Miracle-dz/UniFAR-FTUs huggingface dataset:
Have Seen Enough : Transferring Parts Across Categories

The recent successes of deep learning have been possible due to the availability of increasingly large quantities of annotated data. A natural question, however, is whether further progress can be indefinitely sustained by annotating more data, or whether there is a saturation point beyond which a problem is essentially solved, or the capacity of a model is saturated. In this paper we examine this question from the viewpoint of learning shareable semantic parts, a fundamental building block to generalize visual knowledge between object categories. We ask two research questions often neglected: whether semantic parts are also visually shareable between classes, and how many annotations are required to learn them. In order to answer such questions, we collect 15,000 images of 100 animal classes and annotate them with parts. We then thoroughly test active learning and domain adaptation techniques to generalize to unseen classes parts that are learned from a limited number of classes and example images. Our experiments show that, for a majority of the classes, part annotations transfer well, and that performance reaches 98% of the accuracy of the fully annotated scenario by providing only a few thousand examples.
The evaluation of the relationship between fetuin-A and traditional and non-traditional cardiovascular risk factors in kidney transplantation recipients

Objectives: Kidney transplantation recipients (KTRs) have higher cardiovascular complications risk compared to the general population. Cardiovascular risk factors (CVRF, Traditional and non-traditional) are widely studied to understand the causes of increased cardiovascular disease (CVD) risk in KTRs. Fetuin-A prevents from vascular calcification (VC) by inhibiting production and collapsing of apatite crystals to the vascular wall. The relationship between fetuin-A levels and CVRF in KTRs was investigated in this study. Methods: Sixty-two KTRs with no prior CVD history participated. Anthropometrical, laboratory (fetuin-A, inflammation markers, antioxidants, lipid peroxidation products) and cardiological (echocardiographic, pulse wave velocity) measurements were performed. Participants were divided into two groups according to normal (≥ 0.5 g/L, n = 32, NFA) and low ( 0.05). The ratios of drug usage such as immunosuppressives, anti-hypertensives and statin were comparable between two groups. Parathormone levels were significantly higher in the NFA group ( p = 0.015) and glomerular filtration rate was calculated significantly higher in LFA group ( p = 007). The comparison of other CVRF reveals no significant difference ( p > 0.05). Conclusions: Although many CVRF improved in KTRs, subclinical inflammation markers were still higher than the healthy population. Identification and early recognition of CVRF in high-risk individuals may contribute to the reduction of cardiovascular mortality. In our study, we observed no significant relationship between fetuin-A levels and CVRFs. We evaluated the relationship between serum fetuin-A levels on cardiovascular risk factors by its role in pathophysiology.
KI-Mon: A Hardware-assisted Event-triggered Monitoring Platform for Mutable Kernel Object

Kernel rootkits undermine the integrity of system by manipulating its operating system kernel. External hardware-based monitors can serve as a root of trust that is resilient to rootkit attacks. The existing external hardware-based approaches lack an event-triggered verification scheme for mutable kernel objects. To address the issue, we present KI-Mon, a hardware-based platform for event-triggered kernel integrity monitor. A refined form of bus traffic monitoring efficiently verifies the update values of the objects, and callback verification routines can be programmed and executed for a designated event space. We have built a KI-Mon prototype to demonstrate the efficacy of KI-Mon's event-triggered mechanism in terms of performance overhead for the monitored host system and the processor usage of the KI-Mon processor.
Data driven design

By contemplating on the Eigenchair project, we ponder upon strategies and concepts of designing by using information technologies. What are the potentials of data driven design? What happens with objects when they are abstracted and reduced to a set of data? The emphasis is no longer on the creation of physical objects, but on conceiving meta-objects in the possibility space. Furthermore, this enables us to manipulate with a whole population of objects, instead of a single object. How do we get this abstract system to relate to the real world? Information technologies have opened up a number of new ways of thinking about the world and the object and they, by far, surpassed the formally simplified expression in design and architecture. Based on intellectual heritage of history and culture, information technologies can, by utilizing and recycling various elements and information, explore the 21st century object.
Learning Discrete State Abstractions With Deep Variational Inference

High-dimensional state spaces are common in deep reinforcement learning (Mnih et al., 2015). Although states may be as large as images, typically the information required to make good decisions is much smaller. This motivates the need for state abstraction, the process of encoding states into compressed representations that retain features that inform action and discard uninformative features. One principled approach to state abstraction is bisimulation in Markov decision processes (MDP) (Dean and Givan, 1997). Bisimulations formalize the notion of finding a smaller equivalent abstract MDP that preserves transition and reward information; i.e., it retains relevant decision-making information while reducing the state space size. We demonstrate this idea in Figure 1, where a grid world with fifteen states is compressed into an MDP with three states. In this paper, we introduce an approach to finding approximate MDP bisimulations using the variational information bottleneck (VIB, Tishby et al. (2001); Alemi et al. (2017)). The VIB framework is typically used to learn representations that predict quantities of interest accurately while ignoring certain aspects of the domain. VIB methods have previously been applied to state abstraction, but the learned abstraction does not in general take the form of an MDP bisimulation (Abel et al., 2019). This is problematic, because the abstract MDP can only represent the policies it was trained on, but cannot be used to plan for new tasks. To resolve this, whereas Abel et al. (2019) use the abstract states to predict actions from an expert policy, we use abstract states to predict learned Q-values in the VIB objective. In our setup, a learned encoder maps a state s in the original MDP into a continuous embedding z. We map the continuous state z onto a discrete abstract state s̄ by performing inference in a learned probabilistic model. Our VIB method learns an encoder (i.e. a state abstraction s 7→ z) that is predictive of the Q-values that are returned by a deep Q-network, but is regularized using structured prior over the embedding space (z). Concretely, we propose using priors that prefer clusters with Markovian transition structure. A sequence of embedded states (z1, z2, . . .) is treated as observations from either a Gaussian mixture model (GMM) or an action-conditioned hidden Markov model (HMM), where each embedding zt is emitted from a latent cluster representing abstract state s̄t. In the HMM case, we also learn a cluster transition matrix for each action, serving as the abstract MDP transition model. The key insight is that abstract states s̄ group together ground states s (and embeddings z) with similar Q-values and similar transition properties, thereby forming an approximate MDP bisimulation.
Efficient, Nonintrusive Outcoupling in Organic Light Emitting Devices Using Embedded Microlens Arrays

We demonstrate efficient light extraction from the active region of bottom-emitting organic light emitting devices (OLEDs) using a high refractive index, nondiffractive hemispherical microlens array located between the transparent anode and embedded in the low refractive index glass substrate (n = 1.5). The subelectrode microlens array (SEMLA) results in a maximum external quantum efficiency of 70 ± 4% for green phosphorescent OLEDs (PHOLEDs). Furthermore, the wavelength- and viewing-angle-independent light extraction structure results in white PHOLED external efficiencies of 50 ± 3%. The SEMLA light extraction structure is nonintrusive; that is, it lies completely outside of the OLED structure. Since this design has no effect on the image resolution, it is compatible with applications for both displays and white light illumination, with no dependence on molecular transition dipole orientation and the active organic layers (and hence diode electrical characteristics) used in the PHOLED. Finally, due to th.
Stack-U-Net: Refinement Network for Image Segmentation on the Example of Optic Disc and Cup

In this work, we propose a special cascade network for image segmentation, which is based on the U-Net networks as building blocks and the idea of the iterative refinement. The model was mainly applied to achieve higher recognition quality for the task of finding borders of the optic disc and cup, which are relevant to the presence of glaucoma. Compared to a single U-Net and the state-of-the-art methods for the investigated tasks, very high segmentation quality has been achieved without a need for increasing the volume of datasets. Our experiments include comparison with the best-known methods on publicly available databases DRIONS-DB, RIM-ONE v.3, DRISHTI-GS, and evaluation on a private data set collected in collaboration with University of California San Francisco Medical School. The analysis of the architecture details is presented, and it is argued that the model can be employed for a broad scope of image segmentation problems of similar nature.
Chapter 7: Spectrahedral Approximations of Convex Hulls of Algebraic Sets

This chapter describes a method for finding spectrahedral approximations of the convex hull of a real algebraic variety (the set of real solutions to a finite system of polynomial equations). The procedure creates a nested sequence of convex approximations of the convex hull of the variety. Computations can be done modulo the ideal generated by the polynomials which has several advantages. We examine conditions under which the sequence of approximations converges to the closure of the convex hull of the real variety, either asymptotically or in finitely many steps, with special attention to the case in which the very first approximation yields a semidefinite representation of the convex hull. These methods allow optimization, or approximation of the optimal value, of a linear function over a real algebraic variety via semidefinite programming.
Review of memristor devices in neuromorphic computing: materials sciences and device challenges

The memristor is considered as the one of the promising candidates for next generation computing systems. Novel computing architectures based on memristors have shown great potential in replacing or complementing conventional computing platforms based on the von Neumann architecture which faces challenges in the big-data era such as the memory wall. However, there are a number of technical challenges in implementing memristor based computing. In this review, we focus on the research performed on the memristor material stacks and their compatibility with CMOS processes, the electrical performance, and the integration. In addition, recent demonstrations of neuromorphic computing using memristors are surveyed.
The winner takes it all

• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Natural Resources for Human Health

Fourteen plants endogenous to Sudan were selected to evaluate their total phenolic content, antioxidant, antimalarial and cytotoxicity potential in the present work. Extracts were prepared by maceration of each plant material in chloroform: methanol (C: M; 1:1 v/v) mixture. The antioxidant activity was determined by measuring the radical scavenging effects against 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2’-azino-bis (3-ethylbenzothiazoline-6sulfonic acid) (ABTS), and ferric-reducing (FRAP) capacity. The antiplasmodial activity was determined using the NF54 strain of Plasmodium falciparum. Cytotoxicity was evaluated towards human colorectal carcinoma cell line (HCT-116), human hormone-sensitive and invasive breast cancer cell line (MCF-7), and human hormone-resistant breast cancer cell line (MDA-MB231), in addition to endothelial normal EAhy-296 cell line. Results showed that the plants’ total phenolic and flavonoid contents were variable. Of the 14 plant species, only Burnatia enneandra showed high in vitro antiplasmodial activity (IC50 5758 ng/mL). Some plants possessed considerable free radical scavenging ability and reducing power. Coccinia grandis fruit extract (IC50 13.23± 0.51μg/mL) andGeigeria alata root extract (IC50 35.54± 0.27μg/mL) displayed the highest DPPH and ABTS scavenging activity, respectively. Striga hermonthica whole plant extract exhibited the highest FRAP reducing power (107.15± 0.11 nmol Feeq./mg). At a 100 μg/mL concentration, Dioscorea hirtiflora bulb extract displayed the highest cytotoxicity (74.23 ± 03.72%), followed by Mitragyna inermis fruit extract (65.28 ± 04.60%) against HCT-116 cell line. Aerva javanica leaf extract showed toxicity to the MDA-MB-231 cell line (50.82 ± 07.46%) at 100 μg/mL. The current study results showed that endogenous medicinal plants might represent a rich source of natural antioxidant, antimalarial and antitumor agents.
Natural Resources for Human Health

The study aimed to provide confirmatory status on traditional usage of Detarium microcapum against urinary and intestinal infections and serve as an alternative in fighting against multi-drug resistance. Hence, the determination of chemical constituents of stem bark using GC-MS because of its sensitivity and high resolving power in identifying compounds, and test the antibacterial potential of methanol crude extract on some bacteria associated with the diseases claimed to be treated by traditional healers. Bacterial isolates were collected, and zone of inhibition diameter (ZID), minimum inhibition concentration (MIC), minimum bactericidal concentration (MBC) and minimum bacteriostatic concentration (MBS) were conducted to determine antibacterial activity. At the same time, gas chromatography coupled with mass spectrometry (GC-MS) was used to identify the phytoconstituents. The mean ZID (mm) ranged from 17.3±21 to 20.0±35 mm and showed a statistical significance difference at P< 0.05 for S. aureus, E. coli, and K. pneumonia. The MIC ranged from 11.67±7.6 to 4.17±1.4 mg/L, MBS 16.67±5.8 to 5.00±00, MBC 20.0±00 to 5.0±00, in order of increasing concentrations. Inspection of the MBC/MIC ratio indicated a bactericidal effect across isolates. From the GCMS analysis, twenty-six phytochemical constituents were identified, primarily fatty acids or their esters. Chemical constituents’ from D. microcapum methanol crude extract were identified. The antibacterial activity of some compounds directly correlates with the bioactivity of the same isolated and tested elsewhere, from a different source, thus its potential and therefore supporting the claim for its traditional usage. The research recommends in-depth research toward identifying specific active compounds for public health solutions.
Commercial Policy and Foreign Ownership

To serve the domestic market, foreign multinationals often not only export there but also control local firms through FDI. This paper examines the effects of trade and industrial policies on prices, outputs, profits, and welfare when exports and FDI coexist. Specifically, we focus on the case in which a foreign firm has full control of a local firm through partial ownership. Cross-border ownership on the basis of both financial interests and corporate control leads to horizontal market-linkages through which tariffs and production subsidies may harm a locally-owned firm but benefit a foreign firm. Foreign ownership regulation benefits a locally-owned firm.
Risk Factors for Severe COVID-19 Outcomes Among Persons Aged ≥18 Years Who Completed a Primary COVID-19 Vaccination Series — 465 Health Care Facilities, United States, December 2020–October 2021

Vaccination against SARS-CoV-2, the virus that causes COVID-19, is highly effective at preventing COVID-19-associated hospitalization and death; however, some vaccinated persons might develop COVID-19 with severe outcomes† (1,2). Using data from 465 facilities in a large U.S. health care database, this study assessed the frequency of and risk factors for developing a severe COVID-19 outcome after completing a primary COVID-19 vaccination series (primary vaccination), defined as receipt of 2 doses of an mRNA vaccine (BNT162b2 [Pfizer-BioNTech] or mRNA-1273 [Moderna]) or a single dose of JNJ-78436735 [Janssen (Johnson & Johnson)] ≥14 days before illness onset. Severe COVID-19 outcomes were defined as hospitalization with a diagnosis of acute respiratory failure, need for noninvasive ventilation (NIV), admission to an intensive care unit (ICU) including all persons requiring invasive mechanical ventilation, or death (including discharge to hospice). Among 1,228,664 persons who completed primary vaccination during December 2020-October 2021, a total of 2,246 (18.0 per 10,000 vaccinated persons) developed COVID-19 and 189 (1.5 per 10,000) had a severe outcome, including 36 who died (0.3 deaths per 10,000). Risk for severe outcomes was higher among persons who were aged ≥65 years, were immunosuppressed, or had at least one of six other underlying conditions. All persons with severe outcomes had at least one of these risk factors, and 77.8% of those who died had four or more risk factors. Severe COVID-19 outcomes after primary vaccination are rare; however, vaccinated persons who are aged ≥65 years, are immunosuppressed, or have other underlying conditions might be at increased risk. These persons should receive targeted interventions including chronic disease management, precautions to reduce exposure, additional primary and booster vaccine doses, and effective pharmaceutical therapy as indicated to reduce risk for severe COVID-19 outcomes. Increasing COVID-19 vaccination coverage is a public health priority.
Incidence of juvenile idiopathic arthritis in the Nordic countries. A population based study with special reference to the validity of the ILAR and EULAR criteria.

OBJECTIVE To find the incidence of juvenile arthritis according to the ILAR and EULAR criteria within defined areas in the Nordic countries, and to study the validity of the ILAR and EULAR criteria from this perspective. METHOD A longitudinal, prospective, population based study with patients enrolled according to the ILAR and EULAR criteria. Twenty doctors in Iceland, Norway, Sweden, Denmark, and Finland collected data from the incidence cases within their catchment areas over a period of 1.5 years, beginning July 1, 1997. Clinical and serological data from the first year of the disease were collected. RESULTS In the whole group of 315 patients, the incidence rate was 15 per 100,000 children/year (95% CI 13-17) according to the ILAR criteria, varying from 7 (1-13) in Iceland, 19 (7-31) and 23 (10-36) from 2 different regions in Norway, and 9 (5-12) and 16 (9-23) from 2 different areas in Denmark, to 15 (12-18) in Sweden and 21/100,000/year (15-26) in the Helsinki region in Finland. An early peak in distribution for age of onset was found in girls but not in boys. The number of antinuclear antibody (ANA) positive children in the whole group, made up of children who had undergone at least one analyzed ANA test, was 123/315 (39%). Girls were ANA positive in 83/197 (42%) and boys in 40/118 (34%). Uveitis developed in 27/315 (8.6%) children during the first 6 months of the disease. CONCLUSION Incidence rates of juvenile arthritis for areas within the Nordic countries were in accord with previous data. The ILAR criteria present slightly higher incidence rates, with a shorter disease duration for inclusion, compared to the EULAR criteria. Patients in one subgroup in either of the criteria sets do not necessarily belong to the expected subgroup in the other set of criteria; e.g., for juvenile ankylosing spondylitis (EULAR) and enthesitis related arthritis (ILAR). Our epidemiological findings are a reminder to be aware of possible new subgroups in children with juvenile arthritis.
Representing semantic relatedness

To do text mining, the first question we must address is how to represent documents. The way a document is organised reflects certain explicit and implicit semantic and syntactical coupling relationships which are embedded in its contents. The effective capturing of such content couplings is thereby crucial for a genuine understanding of text representations. It has also led to the recent interest in document similarity analysis, including semantic relatedness, content coverage, word networking, and term-term couplings. Document similarity analysis has become increasingly relevant since roughly 80% of big data is unstructured. Accordingly, semantic relatedness has generated much interest owing to its ability to extract coupling relationships between terms (words or phrases). Existing work has focused more on explicit couplings and this is reflected in the models that have been built. In order to address the research limitations and challenges associated with document similarity analysis, this thesis proposes a semantic coupling similarity measure and the hierarchical tree learning model to fully enrich the semantics within terms and documents, and represent documents based on the comprehensive couplings of term pairs. In contrast to previous work, the models proposed can deal with unstructured data and terms that are coupled for various reasons, thereby addressing natural language ambiguity problems. Chapter 3 explores the semantic couplings of pairwise terms by involving three types of coupling relationships: (1) intra-term pair couplings, reflecting the explicit relatedness within term pairs that is represented by the relation