and simultaneous recordings from over 1000 neurons using multiple probes. This technology represents a signiﬁcant increase in recording access and scalability relative to existing technologies, and enables new classes of experiments involving ﬁne-grained electrophysiological characterization of brain areas, functional connectivity between cells, and simultaneous brain-wide recording at scale.
Primal-Optimal-Binding LPNet: Deep Learning Architecture to Predict Optimal Binding Constraints of a Linear Programming Problem

—Identifying an optimal basis for a linear program- ming problem is a challenging learning task. Traditionally, an optimal basis is obtained via the iterative simplex method which improves from the current basic feasible solution to the adjacent one until it reaches optimal. The obtained result is the value of the optimal solution and the corresponding optimal basis. Even though learning the optimal value is hard but learning the optimal basis is possible via deep learning. This paper presents the primal-optimal-binding LPNet that learns from massive linear programming problems of various sizes casting as all-unit-row-except-first-unit-column matrices. During the training step, these matrices are fed to the special row-column convolutional layer followed by the state-of-the-art deep learning architecture and sent to two fully connected layers. The result is the probability vector of non-negativity constraints and the original linear programming constraints at the optimal basis. The experiment shows that this LPNet achieves 99% accuracy of predicting a single binding optimal constraint on unseen test problems and Netlib problems. It identifies correctly 80% LP problems having all optimal binding constraints and faster than cplex solution time.
Market integration and price volatility of green gram in Maharashtra

Green gram is one of the important pulse crops in India. It has been reported that Green gram has been cultivated in India since ancient times. The present study aimed to know Market integration of Green gram i.e. seasonal variation, price volatility and co-integration among the major Green gram markets in Maharashtra. For study purpose the data related to monthly average prices of Green gram were collected from major markets of Maharashtra state viz. Buldana, Akola, Amravati and Latur markets for the period 2005 to 2016. Moving average method was used to study seasonal variations. The econometric tools like ADF test, Johansen's multiple co-integration tests, Granger causality test and ARCH-GARCH model were used to study price volatility and co-integration among different markets. The results of study showed that the prices of Green gram were higher in the months from December to June in all the selected markets. The cyclical variation observed in the prices of Green gram in the selected markets. For all selected markets, the price series showed the consequences of unit root and were stationary at first difference. The selected markets showed long run equilibrium relationship and co-integration between them. Most of the markets showed bidirectional influence on Green gram prices of each other. Buldana, Akola, Amravati and Latur market, recorded price volatility in Green gram prices.
Indicators of Global Climate Change 2022: annual update of large-scale indicators of the state of the climate system and human influence

Abstract. Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of scientific evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC), including the first global stocktake under the Paris Agreement that will conclude at COP28 in December 2023. Evidence-based decision-making needs to be informed by up-to-date and timely information on key indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5–10 years, creating potential for an information gap between report cycles. We follow methods as close as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report. We compile monitoring datasets to produce estimates for key climate indicators related to forcing of the climate system: emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, surface temperature changes, the Earth's energy imbalance, warming attributed to human activities, the remaining carbon budget, and estimates of global temperature extremes. The purpose of this effort, grounded in an open data, open science approach, is to make annually updated reliable global climate indicators available in the public domain (https://doi.org/10.5281/zenodo.8000192, Smith et al., 2023a). As they are traceable to IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its direction of travel. The indicators show that human-induced warming reached 1.14 [0.9 to 1.4] ∘C averaged over the 2013–2022 decade and 1.26 [1.0 to 1.6] ∘C in 2022. Over the 2013–2022 period, human-induced warming has been increasing at an unprecedented rate of over 0.2 ∘C per decade. This high rate of warming is caused by a combination of greenhouse gas emissions being at an all-time high of 54 ± 5.3 GtCO2e over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that increases in greenhouse gas emissions have slowed, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track a change of direction for human influence on climate.
Differentially Private Aggregation via Imperfect Shuffling

In this paper, we introduce the imperfect shuffle differential privacy model, where messages sent from users are shuffled in an almost uniform manner before being observed by a curator for private aggregation. We then consider the private summation problem. We show that the standard split-and-mix protocol by Ishai et. al. [FOCS 2006] can be adapted to achieve near-optimal utility bounds in the imperfect shuffle model. Specifically, we show that surprisingly, there is no additional error overhead necessary in the imperfect shuffle model.
How to Recover a Secret with O ( n ) Additions ∗

Threshold cryptography is typically based on the idea of secret-sharing a private-key s ∈ F “in the exponent” of some cryptographic group G , or more generally, encoding s in some linearly homomorphic domain. In each invocation of the threshold system (e.g., for signing or decrypting) an “encoding” of the secret is being recovered and so the complexity, measured as the number of group multiplications over G , is equal to the number of F -additions that are needed to reconstruct the secret. Motivated by this scenario, we initiate the study of n -party secret-sharing schemes whose reconstruction algorithm makes a minimal number of additions . The complexity of existing schemes either scales linearly with n log | F | (e.g., Shamir, CACM’79) or, at least, quadratically with n independently of the size of the domain F (e.g., Cramer-Xing, EUROCRYPT ’20). This leaves open the existence of a secret sharing whose recovery algorithm can be computed by performing only O ( n ) additions. We resolve the question in the afﬁrmative and present such a near-threshold secret sharing scheme that provides privacy against unauthorized sets of density at most τ p , and correctness for authorized sets of density at least τ c , for any given arbitrarily close constants τ p < τ c . Re-construction can be computed by making at most O ( n ) additions and, in addition, (1) the share size is constant, (2) the sharing procedure also makes only O ( n ) additions, and (3) the scheme is a blackbox secret-sharing scheme, i.e., the sharing and reconstruction algorithms work universally for all ﬁnite abelian groups F . Prior to our work, no such scheme was known even without features (1)–(3) and even for the ramp setting where τ p and τ c are far apart. As a by-product, we derive the ﬁrst blackbox near-threshold secret-sharing scheme with linear-time sharing. We also present several concrete instantiations of our approach that seem practically efﬁcient
A Consumer Product of Wi-Fi Tracker System using RSSI-based Distance for Indoor Crowd Monitoring

—This study aims to design and develop Wi-Fi tracker system that utilizes RSSI-based distance parameters for crowd-monitoring applications in indoor settings. The system consists of three main components, namely 1) an embedded node that runs on Raspberry-pi Zero W, 2) a real-time localization algorithm, and 3) a server system with an online dashboard. The embedded node scans and collects relevant information from Wi-Fi-connected smartphones, such as MAC data, RSSI, timestamps, etc. These data are then transmitted to the server system, where the localization algorithm passively determines the location of devices as long as Wi-Fi is enabled. The mentioned devices are smartphones, tablets, laptops, while the algorithm used is a Non-Linear System with Lavenberg–Marquart and Unscented Kalman Filter (UKF). The server and online dashboard (web-based application) have three functions, including displaying and recording device localization results, setting parameters, and visualizing analyzed data. The node hardware was designed for minimum size and portability, resulting in a consumer electronics product outlook. The system demonstration in this study was conducted to validate its functionality and performance.
DuckPGQ: Efficient Property Graph Queries in an analytical RDBMS

In the past decade, property graph databases have emerged as a growing niche in data management. Many native graph systems and query languages have been created, but the functionality and performance still leave much room for improvement. The upcoming SQL:2023 will introduce the Property Graph Queries (SQL/PGQ) sub-language, giving relational systems the opportunity to standard-ize graph queries, and provide mature graph query functionality. We argue that (i) competent graph data systems must build on all technology that makes up a state-of-the-art relational system, (ii) the graph use case requires the addition to that of a many-source/destination path-finding algorithm and compact graph rep-resentation, and (iii) incites research in practical worst-case-optimal joins and factorized query processing techniques. We outline our design of DuckPGQ that follows this recipe, by adding efficient SQL/PGQ support to the popular open-source “embeddable analytics” relational database system DuckDB, also originally developed at CWI. Our design aims at minimizing techni-cal debt using an approach that relies on efficient vectorized UDFs. We benchmark DuckPGQ showing encouraging performance and scalability on large graph data sets, but also reinforcing the need for future research under (iii).
Genetic diversity, heritability, correlation coefficient, and path analysis of forage yield components in Iranian Phalaris aquatica L. genotypes

Recommended Citation MOHAMMADI, REZA; POURMOHAMMAD, ALIREZA; HASSANPOURAGHDAM, MOHAMMAD BAGHER; and DİLER, SERAP (2023) "Genetic diversity, heritability, correlation coefficient, and path analysis of forage yield components in Iranian Phalaris aquatica L. genotypes," Turkish Journal of Agriculture and Forestry: Vol. 47: No. 3, Article 6. https://doi.org/10.55730/1300-011X.3090 Available at: https://journals.tubitak. gov.tr/agriculture/vol47/iss3/6
Kim, M, Lee, CK and Jung, Timothy ORCID logoORCID: https://orcid.org/0000-0002-8594-6641 (2019) Exploring Consumer Behavior in Virtual Reality Tourism Using an Extended Stimulus-Organism-Response

Although virtual reality (VR) is an emerging technology in tourism, little research has conducted on what factors make consumers visit locations presented by VR. To address this gap in the literature, this study developed a theoretical framework including authentic experience, cognitive and affective responses, attachment, and visit intention with VR tourism using a stimulus-organism-response (SOR) theory. The results revealed significant impacts of authentic experience on cognitive and affective responses, indicating that authentic experience is an important factor in VR tourism. The study identified cognitive and affective responses as significant mediators in predicting attachment and visit intention. The results demonstrated that the intention to visit places shown in VR tourism was influenced by attachment to VR. Cognitive response was a stronger influence than affective response on the intention to visit a destination in VR. This study sheds light on why potential tourists visit destinations shown in VR.
36-h Total Sleep Deprivation Induced Aberrant Insula Sub-regional Functional Connectivity

Total sleep deprivation (TSD) induces aberrant insula functional connectivity (FC). The insula comprises at least three functionally distinct subregions: anterior dorsal (dAI), anterior ventral (vAI), and posterior insula (PI). Biased attention toward the anterior insula (AI) has limited our complete understanding of the TSD impact. We aimed to investigate TSD-induced functional connectivity and lateralization across the insula subregions. A total of 54 healthy young men completed 36-h TSD. Two sessions of psychomotor vigilance task (PVT) and 3T resting-state functional magnetic resonance imaging (rs-fMRI) scanning were carried out. A seed-based FC was conducted using bilateral insula subregions. Impaired vigilance, altered functional connectivity and lateralization were observed after TSD. The AI showed enhanced connectivity with the cerebellum, middle frontal gyrus, putamen, and postcentral gyrus but decreased connectivity with the temporal lobes, angular gyrus, calcarine sulcus, anterior cingulum, and medial orbitofrontal gyrus. The PI had increased connectivity with the middle frontal, inferior temporal, and inferior parietal gyrus but showed an anti-correlation with the middle temporal regions, posterior cingulum, and angular gyrus. All the seeds showed ipsilateral connections with specific brain regions, excluding the cerebellum. Both the vAI and PI displayed FC with the insula. The correlation analysis between PVT and brain signal changes did not survive Bonferroni correction. This study provided information about potential functional asymmetries of insula subregions caused by 36-h TSD. These findings provided new insights into the neural mechanisms of inter-hemispheric communication and coordination, which is essential for understanding the overall brain function. Keywords: rs-fMRI, insular subregions, PVT, sleep deprivation, functional connectivity
A participatory approach to designing and implementing an occupational health intervention for the nail salon community in the Greater Philadelphia region

Background: The nail salon industry in the US comprises mostly immigrant-owned, small mom-and-pop salons that employ primarily first-generation immigrant workers from Asia. Because of the cultural and language barriers, both owners and workers may not avail themselves of the occupational safety resources. We formed an academic-community partnership to co-design a feasibility study and multi-level occupational health intervention for Vietnamese-speaking salon owners, workers, and community-based organization. Methods: The intervention for each salon included 1) two-hour in-person training covering chemical safety, infection control, musculoskeletal prevention, and workers rights for both the owners and their employees, 2) a tailored recommendation report for the owner, and 3) check-ins with the owners during the three-month follow-up. Community partner was trained to deliver the in-language training with technical assistance from the research team. Baseline and post-intervention individual data about health symptoms and behaviors, as well as personal chemical exposures were collected and analyzed. Results: A total of 44 participants from 12 consented salons enrolled in the study. One salon dropped out follow-up due to change of ownership. Analysis of the differences between post-and pre-intervention showed a tendency toward reduction in self-reported symptoms in the respiratory system, skin, and eyes, neurotoxicity score, as well as some chemical exposures. We could not rule out seasonality as an explanation for these trends. Increase in self-efficacy in some areas was observed post-intervention. Conclusions: Our study demonstrated a successful academic-community partnership to engage community members in the intervention study. While the intervention effects from feasibility study should be interpreted with caution, our preliminary results indicated that our community-based intervention is a promising approach to reduce work-related exposures among nail salon worker workers from Asia.
Feature matching algorithm based on rectification of epipolar-line region

In the process of 3D measurement and structure reconstruction from multi-views,the direct area-correlation method based on epipolar constraint works inefficiently to match targets.To solve this problem,a feature matching algorithm based on rectification of epipolar-line region is proposed.The theory of epipolar constraint is introduced,the correlation matching method and the corresponding feature property of image pair are analyzed,the algorithm is introduced.The algorithm rectifies the regions of the corresponding epipolar-lines which include the feature points to be matched through rotating.Matching by correlating on the rectified regions works automatically and gives accurately correspondence throuth least square image matching.Experiments with real images show that the algorithm improves the robustness and the accuracy of feature matching method under epipolar constraint.
AI-Accelerated Decision Making in Forest Management

This paper is a continuation of the research on the application of artificial intelligence in counting trees with the use of methods for the automatic analysis of photogrammetric data in forests of the continental region. This paper is devoted to the AI application in accelerating decision making processes in forest management. It also discusses how RGB imagery from drones could replace aerial and satellite hyperspectral imagery and automatically detect unhealthy and dead trees. Experimental research was conducted to verify whether Faster R-CNN can automatically detect and classify snag and trees weakened by diseases on aerial RGB data, enabling a quick response to forest-threatening factors. The research is based on photogrammetric data taken in areas of forest districts subordinate to the Regional Directorate of State Forests in Zielona Góra. Non-metric imagery data was collected from drones and small airplanes with a photogrammetric container and postprocessed with respect to the photogrammetric constraints. The results show that in specific cases aerial and satellite hyperspectral imagery can be replaced by RGB orthomosaics in order to decrease the time needed for forestry treatments.
Vertical Extension of Soil Moisture Data in Hungarian Soil Types with Different Interpolation Methods

An important step in today’s modern precision crop production systems is the development of an irrigation system for arable lands. Most of these systems only examine the top, 10 cm layer of the soil. The problem is that the root system of the crops also penetrates and grows into the deeper layers while absorbing water. For this reason, there is a need for a cost-effective solution to quantify the amount of water up to 1 meter depth. Sentek EnviroSCAN sensors were used to measure soil moisture (SM) values of different soil types located in Hungary having different soil mechanical and chemical attributes. SM values were only measured at certain depths. The missing depths are supplemented with estimated data done by interpolation. However, these estimations are not enough, as these sensors were located on different soil types, i.e. one kind of interpolation method may not be suitable to all sensors without significant statistical error. Several soil moisture sensors were deployed. Each of them is placed on a specific soil type. The measured SM depth values were then used to produce estimated SM values lasting from 10 to 100 cm by using 8 types of interpolation methods. The interpolation was performed using a Python framework called Scipy. The final result of the research is the evaluation of the model performance of the soil moisture data estimated by 8 types of interpolation methods for different soil types in Hungary.