Miracle-dz/UniFAR-FTUs huggingface dataset:
Aspects of model dependence of η′–η complex treated by going beyond the isospin limit

Exploring the extent of model dependence, we study effects of certain Ansätze for the T -dependence of the correction term in the QCD topological susceptibility. The one producing unwanted effects on results at T > 0 in the η′-η complex in the usual limit of isospin symmetry, is largely cured from its peculiar behavior and brought into agreement with the other, when breaking of isospin symmetry is allowed and the realistic quark mass ratio mu/md = 0.50 is adopted.
Revisiting the tension between fast bars & the Λ CDM paradigm

The pattern speed with which galactic bars rotate is intimately linked to the amount of dark matter in the inner regions of their host galaxies. In particular, dark matter haloes act to slow down bars via torques exerted through dynamical friction. Observational studies of barred galaxies tend to find that bars rotate fast, while hydrodynamical cosmological simulations of galaxy formation and evolution in the ΛCDM framework have previously found that bars slow down excessively. This has led to a growing tension between fast bars and the ΛCDM cosmological paradigm. In this study we revisit this issue, using the Auriga suite of high resolution, magnetohydrodynamical cosmological zoom-in simulations of galaxy formation and evolution in the ΛCDM framework, finding that bars remain fast down to z = 0. In Auriga, bars form in galaxies that have higher stellar-to-dark matter ratios and are more baryondominated than in previous cosmological simulations; this suggests that in order for bars to remain fast, massive spiral galaxies must lie above the commonly used abundance matching relation. While this reduces the aforementioned tension between the rotation speed of bars and ΛCDM, it accentuates the recently reported discrepancy between the dynamically inferred stellar-to-dark matter ratios of massive spirals and those inferred from abundance matching. Our results highlight the potential of using bar dynamics to constrain models of galaxy formation and evolution.
High-temperature studies of multiple fluorinated traps within an Al2O3 gate dielectric for E-Mode AlGaN/GaN power MIS-HEMTs

Normally-off AlGaN/GaN MIS-HEMT devices with multiple fluorinated ALD-Al2O3 layers as the gate dielectric have been reported to achieve a high threshold voltage for normally-off operations with satisfactory performance for both on and off states at room temperature. However, a large swing in gate threshold voltage is found when devices operate at elevated temperatures. Hence, further study of the gate dielectric on the distribution of fluorinated trap states in the energy band are required to assess the gate function at higher temperatures. Through the use of the charge analytical model and Poole–Frenkel trap emission theory, the gate voltage stressing measurement was carried out to accurately find the effective trap state distribution within the Al2O3 energy bandgap created by fluorinated treatments. For the samples fabricated and used in the investigation, we found that a higher population of fluorinated trap states located deeper than 1.1 eV corresponding to emission levels above 200 °C would allow more trapped charges to remain in the dielectric at high temperature for better threshold voltage retention. We also discovered that a higher fluorine treatment power on the gate dielectric could yield a higher trap state density at deeper levels, resulting in better temperature stability.
Applications of Tunable TiO2 Nanotubes as Nanotemplate and Photovoltaic Device

Highly ordered anodic titanium oxide (ATO) TiO2 nanotube film has been synthesized via a typical two-step anodization method. Following a reductive doping approach, metallic materials (copper and nickel) can be efficiently electrodeposited into the nanotubes. This versatile process yields reproducible tubular structures in ATO membranes, because of the conductive nature of crystallized TiO2, yielding promising potential for nanotemplate applications. In this paper, we present a dye-sensitized solar cell constructed by employing such ATO films. It is observed that the reductive doping treatment can also enhance the solar cell’s short current density and fill factor, resulting in an improved energy conversion efficiency.
Impact of Improved Agricultural Technology Use on Household Income in Eastern Ethiopia: Empirical Evidence from a Propensity Score Matching Estimation

This article investigates the impact of improved agricultural technology use on farm household income in eastern Ethiopia. Primary data for the study were obtained from a random sample of 248 rural households, 119 of which are improved technology users and the rest are non-users. The research employed the propensity score matching procedure to establish the causal relationship between adoption of improved crop and livestock technologies and changes in farm income. Results from the econometric analysis show that households using improved agricultural technologies had, on average, 23,031.28 Birr 1 higher annual farm income compared to those households not using such technologies. Our findings highlight the importance of promoting multiple and complementary agricultural technologies among rural smallholders. Therefore, we suggest that rural technology generation, dissemination and adoption interventions be strengthened. Moreover, the linkage among research, extension, universities and farmers needs to be enhanced through facilitating multistakeholder innovation platforms.
Reactivation of the tumor suppressor PTEN by mRNA nanoparticles enhances antitumor immunity in preclinical models

The restoration of tumor suppressor PTEN by synthetic mRNA nanoparticles sensitizes Pten-null or mutated tumors to immune checkpoint blockade therapy. Promoting PTEN A mechanism by which tumors promote an immune suppressive microenviroment is through deletion or mutation of tumor suppressor genes, including the gene encoding phosphatase and tensin homolog deleted on chromosome 10 (PTEN). Thus, restoration of PTEN expression in tumor cells may serve as a means of enhancing antitumor immunity. To accomplish this, Lin et al. delivered PTEN-encoding synthetic mRNA in nanoparticles in vitro and in vivo. The nanoparticle-delivered mRNA led to expression of PTEN in tumor cells, which in turn reversed the suppressive tumor microenvironment. This led to improved control of tumor burden in mouse models of melanoma and prostate cancer. Thus, delivery of tumor suppressor–encoding mRNAs by nanoparticles may be a strategy to improve antitumor immune responses. Increasing clinical evidence has demonstrated that the deletion or mutation of tumor suppressor genes such as the gene-encoding phosphatase and tensin homolog deleted on chromosome 10 (PTEN) in cancer cells may correlate with an immunosuppressive tumor microenvironment (TME) and poor response or resistance to immune checkpoint blockade (ICB) therapy. It is largely unknown whether the restoration of functional PTEN may modulate the TME and improve the tumor’s sensitivity to ICB therapy. Here, we demonstrate that mRNA delivery by polymeric nanoparticles can effectively induce expression of PTEN in Pten-mutated melanoma cells and Pten-null prostate cancer cells, which in turn induces autophagy and triggers cell death–associated immune activation via release of damage-associated molecular patterns. In vivo results illustrated that PTEN mRNA nanoparticles can reverse the immunosuppressive TME by promoting CD8+ T cell infiltration of the tumor tissue, enhancing the expression of proinflammatory cytokines, such as interleukin-12, tumor necrosis factor–α, and interferon-γ, and reducing regulatory T cells and myeloid-derived suppressor cells. The combination of PTEN mRNA nanoparticles with an immune checkpoint inhibitor, anti–programmed death–1 antibody, results in a highly potent antitumor effect in a subcutaneous model of Pten-mutated melanoma and an orthotopic model of Pten-null prostate cancer. Moreover, the combinatorial treatment elicits immunological memory in the Pten-null prostate cancer model.
Human Data Interaction in Data-Driven Media Experiences : An Exploration of Data Sensitive Responses to the Socio-Technical Challenges of Personal Data Leverage

While explication of socio-technical challenges posed by personal data leverage in media research has been of interest recently, effective responses that alleviate them are yet to be studied. This paper reports the use of a Cross Media Profiler prototype supported by a Personal Data Store, that combines personal data from different media services for more holistic media recommendations. This prototype is used to probe and explore the integration of Human Data Interaction principles in media experiences, as a response to these challenges. Our focus groups reveal that users prefer the media service when supported by the PDS while highlighting improvements around transparency and control. The work leads to two outcomes : design recommendations for future media experiences to embody increased sensitivity to the implications of personal data leverage and a critique of the HDI agenda through the lens of data driven media, which scopes out potential for future IMX intervention.
Taking That Perfect Aerial Photo: A Synopsis of Interactions for Drone-based Aerial Photography and Video

Personal drones are more and more present in our lives and acting as “flying cameras” is one of their most prominent applications. In this work, we conduct a synopsis of the scientific literature on human-drone interaction to identify system functions and corresponding commands for controlling drone-based aerial photography and video, from which we compile a dictionary of interactions. We also discuss opportunities for more research at the intersection of drone computing, augmented vision, and personal photography.
Building a ‘Sicko’ AI: AIBO: An Emotionally Intelligent Artificial Intelligent GPT-2 AI Brainwave Opera

Using the GPT-2 algorithm developed by OpenAI in February 2019, a skewered or ‘sicko’ AI character was created as a live time entity running in the Google cloud. The algorithm allows imitations of human dialogue that produce fake and often realistic interactions emanating from computer cloud-based agents. The character was created as one of two characters in the emotionally intelligent artificial intelligence brainwave opera AIBO (Artificial Intelligent Brainwave Opera). The spoken word opera rhetorically inquired “Can an AI be fascist? ” and “Can an AI have epigenetic or inherited traumatic memory? ” through the interplay of human and non-human characters. This paper discusses certain aspects involved in creating the GPT-2 cloud-based character AIBO.
Evaluating AI assisted subtitling

Recent advances in artificial intelligence (AI) have led to an increased focus on automating media production. One relevant application area for AI is using speech recognition to create subtitles and closed captions for videos. The AI methods based on machine learning are still not sufficiently reliable in terms of producing perfect or acceptable subtitles. To compensate for this unreliability, AI can be used to build tools that support, rather than replace, human efforts and to create semi-automated workflows. In this paper, we present a prototype for including automated speech recognition for subtitling in an existing production-grade video editing tool. We devised an experiment with 25 participants and tested the efficiency and effectiveness of this tool compared to a fully manual process. The results show that there is a significant increase in both effectiveness and efficiency for novices in subtitling. Furthermore, the participants found the augmented process to be more demanding. We identify some usability issues and design choices that pertain to making augmented subtitling easier.
Diagnostic studies of ion beam formation in inductively coupled plasma

vi CHAPTER 1. GENERAL INTRODUCTION 1 Inductively Coupled Plasma Mass Spectrometry ICP Ion Extraction Polyatomic Ion Interferences Polyatomic Ion Mitigation Techniques Characteristics of Collision Technology Dissertation Organization References Figures 1 1 3 4 5 7 8 9 14 CHAPTER 2. UPDATED IONIZATION EFFICIENCY CALCULATIONS FOR INDUCTIVELY COUPLED PLASMA MASS SPECTROMETRY 16 Abstract Introduction Theory Saha equation for singly charged ions Saha equation for doubly charged ions Ionization of metal oxide molecules Dissociation of metal oxide ions Updated ionization efficiency tables for singly charged ions Ionization efficiency tables of doubly charged ion formation Metal oxide ionization and dissociation 16 16 19 21 22 22 23 24 25 27Introduction Theory Saha equation for singly charged ions Saha equation for doubly charged ions Ionization of metal oxide molecules Dissociation of metal oxide ions Updated ionization efficiency tables for singly charged ions Ionization efficiency tables of doubly charged ion formation Metal oxide ionization and dissociation 16 16 19 21 22 22 23 24 25 27
Global mental health and HIV care: gaps and research priorities

Common mental disorders, including depression, anxiety and post-traumatic stress disorder (PTSD), occur at high rates among people living with HIV (PLWH) [1]. Mental disorders are associated with poorer HIV care continuum outcomes [2] and co-occur with other psychological and structural factors including violence, stigma and other social determinants of health, which are additional barriers to HIV treatment [3]. Furthermore, mental disorders may interact with and impact biological factors to worsen HIV outcomes. This viewpoint highlights three research gaps in mental health among PLWH: (a) understanding complex interactions between biological, psychological and structural factors that influence mental disorders in PLWH; (b) developing and testing interventions to address mental health, as well as cooccurring psychological and structural factors, to improve HIV outcomes and (c) implementation science to understand how to best implement and scale up evidence-based interventions to improve mental health and HIV outcomes (see Figure 1).
Three-Dimensional Reconstruction of Huizhou Landscape Combined with Multimedia Technology and Geographic Information System

Huizhou architecture is the most important Chinese ancient architecture. Being a traditional architecture, Huizhou architecture has always maintained a unique artistic style of elegance, simplicity, richness, and integration. Huizhou landscape is an essential embodiment of Huizhou culture. The landscape and pattern of Huizhou villages are the external manifestations of Huizhou culture symbols. This architecture needs to be preserved using multimedia technology and geographic information system. This paper uses these systems to extract the landscape image and develop three-dimensional Huizhou village’s modeling methods with automation, high efficiency, and low cost. It has realized the functions of landscape information query, high-dimensional measurement, terrain analysis, visibility analysis, and virtual roaming of Huipai village. This modeling has specific promotion effects on landscape spatial analysis, landscape planning, ecological protection, natural and cultural inheritance, and tourism development.
GreenCities - Monitoring Cities for Nature Based Urban Solutions and Ecosystem Services / GreenCities - Erdbeobachtung für urbane, naturbasierte Lösungen und Ökosystemdienstleistungen

Cities are unique living entities that evolve and require care to thrive. Earth observation of the urban environment is therefore essential to keep a watchful eye on their condition. In-situ measurements are also needed to provide details that cannot be easily derived from remotely sensed imagery. Green infrastructure and the ecosystem services they provide play an important role for the wellbeing of city populations and provide resilience against the effects of climate change. EO is essential for mapping the relevant details to produce information on which to base healthy city evolution.
Decoupling Global and Local Representations via Invertible Generative Flows

In this work, we propose a new generative model that is capable of automatically decoupling global and local representations of images in an entirely unsupervised setting, by embedding a generative ﬂow in the VAE framework to model the decoder. Speciﬁcally, the proposed model utilizes the variational auto-encoding framework to learn a (low-dimensional) vector of latent variables to capture the global information of an image, which is fed as a conditional input to a ﬂow-based invertible decoder with architecture borrowed from style transfer literature. Experimental results on standard image benchmarks demonstrate the effectiveness of our model in terms of density estimation, image generation and unsupervised representation learning. Importantly, this work demonstrates that with only architectural inductive biases, a generative model with a likelihood-based objective is capable of learning decoupled representations, requiring no explicit supervision. The code for our model is available at https://github.com/XuezheMax/wolf .
E-Commerce Enterprise Supply Chain Financing Risk Assessment Based on Linked Data Mining and Edge Computing

In recent years, the rapid development of information technology has affected the way the world economy operates. The emergence of e-commerce has greatly shortened the time and space distance between economic participants and maximized the sharing of resources. However, the financial management and risk assessment capabilities of the existing supply are insufficient to adapt to the rapidly developing new environment. This article uses a combination of normative analysis and empirical analysis to analyze the status quo of the supply chain of small and medium e-commerce companies. First, this article establishes an evaluation framework for the supply chain of e-commerce companies based on edge computing. Second, according to the distribution of the supply chain, this article adds the member’s predetermined quota, reputation, execution time, and other indicators as parameters to establish a fuzzy neural network model. On this basis, combined with the price regression model, the pricing plan is evaluated. The results show that the financing risk obtained by this model differs very little from the actual risk. The above-mentioned model constructs an e-commerce enterprise supply chain financing risk management model that adapts to the environment of the new era.
Psychotropic Activity of Sphaeranthus indicus Linn. In Experimental Animals

Sphaeranthus indicus Linn. (Asteraceae) is a branched herb with purple color flowers, distributed in wet places. The present study evaluated the neuropharmacological effects of the hydroalcoholic extract of S. indicus (SIE) in rats and mice. Effect of SIE (100, 200 and 500 mg/kg, p.o.) on spontaneous motor activity, pentobarbital-induced sleeping time, motor coordination, exploratory behaviour and apomorphine-induced stereotypy were investigated in mice. SIE (100, 200 and 500 mg/kg, p.o.) induced catalepsy and effect of SIE on haloperidol induced catalepsy were studied in rats. The SIE showed significant reduction of spontaneous motor activity, exploratory behaviour and prolonged pentobarbital sleeping time in the mice. Neuroleptic potential of SIE was observed by the results in which SIE antagonized apomorphine-induced stereotypy in mice, produced catalepsy and potentiated haloperidol-induced catalepsy in rats. Further, SIE had no effect on motor-coordination as determined by the rota rod test. These results provide evidence that the hydroalcoholic extract of Sphaeranthus indicus may contain psychoactive substances that are sedative in nature with possible neuroleptic properties.
Generative Scene Graph Networks

Human perception excels at building compositional hierarchies of parts and objects from unlabeled scenes that help systematic generalization. Yet most work on generative scene modeling either ignores the part-whole relationship or assumes access to predefined part labels. In this paper, we propose Generative Scene Graph Networks (GSGNs), the first deep generative model that learns to discover the primitive parts and infer the part-whole relationship jointly from multi-object scenes without supervision and in an end-to-end trainable way. We formulate GSGN as a variational autoencoder in which the latent representation is a treestructured probabilistic scene graph. The leaf nodes in the latent tree correspond to primitive parts, and the edges represent the symbolic pose variables required for recursively composing the parts into whole objects and then the full scene. This allows novel objects and scenes to be generated both by sampling from the prior and by manual configuration of the pose variables, as we do with graphics engines. We evaluate GSGN on datasets of scenes containing multiple compositional objects, including a challenging Compositional CLEVR dataset that we have developed. We show that GSGN is able to infer the latent scene graph, generalize out of the training regime, and improve data efficiency in downstream tasks.
Towards Impartial Multi-task Learning

Multi-task learning (MTL) has been widely used in representation learning. However, naı̈vely training all tasks simultaneously may lead to the partial training issue, where specific tasks are trained more adequately than others. In this paper, we propose to learn multiple tasks impartially. Specifically, for the task-shared parameters, we optimize the scaling factors via a closed-form solution, such that the aggregated gradient (sum of raw gradients weighted by the scaling factors) has equal projections onto individual tasks. For the task-specific parameters, we dynamically weigh the task losses so that all of them are kept at a comparable scale. Further, we find the above gradient balance and loss balance are complementary and thus propose a hybrid balance method to further improve the performance. Our impartial multi-task learning (IMTL) can be end-to-end trained without any heuristic hyper-parameter tuning, and is general to be applied on all kinds of losses without any distribution assumption. Moreover, our IMTL can converge to similar results even when the task losses are designed to have different scales, and thus it is scale-invariant. We extensively evaluate our IMTL on the standard MTL benchmarks including Cityscapes, NYUv2 and CelebA. It achieves the new stateof-the-art among loss weighting methods under the same experimental settings.
Time-Sensitive Adaptive Model for Adult Image Classification

Images play an important role in modern internet communications, but not all of the images shared by the users are appropriate, and it is necessary to check and reject the inappropriate ones. Deep neural networks do this task perfectly, but it may not be necessary to use maximum power for all images. Many easier-to-identify images may be classiﬁed at a lower cost than running the full model. Also, the pressure on the system varies from time to time, so an algorithm that can produce the best possible results for diﬀerent budgets is very useful. For this purpose, a deep convolutional neural network with the ability to generate several outputs from its various layers has been designed. Each output can be considered as a classiﬁer with its own cost and accuracy. A selector is then used to select and combine the results of these outputs to produce the best possible result in the speciﬁed time budget. The selector uses a reinforcement learning model, which, despite the time-consuming learning phase, is fast at execution time. Our experiments on challenging social media images dataset show that the proposed model can reduce the processing time by 32 % by sacriﬁcing only 1.4 % of accuracy compared to the VGG-f network. Also, using diﬀerent metrics such as F1-score and AUC (the Area Under the Curve in the accuracy vs. time budget chart), the superiority of the proposed model at diﬀerent time budgets over the base model is shown.