Fairness has become a critical value online, and the latest studies consider it in many problems. In recommender systems, fairness is important since the visibility of items is controlled by systems. ...Previous fairness-aware recommender systems assume that sufficient relationship data between users and items are available. However, it is common that new users and items are frequently introduced, and they have no relationship data yet. In this paper, we study recommendation methods to enhance fairness in a cold-start state. Fairness is more significant when the preference of a user or the popularity of an item is unknown. We propose a meta-learning-based cold-start recommendation framework called FaRM to alleviate the unfairness of recommendations. The proposed framework consists of three steps. We first propose a fairness-aware meta-path generation method to eliminate bias in sensitive attributes. In addition, we construct fairness-aware user representations through the meta-path aggregation approach. Then, we propose a novel fairness objective function and introduce a joint learning method to minimize the trade-off between relevancy and fairness. In extensive experiments with various cold-start scenarios, it is shown that FaRM is significantly superior in fairness performance while preserving relevance accuracy over previous work.
The hypomania checklist-32 (HCL-32) is a widely used questionnaire developed for identifying hypomanic components in patients with a depressive episode. Measuring and screening previous hypomanic ...symptoms in individuals without any definite history of depressive episode would also be needed for early detection of bipolar disorders (BDs). This study aimed at testing the clinical utility of the HCL-32 for screening of BDs in the non-clinical population.
Lifetime history of hypomanic symptoms was evaluated by using the HCL-32 in 220 patients with BDs and 313 non-clinical individuals. Sensitivity, specificity, and the area under the curve (AUC) of the Receiver Operating Characteristic (ROC) were evaluated for assessing the discriminatory power of the scale and its two sub-domains in screening BDs.
The mean HCL-32 total score was significantly higher in the Bipolar II disorder group compared to the non-clinical group (P < 0.001). Most of the items (10/12) of the irritable/risk-taking factor showed higher positive responses in patient groups. Items of active/elated factor showed mixed results. The HCL-32 total score and the active/elated factor score were not adequate for both BDs and its subgroups with AUC values of less than 0.7. The irritable/risk-taking factor score showed higher discrimination power, i.e. AUC for BDs, Bipolar I disorder, and Bipolar II disorder was 0.71, 0.67, and 0.75, respectively.
The HCL-32 could not adequately distinguish BD patients from the non-clinical adult population. However, the current study identified items of irritable/risk-taking factor of the scale that could be useful in screening BDs in the general population.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
BACKGROUNDThe neuregulin 1 (NRG1) gene has been investigated as a candidate susceptibility gene for schizophrenia. A number of studies have also explored the genetic effect of NRG1 on cognitive ...deficits related to schizophrenia, and thus far generated inconsistent results. The current study aimed to determine whether genetic variations in NRG1 are associated with cognitive domains in schizophrenic patients and healthy individuals.
MATERIALS AND METHODSComprehensive neuropsychological tests composed of six cognitive domains were administered to 135 clinically stable patients with schizophrenia and 119 healthy individuals. On the basis of previous reports of positive association, a total of four single nucleotide polymorphisms were analyzed. In testing the genotype effect on cognitive domains, we used repeated-measure analysis for six cognitive domain scores of each individual as repeated measurements.
RESULTSAn association of P-value less than 0.05 with at least one cognitive domain in patients and/or healthy individuals was observed for all of the single nucleotide polymorphisms. After applying the correction for multiple testing, the association remained statistically significant between rs6994992 and general cognitive ability (g) in the patient group and between rs2439272 and the ‘working memory’ domain in the group of healthy participants.
CONCLUSIONThis study suggests the involvement of NRG1 in the susceptibility for developing cognitive deficits in schizophrenic patients. For some cognitive domains, its genetic effect was also significant in generating interindividual variability within the normal functional range.
Abstract
The preparation of metal-organic frameworks (MOFs) having many open metal sites is an excellent approach for the development of highly active MOF-based catalysts. Herein, well-defined ...rice-shaped MOF-74 microparticles having structural defects are prepared by incorporating two analogous organic linkers 2,5-dihydroxy-1,4-bezenedicarboxylic acid (DHBDC) and 2-hydroxy-1,4-benzenedicarboxylic acid (HBDC) within the MOF-74 structure. The replacement of some of DHBDC in MOF-74 by HBDC causes the structural defects (excluding some of the bridged hydroxyl groups), and these structural defects provide the additional open metal sites within MOF-74. Finally, the additional open metal sites within MOF-74 result in the enhanced catalytic activity for the cyanosilylation of several aldehydes. A series of MOF-74s is prepared with various incorporated amounts of HBDC, and the optimum ratio between DHBDC and HBDC in MOF-74 to achieving the best catalytic performance is determined. In addition, the defected MOF-74 displays an excellent recyclability for the catalytic reaction.
Physical Layer Communication via Deep Learning Kim, Hyeji; Oh, Sewoong; Viswanath, Pramod
IEEE journal on selected areas in information theory,
05/2020, Letnik:
1, Številka:
1
Journal Article
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Reliable digital communication is a primary workhorse of the modern information age. The disciplines of communication, coding, and information theories drive the innovation by designing efficient ...codes that allow transmissions to be robustly and efficiently decoded. Progress in near optimal codes is made by individual human ingenuity over the decades, and breakthroughs have been, befittingly, sporadic and spread over several decades. Deep learning is a part of daily life where its successes can be attributed to a lack of a (mathematical) generative model. Deep learning empirically fits neural network models to the data, and the result has been extremely potent. In yet other applications, the data is generated by a simple model and performance criterion mathematically precise and training/test samples infinitely abundant, but the space of algorithmic choices is enormous (example: chess). Deep learning has recently shown strong promise in these problems too (example: alphazero). The latter scenario is a good description of communication theory. The mathematical models underlying canonical communication channels allow one to sample an unlimited amount of data to train and test the communication algorithms ((encoder, decoder) pairs) and the metric of bit (or block) error rate allows for mathematically precise evaluation. Motivated by the successes of deep learning in mathematically well defined and extremely challenging tasks of chess, Go, and protein folding, we posit that deep learning methods can play a crucial role in solving core goals of coding theory: designing new (encoder, decoder) pairs that improve state of the art performance over canonical channel models. This manuscript surveys recent advances towards demonstrating this hypothesis, by focusing on strengthening a specific family of coding methods - sequential codes (convolutional codes) and codes that use them as basic building blocks (Turbo codes) - via deep learning methods. New state of the art results are derived on several canonical channels, including the AWGN channel with feedback.
Precisely constructed porous composites containing catalytically active nanoparticles can stabilize unstable nanoparticles, thus improving catalytic activity and longevity while preventing ...agglomeration of active nanoparticles. Herein, we report the confined incorporation of highly active metal nanoparticles within a metal–organic framework support and efficient catalytic performances in the reduction of organic pollutants, such as methylene blue (MB) and 4-nitrophenol (4-NP). UiO-66-based porous composites (M@UiO-66, M = Pt or Ag) containing well-dispersed metal nanoparticles are constructed via the one-step thermal treatment of UiO-66 implanted with metal ions (UiO-66/M n+, M n+ = Pt2+ or Ag+). The comprehensive features of M@UiO-66s, such as well-dispersed nanocatalysts, well-developed pores, and characteristic surface charges, expedite not only efficient but also selective catalytic activities in the reduction of MB or 4-NP, along with impressive recyclability.
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