On-Line Learning Theory of Soft Committee Machines with Correlated Hidden Units - Steepest Gradient Descent and Natural Gradient Descent -

The permutation symmetry of the hidden units in multilayer perceptrons causes the saddle structure and plateaus of the learning dynamics in gradient learning methods. The correlation of the weight vectors of hidden units in a teacher network is thought to affect this saddle structure, resulting in a prolonged learning time, but this mechanism is still unclear. In this paper, we discuss it with regard to soft committee machines and on-line learning using statistical mechanics. Conventional gradient descent needs more time to break the symmetry as the correlation of the teacher weight vectors rises. On the other hand, no plateaus occur with natural gradient descent regardless of the correlation for the limit of a low learning rate. Analytical results support these dynamics around the saddle point.Comment: 7 pages, 6 figure

Free oscillations in a beta-plane ocean

This paper is concerned with the free oscillations in a rectangular equatorial beta-plane model of large oceans with a free surface placed symmetrically on the equator. With the depth constant and the stratification horizontally uniform, the motion may be separated into vertical modes, each having free oscillations...

Editor\u27s Commentary: Gravitational circulation in straits and estuaries

For decades, people understood the rudiments of how idealized estuaries work: fresh water flows outward above saltier water that intrudes into the estuary. At the same time, mixing smooths out contrasts, so that the ultimate shallow outflow water is much saltier that the upstream riverine inflow had been. But, this vision is qualitative, so that it is hard to predict or diagnose flow structures or the outflow salinity. Hansen and Rattray provided a major step forward in an age before any sophisticated numerical models could deal simultaneously with the many competing effects..

A gap analysis of UK biobank publications reveals SNPs associated with intrinsic subtypes of breast cancer

Breast cancer is a multifaceted disease and a leading cause of cancer morbidity and mortality in females across the globe. In 2020 alone, 2.3 million women were diagnosed and 685,000 died of breast cancer worldwide. With the number of diagnoses projected to increase to 3 million per year by 2040 it is essential that new methods of detection and disease stratification are sought to decrease this global cancer burden. Although significant improvements have been made in breast cancer diagnosis and treatment, the prognosis of breast cancer remains poor in some patient groups (i.e. triple negative breast cancer), necessitating research into better patient stratification, diagnosis and drug discovery. The UK Biobank, a comprehensive biomedical and epidemiological database with a wide variety of multiomics data (genomics, proteomics, metabolomics) offers huge potential to uncover groundbreaking discoveries in breast cancer research leading to improved patient stratification. Combining genomic, proteomic, and metabolic profiles of breast cancer in combination with histological classification, can aid treatment decisions through accurate diagnosis and prognosis prediction of tumor behaviour. Here, we systematically reviewed PubMed publications reporting the analysis of UK Biobank data in breast cancer research. Our analysis of UK Biobank studies in the past five years identified 125 publications, of which 76 focussed on genomic data analysis. Interestingly, only two studies reported the analysis of metabolomics and proteomics data, with none performing multiomics analysis of breast cancer. A meta-analysis of the 76 publications identified 2870 genetic variants associated with breast cancer across 445 genes. Subtype analysis revealed differential genetic alteration in 13 of the 445 genes and the identification of 59 well-established breast cancer genes. in differential pathways. Pathway interaction analyses illuminated their involvement in general cancer biomolecular pathways (e.g. DNA damage repair, Gene expression). While our meta-analysis only measured genetic differences in breast cancer due to current usage of UK Biobank data, minimal multi-omics analyses have been performed and the potential for harnessing multi-omics strategies within the UK Biobank cohort holds promise for unravelling the biological signatures of distinct breast cancer subtypes further in the future.</p

Effects of friction and surface tide angle of incidence on the coastal generation of internal tides

For the generation of internal waves by long surface waves, the normal-mode equations and solutions that satisfy the boundary conditions in a two-layer system are found analytically. Frictional effects decrease the amplitude of an internal wave over the shelf, changing it from a standing wave to a wave that progresses coastward and decreases the interference on the amplitude of the offshore progressive wave traveling seaward. Model studies, using a two-layer system of fresh water and saline water in a 9.9-m-long channel, gave favorable results relative to the theoretical results

Noise, regularizers, and unrealizable scenarios in online learning from restricted training sets

We study the dynamics of on-line learning in multilayer neural networks where training examples are sampled with repetition and where the number of examples scales with the number of network weights. The analysis is carried out using the dynamical replica method aimed at obtaining a closed set of coupled equations for a set of macroscopic variables from which both training and generalization errors can be calculated. We focus on scenarios whereby training examples are corrupted by additive Gaussian output noise and regularizers are introduced to improve the network performance. The dependence of the dynamics on the noise level, with and without regularizers, is examined, as well as that of the asymptotic values obtained for both training and generalization errors. We also demonstrate the ability of the method to approximate the learning dynamics in structurally unrealizable scenarios. The theoretical results show good agreement with those obtained by computer simulations

Subjective Experiences of the Benefits and Key Elements of a Cognitive Behavioral Intervention Focused on Community Work Outcomes in Persons With Mental Illness

New research suggests that group-based cognitive behavioral therapy (CBT) may help improve employment outcomes in persons with mental illness, yet the effects and potential key elements facilitating change in such interventions are unclear. Using a mixed methods approach, this study examined the perspectives of persons with mental illness after participating in a pilot study of the “CBT for Work Success” intervention. Findings demonstrate that participants valued the intervention and perceived that it assisted them in achieving work goals. Therapeutic effects included improved self-efficacy, work motivation, enhanced sense of self as workers, and increased beliefs that work success is attainable. CBT for Work Success elements perceived to be important in facilitating work goals included cognitive restructuring, behavioral coping strategies, problem solving work barriers, meaningful reflection on oneself as a worker, and important factors associated with the group process. The authors discuss the implications of these findings and future research directions

Metabolic Profiling of Geobacter sulfurreducens during Industrial Bioprocess Scale-Up

During the industrial scale-up of bioprocesses it is important to establish that the biological system has not changed significantly when moving from small laboratory-scale shake flasks or culturing bottles to an industrially relevant production level. Therefore, during upscaling of biomass production for a range of metal transformations, including the production of biogenic magnetite nanoparticles by Geobacter sulfurreducens, from 100-ml bench-scale to 5-liter fermentors, we applied Fourier transform infrared (FTIR) spectroscopy as a metabolic fingerprinting approach followed by the analysis of bacterial cell extracts by gas chromatography-mass spectrometry (GC-MS) for metabolic profiling. FTIR results clearly differentiated between the phenotypic changes associated with different growth phases as well as the two culturing conditions. Furthermore, the clustering patterns displayed by multivariate analysis were in agreement with the turbidimetric measurements, which displayed an extended lag phase for cells grown in a 5-liter bioreactor (24 h) compared to those grown in 100-ml serum bottles (6 h). GC-MS analysis of the cell extracts demonstrated an overall accumulation of fumarate during the lag phase under both culturing conditions, coinciding with the detected concentrations of oxaloacetate, pyruvate, nicotinamide, and glycerol-3-phosphate being at their lowest levels compared to other growth phases. These metabolites were overlaid onto a metabolic network of G. sulfurreducens, and taking into account the levels of these metabolites throughout the fermentation process, the limited availability of oxaloacetate and nicotinamide would seem to be the main metabolic bottleneck resulting from this scale-up process. Additional metabolite-feeding experiments were carried out to validate the above hypothesis. Nicotinamide supplementation (1 mM) did not display any significant effects on the lag phase of G. sulfurreducens cells grown in the 100-ml serum bottles. However, it significantly improved the growth behavior of cells grown in the 5-liter bioreactor by reducing the lag phase from 24 h to 6 h, while providing higher yield than in the 100-ml serum bottles

Sex differences in colon cancer metabolism reveal a novel subphenotype

Women have a lower incidence of colorectal cancer (CRC) than men, however, they have a higher incidence of right-sided colon cancer (RCC). This is of concern as patients with RCC have the poorest clinical outcomes among all CRC patients. Aberrant metabolism is a known hallmark and therapeutic target for cancer. We propose that metabolic subphenotypes exist between CRCs due to intertumoral molecular and genomic variation, and differences in environmental milieu of the colon which vary between the sexes. Metabolomics analysis of patient colon tumors (n = 197) and normal tissues (n = 39) revealed sex-specific metabolic subphenotypes dependent on anatomic location. Tumors from women with RCC were nutrient-deplete, showing enhanced energy production to fuel asparagine synthesis and amino acid uptake. The clinical importance of our findings were further investigated in an independent data set from The Cancer Genomic Atlas, and demonstrated that high asparagine synthetase (ASNS) expression correlated with poorer survival for women. This is the first study to show a unique, nutrient-deplete metabolic subphenotype in women with RCC, with implications for tumor progression and outcomes in CRC patients