Miracle-dz/UniFAR-FTUs huggingface dataset:
A Design Space for Memory Augmentation Technologies

The pervasive nature of display technologies can enable novel ever-accessible memory aids to address deterioration caused by ageing and cognitive decline. To date, however, memory has largely been treated as a single-unit, and there has been little formal consideration of how to select the most appropriate technology for a given intervention. We build on existing domain knowledge from neuroscience and psychology to suggest a novel design space with two axes: processing level, and display modality. In particular, we consider how augmentations might intervene at a biological, cognitive or meta-cognitive level using head-mounted (private) displays, small-scale (personal) displays, larger public and semi-public displays, and with technology that bypasses the visual channels entirely (e.g. through neural stimulation or non-visual senses). We then provide examples of potential studies to explore these design areas, and discuss future directions this approach to memory augmentation may take. Consideration is also given to the ethics of memory augmentation.
The Effectiveness of Job-Retention Schemes: COVID-19 Evidence From the German States

Kurzarbeit (KA), Germany’s short-time work program, is widely credited with saving jobs and supporting domestic demand during the COVID-19 recession. We quantify the impact by exploiting state-level variation in exposure to the pandemic shock and KA take-up. We construct a shift-share measure of the labor demand shock and instrument KA take-up using the pre-existing, state-specific share of workers eligible for KA. We find, first, that KA was crucial in mitigating unemployment: absent its expansion the unemployment rate would have increased by an additional 3 pp on average at the trough of the recession. Second, KA also bolstered domestic demand: the contraction in consumption could have been 2 to 3 times larger absent the program. Finally, we provide preliminary evidence on the sensitivity of the medium-run reallocation of resources to the prevalence of jobretention schemes during the Global Financial Crisis. Keywords: Kurzarbeit, Short-time work, Unemployment, COVID-19, KA take-up, job-retention scheme, labor demand shock, evidence from the German States, employment growth, Labor demand, Employment, Labor markets, Unemployment, COVID-19, Global
Modulating Optical Characteristics of Nanoimprinted Plasmonic Device by Re-Shaping Process of Polymer Mold

Metal nanostructures exhibit specific optical characteristics owing to their localized surface plasmon resonance (LSPR) and have been studied for applications in various optical devices. The LSPR property strongly depends on the size and shape of metal nanostructures; thus, plasmonic devices must be designed and fabricated according to their uses. Nanoimprint lithography (NIL) is an effective process for repeatedly fabricating metal nanostructures with controlled sizes and shapes and require optical properties. NIL is a powerful method for mass-producible, low-cost, and large-area fabrication. However, the process lacks flexibility in adjusting the size and shape according to the desirable optical characteristics because the size and shape of metal nanostructures are determined by a single corresponding mold. Here, we conducted a re-shaping process through the air-plasma etching of a polymer’s secondary mold (two-dimensional nanopillar array made of cyclo-olefin polymer (COP)) to modulate the sizes and shapes of nanopillars; then, we controlled the spectral characteristics of the imprinted plasmonic devices. The relationship between the structural change of the mold, which was based on etching time, and the optical characteristics of the corresponding plasmonic device was evaluated through experiments and simulations. According to evaluation results, the diameter of the nanopillar was controlled from 248 to 139 nm due to the etching time and formation of a pit structure. Consequently, the spectral properties changed, and responsivity to the surrounding dielectric environment was improved. Therefore, plasmonic devices based on the re-shaped COP mold exhibited a high responsivity to a refractive index of 906 nm/RIU at a wavelength of 625 nm.
Revising Conceptual Similarity by Neural Networks

: Similarity is an excellent example of a domain-general source of information. Even when we do not have speciﬁc knowledge of a domain, we can use similarity as a default method to reason about it. Similarity also plays a signiﬁcant role in psychological accounts of problem solving, memory, prediction, and categorisation. However, despite the strong presence of similarity judgments in our reasoning, a general conceptual model of similarity has yet to be agreed upon. In this paper, we propose an alternative, unifying solution in this challenge in concept research based on the recent Eliasmith’s theory of biological cognition. Speciﬁcally we introduce the Semantic Pointer Model of Similarity (SPMS) which describes concepts in terms of processes involving a recently postulated class of mental representations called semantic pointers . We discuss how such model is in accordance with the main guidelines of most traditional models known in literature, on the one hand, and gives a solution to most of the criticisms against these models, on the other. We also present some preliminary experimental evaluation in order to support our theory and verify whether similarities derived by human judgments can be compatible with the SPMS.
Securing Big Data Scientific Workflows via Trusted Heterogeneous Environments

Big data workflow management systems (BDWMS)s have recently emerged as popular data analytics platforms to conduct large-scale data analytics in the cloud. However, the protection of data confidentiality and secure execution of workflow applications remains an important and challenging problem. Although a few data analytics systems, such as VC3 and Opaque, were developed to address security problems, they are limited to specific domains such as Map-Reduce-style and SQL query workflows. A generic secure framework for BDWMSs is still missing. In this article, we propose SecDATAVIEW, a distributed BDWMS that employs heterogeneous workers, such as Intel SGX and AMD SEV, to protect both workflow and workflow data execution, addressing three major security challenges: (1) Reducing the TCB size of the big data workflow management system in the untrusted cloud by leveraging the hardware-assisted TEE and software attestation; (2) Supporting Java-written workflow tasks to overcome the limitation of SGX’s lack of support for Java programs; and (3) Reducing the adverse impact of SGX enclave memory paging overhead through a “Hybrid” workflow task scheduling system that selectively deploys sensitive tasks to a mix of SGX and SEV worker nodes. Our experimental results show that SecDATAVIEW imposes moderate overhead on the workflow execution time.
Process Model for Digital Transformation of University Knowledge Transfer

: The digital transformation is still a volatile concept with different understandings between researchers and practitioners. Nevertheless, digital technologies and concepts are finding their way into all areas of life, be it private or professional life. Even universities are not spared from digital transformation. They need to incorporate innovations not only within their curricula, but also in their inner structures and administration to ensure up to date research and transfer. Therefore, a structured model for the digital transformation of transfer in universities was created. The main purpose of this paper is to provide practical support and break down barriers in the digital transformation of knowledge transfer in and out of universities.
DNA Methylation Patterns Differ between Free-Living Rhizobium leguminosarum RCAM1026 and Bacteroids Formed in Symbiosis with Pea (Pisum sativum L.)

Rhizobium leguminosarum (Rl) is a common name for several genospecies of rhizobia able to form nitrogen-fixing nodules on the roots of pea (Pisum sativum L.) and undergo terminal differentiation into a symbiotic form called bacteroids. In this work, we compared the genomes of the free-living and differentiated forms of the Rl strain RCAM1026 using Oxford Nanopore long reads. No significant genome rearrangements were observed, but the relative abundances of replicons were different between the cell states. GANTC, GGCGCC and GATC methylated motifs have been found in the genome, along with genes for methyltransferases with matching predicted targets. Methylation patterns for the GANTC and GATC motives differed significantly depending on the cell state, which indicates their possible connection to the regulation of symbiotic differentiation. The GGCGCC motif was completely methylated in both bacteria states, and, apparently, is a target for the modification-restriction system. Currently, the methylation patterns in symbiotic bacteria are not extensively studied, so a further investigation of the topic coupled with gene expression data is needed to elucidate the function of differential methylation in terminal differentiation of R. leguminosarum and other rhizobia.
“Shadowbanning is not a thing”: black box gaslighting and the power to independently know and credibly critique algorithms

ABSTRACT Efforts to govern algorithms have centerd the ‘black box problem,’ or the opacity of algorithms resulting from corporate secrecy and technical complexity. In this article, I conceptualize a related and equally fundamental challenge for governance efforts: black box gaslighting. Black box gaslighting captures how platforms may leverage perceptions of their epistemic authority on their algorithms to undermine users’ confidence in what they know about algorithms and destabilize credible criticism. I explicate the concept of black box gaslighting through a case study of the ‘shadowbanning’ dispute within the Instagram influencer community, drawing on interviews with influencers (n = 17) and online discourse materials (e.g., social media posts, blog posts, videos, etc.). I argue that black box gaslighting presents a formidable deterrent for those seeking accountability: an epistemic contest over the legitimacy of critiques in which platforms hold the upper hand. At the same time, I suggest we must be mindful of the partial nature of platforms’ claim to ‘the truth,’ as well as the value of user understandings of algorithms.
Journal of WSCG, Vol.27, No.1-2, Cummulative edition

The procedural generation of data sets for empirical algorithm validation and deep learning tasks in the area of primitive-based geometry is cumbersome and time-consuming while ready-to-use data sets are rare. We propose a new and highly flexible framework based on Evolutionary Computing that is able to create primitive-based abstractions of existing triangle meshes favoring fast running times and high geometric variation over reconstruction precision. These abstractions are represented as CSG trees to widen the scope of possible applications. As part of the evaluation, we show how we successfully used the generator to create a data set for the evaluation of neural point cloud segmentation pipelines and additionally explain how to use the system to create artistic abstractions of meshes provided by publicly available triangle mesh databases.
Optimal Scheduling for Flying Taxi Operation

: According to the traffic congestion problem in big cities around the world, some sustainable transportation technologies are researched and developed in these recent years. Flying taxi is a transportation mode, which is being developed from several major brands. It can become an alternative transportation mode in the future. In this work, a simplified optimization model of the flying taxi scheduling is proposed. Two algorithms, which consists of genetic algorithm and simulated annealing, are used to solve the problem. The experiments are conducted on 15 instances with different number of customer demands (between 10 and 200 demands) and different number of available flying taxis in the system (from 2 to 10 taxis). The experimental results show that both algorithms are efficient to solve the problem. The genetic algorithm obtains better quality solutions for the small and medium size instances but it spends more computation time than the simulated annealing. However, the simulated annealing can solve the large instances and obtain good solutions in reasonable time.
SAP-Net: A Simple and Robust 3D Point Cloud Registration Network Based on Local Shape Features

Point cloud registration is a key step in the reconstruction of 3D data models. The traditional ICP registration algorithm depends on the initial position of the point cloud. Otherwise, it may get trapped into local optima. In addition, the registration method based on the feature learning of PointNet cannot directly or effectively extract local features. To solve these two problems, this paper proposes SAP-Net, inspired by CorsNet and PointNet++, as an optimized CorsNet. To be more specific, SAP-Net firstly uses the set abstraction layer in PointNet++ as the feature extraction layer and then combines the global features with the initial template point cloud. Finally, PointNet is used as the transform prediction layer to obtain the six parameters required for point cloud registration directly, namely the rotation matrix and the translation vector. Experiments on the ModelNet40 dataset and real data show that SAP-Net not only outperforms ICP and CorsNet on both seen and unseen categories of the point cloud but also has stronger robustness.
IUScholarWorks at Indiana University South Bend Exploring Determinants of U.S. Household Debt

The fast growing household debt in the U.S. has become a concern to the general public and policy makers. This paper attempts to explore the factors influencing the U.S. household indebtedness using quarterly data over the period of 1980-2010 and controlling for the time series issues. The estimated results show that the unemployment rate, interest rate, disposable personal income per capita, share of retiring population, and educational attainment are negatively associated with the household debt, while housing prices, consumer confidence, and the share of working-age population are positively related to the household borrowing.
A new genus of anaxyelid wood wasps from the mid-Cretaceous and the phylogeny of Anaxyelidae (Hymenoptera)

Two new species of wood wasps (Anaxyelidae), Orthosyntexis elegansgen. et sp. nov. and Orthosyntexis thantisp. nov., are described from mid-Cretaceous Kachin amber. Orthosyntexisgen. nov. exhibits characters and character combinations unique to the family, such as the combination of two mesotibial spurs, a length ratio of forewing 1Rs to 1M<1, a length to width ratio of forewing cell 1mcu ≥ 2, and the presence of 2M+Cu in the hind wing. The new species and morphological characters allow for an exploration of anaxyelid phylogeny. The phylogenetic results indicate that Anaxyelidae are monophyletic and it is suggested to classify the genera in two subfamilies, Syntexinae and Anaxyelinae, the latter including Kempendajinae and Dolichostigmatinae.
Leveraged Buyouts

This study comprehensively reviews theoretical and empirical literature pertaining to leveraged buyouts. An agency theory framework best descr·ibes the source of LBO value creation. Agency conflicts are mitigated through extensive utilisation of debt capital and concentrated equity ownership, which are functions of asset and organisation structures. The evidence generally supports the hypothesis that economic wealth is created by leveraged buyouts, rather than merely redistributed among stakeholders. This thesis uses a multiple case design to examine leveraged buyouts in Australia. It compiles data from a broad range of public and private sources, and conducts qualitative and quantitative analysis on six (6) leveraged buyouts. Case re~ults indicate that industry and business attributes synonymous with US and UK buyouts are important determinants of Austral.i. an leveraged buyouts. Business attributes ar·e the primary motivating farces. Ownership structures comply with foreign expectations, and capital structures are more closely aligned with those reported in UK research. Industry adjusted performance was analysed for a subset of three (3) leveraged buyouts with post-buyout periods of sufficient duration. Profit margins and capital utilisation exceeded industry medians in each post-buyout year, for each
Edge

This chapter presents three films that deal with homelessness and migration: Qu’ils reposent en révolte (Sylvain George, 2011) about migrants in Calais; Au bord du monde/On the Edge of the World (Claus Drexel, 2014) on homeless people in central Paris; and La Permanence/On Call (Alice Diop, 2016), which depicts a free medical clinic in Bobigny. In all of these films, the protagonists live at the metaphorical edge of society, and some walk other edges as well, between life and death, between physical and mental illness and health. These three directors explore what happens at these edges, far outside the realm of direct experience for most documentary film viewers. They follow characters to physical and geographical edges—national borders, riverbanks, beaches, and roadsides—and invite viewers to tiptoe up to those edges and feel the danger they pose. The films create film spaces, if ephemeral ones, where these rootless individuals do belong, where they can emerge as individuals, and where there is a potential for experiential and ecological connection with the viewer.
Social Comparison in Multi-Task Environments – Sacrificing Overall Performance for Local Excellence?

Using a field experiment with a German retail chain, we will provide evidence on how different aggregation levels of relative performance information (RPI) affect effort allocation in multitask environments. Employees have two separate tasks which make up their overall performance. We will randomly assign store employees to three different groups: (i) the control group which receives only RPI on the overall performance of both tasks, (ii) a treatment group which receives RPI on both tasks separately, but not overall performance, and (iii) a treatment group which receives RPI on overall performance and separate tasks. We expect that employees who only receive RPI on both separate tasks will focus on one task while neglecting the other, even though this might be detrimental to overall performance (effort distortion effect). However, presented with RPI on overall performance and separate tasks we expect that employees will focus on overall performance and shift their effort on tasks with a higher contribution to overall performance (decision facilitating effect).
Multi-Target Chemometric Modelling, Fragment Analysis and Virtual Screening with ERK Inhibitors as Potential Anticancer Agents

Two isoforms of extracellular regulated kinase (ERK), namely ERK-1 and ERK-2, are associated with several cellular processes, the aberration of which leads to cancer. The ERK-1/2 inhibitors are thus considered as potential agents for cancer therapy. Multitarget quantitative structure–activity relationship (mt-QSAR) models based on the Box–Jenkins approach were developed with a dataset containing 6400 ERK inhibitors assayed under different experimental conditions. The first mt-QSAR linear model was built with linear discriminant analysis (LDA) and provided information regarding the structural requirements for better activity. This linear model was also utilised for a fragment analysis to estimate the contributions of ring fragments towards ERK inhibition. Then, the random forest (RF) technique was employed to produce highly predictive non-linear mt-QSAR models, which were used for screening the Asinex kinase library and identify the most potential virtual hits. The fragment analysis results justified the selection of the hits retrieved through such virtual screening. The latter were subsequently subjected to molecular docking and molecular dynamics simulations to understand their possible interactions with ERK enzymes. The present work, which utilises in-silico techniques such as multitarget chemometric modelling, fragment analysis, virtual screening, molecular docking and dynamics, may provide important guidelines to facilitate the discovery of novel ERK inhibitors.
Low statistical power and overestimated anthropogenic impacts, exacerbated by publication bias, dominate field studies in global change biology

Field studies are essential to reliably quantify ecological responses to global change because they are exposed to realistic climate manipulations. Yet such studies are limited in replicates, resulting in less power and, therefore, potentially unreliable effect estimates. Furthermore, while manipulative field experiments are assumed to be more powerful than non‐manipulative observations, it has rarely been scrutinized using extensive data. Here, using 3847 field experiments that were designed to estimate the effect of environmental stressors on ecosystems, we systematically quantified their statistical power and magnitude (Type M) and sign (Type S) errors. Our investigations focused upon the reliability of field experiments to assess the effect of stressors on both ecosystem's response magnitude and variability. When controlling for publication bias, single experiments were underpowered to detect response magnitude (median power: 18%–38% depending on effect sizes). Single experiments also had much lower power to detect response variability (6%–12% depending on effect sizes) than response magnitude. Such underpowered studies could exaggerate estimates of response magnitude by 2–3 times (Type M errors) and variability by 4–10 times. Type S errors were comparatively rare. These observations indicate that low power, coupled with publication bias, inflates the estimates of anthropogenic impacts. Importantly, we found that meta‐analyses largely mitigated the issues of low power and exaggerated effect size estimates. Rather surprisingly, manipulative experiments and non‐manipulative observations had very similar results in terms of their power, Type M and S errors. Therefore, the previous assumption about the superiority of manipulative experiments in terms of power is overstated. These results call for highly powered field studies to reliably inform theory building and policymaking, via more collaboration and team science, and large‐scale ecosystem facilities. Future studies also require transparent reporting and open science practices to approach reproducible and reliable empirical work and evidence synthesis.
Bioprospection of Enggano Macroscopic Fungi as Antibacterial and Antioxidant Agents

Macrofungi in Indonesia have not been widely studied for their pharmacological activity, especially as a source of antibacterial and antioxidant properties, even though Indonesia as a tropical country has quite a high diversity of macrofungi. This study aims to reveal the potential of macrofungi from the Enggano forest as a source of antibacterial and antioxidant compounds. Four types of macrofungi were collected and their metabolites were extracted using four types of organic solvents. Antibacterial and antioxidant activity assay of the extract was carried out using the TLC Bioautography method. From the sixteen macrofungal extracts, there is one extract that has the strongest antibacterial activity compared to the others, namely n-hexane Coriolopsis polyzona. It showed moderate antibacterial activity against Staphylococcus aureus and Escherichia coli with MIC values of 256–128 μg/mL, respectively. Meanwhile, the antioxidant activity of the macrofungal extracts showed weak activity with IC50 values of 3080–7370 μg/mL (AAI values of 0.033–0.079).