Miracle-dz/UniFAR-FTUs huggingface dataset:
DBSCAN: Optimal Rates For Density-Based Cluster Estimation

We study the problem of optimal estimation of the density cluster tree under various assumptions on the underlying density. Building up from the seminal work of Chaudhuri et al. [2014], we formulate a new notion of clustering consistency which is better suited to smooth densities, and derive minimax rates of consistency for cluster tree estimation for Holder smooth densities of arbitrary degree \alpha. We present a computationally efficient, rate optimal cluster tree estimator based on a straightforward extension of the popular density-based clustering algorithm DBSCAN by Ester et al. [1996]. The procedure relies on a kernel density estimator with an appropriate choice of the kernel and bandwidth to produce a sequence of nested random geometric graphs whose connected components form a hierarchy of clusters. The resulting optimal rates for cluster tree estimation depend on the degree of smoothness of the underlying density and, interestingly, match minimax rates for density estimation under the supremum norm. Our results complement and extend the analysis of the DBSCAN algorithm in Sriperumbudur and Steinwart [2012]. Finally, we consider level set estimation and cluster consistency for densities with jump discontinuities, where the sizes of the jumps and the distance among clusters are allowed to vanish as the sample size increases. We demonstrate that our DBSCAN-based algorithm remains minimax rate optimal in this setting as well.
DIFFERENTIAL GLUCOSE AND LACTATE METABOLISM OF PERFUSED CULTURED ASTROCYTES ASSESSED WITH QUANTITATIVE ON- LINE MONITORING

Both glucose and lactate are important substrates in brain metabolism. Astrocytes are known to release lactate, and are suggested to have a feeding role for neurons. In this study we investigated glucose and lactate metabolism in primary astrocytic cultures, using a novel in vitro on-line monitoring unit. By placing cells in a specially designed flow-through cell holder, we measured glucose use and lactate uptake/release every minute, using an online flow injection analysis (FIA) system with biosensors for glucose and lactate. Primary astrocytic cultures were perfused with medium containing different concentrations of glucose, lactate or a combination of both energy substrates. We measured a glucose use of approximately 3.5 femtomoles/cell/minute, and found that glucose was the most preferred substrate. Furthermore, the present experiments suggest the existence of large glycogen stores in primary astrocytes, that are broken down and released as lactate. Although lactate is taken up from the medium, medium supplemented with lactate did not alter glucose metabolism, suggesting a separate lactate metabolic compartment in astrocytes.
Power Density Analysis by using Soft Computing Techniques for Microbial Fuel Cell

Microbial electrochemical technologies, i.e. microbial fuel cells (MFCs), are attracting attention due to their dual functions of energy generation and waste removal from wastewaters. Microorganisms use microbial metabolism to convert biochemical metabolic energy into electricity using different substrates. This study was conducted on single-cell microbial fuel cells using domestic wastewater as a substrate. A steady state condition has been observed for performance of microbial fuel cell at laboratory scale; during this period the maximum voltage, current and power obtained were 210 mV, 24mA and 34.97 mW respectively. The experimental results were analyzed to determine the power density using four soft-computing techniques, i.e., an artificial neural network (ANN), an M5P model tree, a random forest (RF) and the Gaussian process (two kernel puk (Pearson universal kernel) and rbf (radial basis function)). 70% of the data was used for training and the remaining 30% for validation. The input parameters used were day, time, voltage and current, while power density was an output parameter for all modeling approaches. Better results were achieved with the artificial neural network, random forest and M5P model tree than the Gaussian process. The artificial neural network technique gave good results with correlation coefficient (R), root-relative square error (RRSE), relative absolute error (RAE), and root mean square error (RMSE) of 1, 0.4482, 0.4377 and 0.0056, respectively.
Multi-Gradient Descent for Multi-Objective Recommender Systems

Recommender systems need to mirror the complexity of the environment they are applied in. The more we know about what might benefit the user, the more objectives the recommender system has. In addition there may be multiple stakeholders - sellers, buyers, shareholders - in addition to legal and ethical constraints. Simultaneously optimizing for a multitude of objectives, correlated and not correlated, having the same scale or not, has proven difficult so far. We introduce a stochastic multi-gradient descent approach to recommender systems (MGDRec) to solve this problem. We show that this exceeds state-of-the-art methods in traditional objective mixtures, like revenue and recall. Not only that, but through gradient normalization we can combine fundamentally different objectives, having diverse scales, into a single coherent framework. We show that uncorrelated objectives, like the proportion of quality products, can be improved alongside accuracy. Through the use of stochasticity, we avoid the pitfalls of calculating full gradients and provide a clear setting for its applicability.
HALucinator: Firmware Re-hosting Through Abstraction Layer Emulation

Given the increasing ubiquity of online embedded devices, analyzing their ﬁrmware is important to security, privacy, and safety. The tight coupling between hardware and ﬁrmware and the diversity found in embedded systems makes it hard to perform dynamic analysis on ﬁrmware. However, ﬁrmware developers regularly develop code using abstractions, such as Hardware Abstraction Layers (HALs), to simplify their job. We leverage such abstractions as the basis for the re-hosting and analysis of ﬁrmware. By providing high-level replacements for HAL functions (a process termed High-Level Emulation – HLE ), we decouple the hardware from the ﬁrmware. This approach works by ﬁrst locating the library functions in a ﬁrmware sample, through binary analysis, and then providing generic implementations of these functions in a full-system emulator. Wepresent these ideas in a prototype system, HALucinator, able to re-host ﬁrmware, and allow the virtual device to be used normally. First, we introduce extensions to existing library matching techniques that are needed to identify library functions in binary ﬁrmware, to reduce collisions, and for inferring additional function names. Next, we demonstrate the re-hosting process, through the use of simpliﬁed handlers and peripheral models , which make the process fast, ﬂexible, and portable between ﬁrmware samples
Economic and Mathematical Models for Assessing and Forecasting the Dynamics of Labour Potential of the Border Regions of Siberia and Far East

The article is dedicated to assessment and forecasting of Gross Regional Product, employment, labour force and unemployment by sectors of the economy in the border regions of Siberia and Far East until 2025. For this purpose, there are developed economic and mathematical models considering the parameters of social and economic development. The social component is based on systematic approach to forecasting employment depending on the anthropogenic burden index, which takes into account duration and standard of living, population literacy, crime rate, environmental status and other indicators of the border regions of Siberia. The economic component uses econometric analysis of the types of economic activity in the border regions of Siberia and Far East, as well as time series analysis to forecast employment both in the medium and short term. In the labour market part, the labour force is projected considering the socio-economic effect of hidden unemployment.
Green-to-Blue Triplet Fusion Upconversion Sensitized by Anisotropic CdSe Nanoplatelets

Green-to-blue photon upconversion bears great potential in photocatalytic applications. However, current hybrid inorganic-organic upconversion schemes utilizing spherical CdSe nanocrystals are often limited by energetic polydispersity, low quantum yields and an additional tunneling barrier resulting from the necessity of surface-passivating inorganic shells. In this contribution, we introduce anisotropic CdSe nanoplatelets as triplet sensitizers. Here, quantum confinement occurs in only one direction, erasing effects stemming from energetic polydispersity. We investigate the triplet energy transfer from the CdSe nanoplatelets to the surface-bound triplet acceptor 9-anthracene carboxylic acid. We further focus on the influence of nanoplatelet stacking and singlet back transfer on the observed upconversion efficiency. We obtain an upconversion quantum yield of 5.4% at a power density of 11 W/cm2­ using the annihilator 9,10-diphenylanthracene, and a low efficiency threshold Ith of 237 mW/cm2.
Morphometric analysis of bone in the ovariectomized rat using in vivo micro-CT

Purpose: The aim of this study was to observe the bony change in the OVX rat longitudinally and to study the alendronate effect. Materials and Methods: Eighteen Sprague-Dewley rats, eight-week old each, were randomly assigned into three groups: one of those sham-operated (N=4), the other two were OVX: saline-treated (N=7) and alendronate-treated group (N=7). The saline-treated group was administered with saline solution (0.1mL/100g) daily, while the alendronate-treated group was given alendronate (1mg/kg, Sigma-Aldrich Corp. Korea) daily. Micro-CT scannings of the lumbar were consecutively done at baseline, at 3-week intervals during 9 weeks. Two and three dimensional bony analysis were done. Bone mineral density (BMD) was measured with Piximus (GE Lunar Co. USA). The average values of these three methods were compared with each group. Results: After 6 weeks the BMD of the OVX group showed lower tendency than that of sham group. After 6 weeks many 3D parameters of micro-CT showed higher values in the OVX-alendronate group compared with the OVXsaline group. Most 2D bony parameters were higher in the OVX-alendronate group compared with the OVX-saline group at 9 weeks. Conclusion: This study showed low BMD of the OVX group after 6 weeks and showed the effect of alendronate on the BMD and bony structures of ovariectomized rats. This study also showed usefulness of in vivo micro-CT in monitoring individual bone changes over time.
Learning Discriminative Sentiment Representation from Strongly- and Weakly Supervised CNNs

Visual sentiment analysis is attracting increasing attention with the rapidly growing amount of images uploaded to social networks. Learning rich visual representations often requires training deep convolutional neural networks (CNNs) on massive manually labeled data, which is expensive or scarce especially for a subjective task like visual sentiment analysis. Meanwhile, a large quantity of social images is quite available yet noisy by querying social networks using the sentiment categories as keywords, where various types of images related to the specific sentiment can be easily collected. In this article, we propose a multiple kernel network for visual sentiment recognition, which learns representation from strongly- and weakly supervised CNNs. Specifically, the weakly supervised deep model is trained using the large-scale data from social images, whereas the strongly supervised deep model is fine tuned on the affecitve datasets with manual annotation. We employ the multiple kernel scheme on the multiple layers of CNNs, which can automatically select the discriminative representation by learning a linear combination from a set of pre-defined kernels. In addition, we introduce a large-scale dataset collected from popular comics of various countries, such as America, Japan, China, and France, which consists of 11,821 images with various artistic styles. Experimental results show that the multiple kernel network achieves consistent improvements over the state-of-the-art methods on the public affective datasets, as well as the newly established Comics dataset. The Comics dataset can be found at http://cv.nankai. edu.cn/projects/Comic.
Evaluation of bone-conducted ultrasonic hearing-aid regarding transmission of speaker gender and age information

Human listeners can perceive speech signals in a voice-modulated ultrasonic carrier from a bone-conduction stimulator, even if the listeners are patients with sensorineural hearing loss. Considering this fact, we have been developing a bone-conducted ultrasonic hearing aid (BCUHA). The purpose of this study was to assess the usefulness of the BCUHA in transmission of speakers’ physical attributes: gender and age. The evaluation used gender and age-identiﬁcation experiments. The experiments were also conducted under air-conduction (AC) and cochlear implant simulator (CIsim) conditions. The results showed that: the BCUHA can well transmit speakers’ gender information; the BCUHA can transmit speaker age information better than CIsim.
Bootstrap Standard Error Estimates and Inference

Asymptotic justi cation of the bootstrap often takes the form of weak convergence of the bootstrap distribution to some limit distribution. Theoretical literature recognized that the weak convergence does not imply consistency of the bootstrap second moment or the bootstrap variance as an estimator of the asymptotic variance, but such concern is not always reected in the applied practice. We bridge the gap between the theory and practice by showing that such common bootstrap based standard error in fact leads to a potentially conservative inference. JEL Classi cation: C12, C14, C31, C32
Design Research Themes for Mindful Interaction

The increasing excessive, automated or unaware use of smartphones calls for a better understanding and design of ‘mindful’ interactions with this device. In this 2-week study with 11 participants we focus on the influence of the operating system’s interface in inviting for ‘mindful’ or ‘mindless’ interactions. Patterns of smartphone use were studied with a standard interface for the first week, and a reconfigured interface for the second week the reconfigured interface being designed for more intentional use. Based on the study results we formulate a set of 5 design research themes for mindful interaction, and illustrate them in a conceptual proposal.
Using semantically-organized networks for the visualiza- tion of short texts

1Parsons School of Design, The New School 2Department of Social and Decision Sciences, Carnegie Mellon University 3School of Informatics, Computing, and Engineering, Indiana University Bloomington 4School of Earth and Space Exploration, Arizona State University 5School of Design Strategies, Parsons School of Design 6Department of Communication, University of California, Davis 7National War College, National Defense University
Fact-Checking Fake News

‘Fake News’ is recognised as a serious problem by modern society, and developing automated fact-checking systems can help to recognise and disprove it. This thesis introduces an end-to-end automated fact-checking system for the FEVER challenge. The document retrieval stage uses term frequency-inverse document frequency (TFIDF) Title Normalisation, a new ranking method that solves the pre-processing problems of rule-based title matching methods. The sentence retrieval stage and label prediction stage use adapted versions of ESIM (Chen et al. 2016). A comparative study of different TFIDF methods shows that using unigrams with titles and TFIDF title normalisation performs best. Title Normalisation improves the absolute performance of the top 100 ranked documents with approximately 8%. Furthermore, I introduce a two-stage document retrieval system and create Exact Match Embeddings to deal with out-of-vocabulary words. Finally, I find that refuted claims are harder to retrieve than supported claims with text-based document retrieval models. Title-based document retrieval systems do not show this phenomenon and perform better.
Creating Shared Value through Strategic Biobanking Public-Private Partnerships in Healthcare TROLLE VON SYDOW YLLENIUS ANTON AGERBERG

Societies are plagued by growing healthcare expenditures and budgetary constraints. The strategy for solving the issue has been heavily debated, with proposed solutions such as Valuebased healthcare (VBHC), Public-Private Partnerships (PPP) and improved medical treatments. A novel concept that aims to improve medical treatment is strategic biobanking. Strategic biobanking is the act of saving biological samples and clinical data for future research. Access to strategic samples can speed up future clinical trials and studies, provide researchers with more useful research material, enable more thorough analyses of biomarkers, facilitate faster drug development, and increase the power of both retrospective analyses and precision medicine. This thesis studies the shared value effects of a strategic biobanking PPP by drawing on the theoretical fields of VBHC, PPP and Creating Shared Value (CSV). Specifically, the effects of hospital organisational structure, regulatory framework and public interest on strategic biobanking PPPs was studied. The research was carried out through a single holistic case study of Karolinska University Hospital in Stockholm, Sweden and multiple pharmaceutical companies, and data was collected through semi-structured interviews. Data analysis was carried out in accordance with the grounded theory framework. The researchers find that regulatory structure can limit the options when crafting the business model and the industry value proposition for a strategic biobanking PPP. Some strategies on how to deal with these restraints are outlined. Furthermore, the research highlights the importance of longitudinal data-sets and how a hospital organised according to the VBHC principles is more suitable for implementation of longitudinal sampling routines. Finally, the research shows that that the concept of CSV can act as guidance for private partner decision making to increase public interest. By adopting principles of transparency regarding financial incentives and motivations, an industry partner can garner increased trust with the general public as well as their public partner. The shared value effects are pronounced, and the study finds that a strategic biobanking PPP moves the boundary for what is scientifically possible for all stakeholders in the healthcare domain. Key-words Creating shared value, Strategic CSR, Public-private Partnerships, Value based healthcare, Biobanking Examensarbete TRITA-ITM-EX 2018:XYZ Gemensamt värdeskapande genom strageisk biobankning
MAPPING AND DIFFERENCES OF SOIL PHYSICAL PROPERTIES

Soil physical properties are of great importance for the evaluation of soil compaction or for soil conditions characterization. The latter option is important for the purpose of soil tillage machines. Field measurements were arranged in order to characterize the variation of soil physical properties within the plot. Two methods of soil physical characteristics evaluation were used: soil cone index and undisturbed soil sampling. Undisturbed soil samples were taken from 102 points in almost regular grid 6 x 6 m. Cone index was measured thrice in the each point. The maps of mentioned measurements were created and compared by using statistical methods. Penetration resistance generally copied soil bulk density but otherwise than expected. Nevertheless, the most important finding was that relatively very small changes of soil moisture content can influence penetration resistance regardless, or with minimal impact, of soil bulk density results.
Behavioral intention of using one-stop mobile application: evidence from department stores

This research study is an attempt to focus on intentions of one-stop mobile application of departmental stores to determine the effects of time availability, perceived usefulness, compatibility, and apparently perceived easiness, besides the involvement of customers, which will obviously create a correlation effect in their continuous use. The Technology Acceptance Model and Diffusion of Innovations Theory have been used in this research as the well-set theoretical foundations. To further conduct the research on the right footings, we used the network of questionnaires to carry out an appropriate investigation on a total of 308 effective samples, recovered accordingly. The results of our research clearly indicated the following aspects broadly: compatibility, perceived usefulness and perceived ease of use will positively affect the intentions; whereas time availability and compatibility will positively affect the customer? s involvement; and the customer? s involvement will in turn, positively affect the continuous intentions to use the mobile application. In spite of these positive results, the research also indicated that three hypotheses are invalid. To be more precise, following factors had been included therein: (i) time availability versus intention to use, (ii) perceived usefulness versus customer involvement, and (iii) perceived ease of use versus customer involvement.
Gender, Social Recognition, and Political Influence∗

What determines women’s political influence? While the literature on political engagement focuses on individual traits, a itudes, and participation, we argue that how these factors translate to political influence is fundamentally a social process that requires recognition from the broader community, with important implications for women’s influence. Using new data on networks of political influence in Philippine villages, we show that even a er controlling for socioeconomic status or political participation, women are still markedly less likely to be recognized as influential. Furthermore, we show that the commonly understood factors that correspond to political influence apply primarily to men, and that for women, the determinants of influence are more complex: embeddedness in the community and participation in community activities are more important than connections or wealth. In addition, we highlight important mechanisms of political influence for women and key limitations for how women can use their social networks for political ends. We thank Philip Keefer, Julien Labonne, Pablo erubin, and Francesco Trebbi for sharing data for this project and Marisa Abrajano, Therese Anders, Arjun Chowdhury, Julien Labonne, John Marshall, Diana Z. O’Brien, Christian Phillips, and Marissa Theys for providing valuable feedback. We also appreciate helpful suggestions from conference and seminar participants at the USC School of International Relations, PolNet 2018, and APSA. We thank Prudenciano Gordoncillo and the team at the University of the Philippines, Los Baños for assistance with the data collection. All remaining errors are ours. Department of Political Science and Vancouver School of Economics; cesi.cruz@ubc.ca Department of Agricultural Economics; cmatolentino@gmail.com