When and where do feed-forward neural networks learn localist representations?

According to parallel distributed processing (PDP) theory in psychology, neural networks (NN) learn distributed rather than interpretable localist representations. This view has been held so strongly that few researchers have analysed single units to determine if this assumption is correct. However, recent results from psychology, neuroscience and computer science have shown the occasional existence of local codes emerging in artificial and biological neural networks. In this paper, we undertake the first systematic survey of when local codes emerge in a feed-forward neural network, using generated input and output data with known qualities. We find that the number of local codes that emerge from a NN follows a well-defined distribution across the number of hidden layer neurons, with a peak determined by the size of input data, number of examples presented and the sparsity of input data. Using a 1-hot output code drastically decreases the number of local codes on the hidden layer. The number of emergent local codes increases with the percentage of dropout applied to the hidden layer, suggesting that the localist encoding may offer a resilience to noisy networks. This data suggests that localist coding can emerge from feed-forward PDP networks and suggests some of the conditions that may lead to interpretable localist representations in the cortex. The findings highlight how local codes should not be dismissed out of hand

Underdetermined blind source separation based on Fuzzy C-Means and Semi-Nonnegative Matrix Factorization

Conventional blind source separation is based on over-determined with more sensors than sources but the underdetermined is a challenging case and more convenient to actual situation. Non-negative Matrix Factorization (NMF) has been widely applied to Blind Source Separation (BSS) problems. However, the separation results are sensitive to the initialization of parameters of NMF. Avoiding the subjectivity of choosing parameters, we used the Fuzzy C-Means (FCM) clustering technique to estimate the mixing matrix and to reduce the requirement for sparsity. Also, decreasing the constraints is regarded in this paper by using Semi-NMF. In this paper we propose a new two-step algorithm in order to solve the underdetermined blind source separation. We show how to combine the FCM clustering technique with the gradient-based NMF with the multi-layer technique. The simulation results show that our proposed algorithm can separate the source signals with high signal-to-noise ratio and quite low cost time compared with some algorithms

Photoprotective Effect of Sunscreen Cream with Addition of Carrageenan and Black Mangrove Fruit (Rhizopora Mucronata Lamk.)

The need of people\u27s face protection from sun exposure is continuosly increasing. However, the available sunscreen in the market are mostly chemicaly generated. Seaweed (Kappaphycus alvarezii) produced carrageenan, which can be used as stabilizer, thickener, and emulsifier on sunscreen production. Black mangrove fruit (R. mucronata) contains an antioxidant activity, tanin, flavonoids, and phenolic compounds, which are potential to be used for UV light absorber as well as skin protector. The aims of this research were to determine: (1) the best carrageenan concentration in the cream; (2) content of total phenol, flavonoids, and tannin of mangrove fruit extract; and (3) Sun Protection Factor (SPF) value of sunscreen cream. The experiment used a complete random design and with Duncan test. The result showed that the best natural sunscreen formulation was an addition of 0.5% carrageenan and 1% extract from R. mucronata. The best characteristics of natural sunscreen were found within the level of 7.62 pH, 38.250 cP viscosity, 100% emulsion stability, 3.72% shrinkage, and <102 colonies/g total microbial, 10.21 ± 0.06 SPF content, 37.90% total phenol, 0.51% total flavonoids, and 6.20 mg/g tannins

Determination of Optical Constants for Titan Aerosol-, and Exoplanet and Brown Dwarf Cloud Particle Analogs from the Visible to the Far Infrared

Here we present optical constants covering a broad wavelength range, from the visible to the far infrared, for Titan aerosol analogs produced in the Titan Haze Simulation (THS) experiment at Ames COSmIC facility, as well as other exoplanet-relevant materials

Ecological Conditions and Economic Values of Coral Reef Flats in Mattiro Deceng Village, Badi Island, Pangkajenne Kepulauan Regency, South Sulawesi

Penelitian ini dilakukan di Perairan Pulau Badi Desa Mattiro Deceng Kabupaten Pangkajenne Kepulauan, Sulawesi Selatan dengan tujuan untuk: 1. Mengetahui kondisi ekologi ekosistem terumbu karang pada lokasi penelitian. 2. Mengetahui nilai ekonomi ekosistem terumbu karang pada lokasi penelitian. Untuk menentukan kondisi ekologi terumbu karang akan di peroleh dengan menggunakan metode Line Intercept Transek (LIT) pada 3 lokasi berbeda yaitu inner reef, middle reef, dan outer reef, dimana setiap biota bentik yang dilewati transek akan dicatat menurut bentuk pertumbuhannya. Sedangkan untuk memperoleh nilai ekonomi total dilakukan wawancara kepada masyarakat yang beraktivitas di ekosistem terumbu karang dengan menggunakan metode Purposive Sampling. Kondisi terumbu karang Pulau Badi pada inner reef (48,62%) termasuk dalam kategori sedang dan pada middle reef ( 64,10%) dan outer reef (50,01%) termasuk dalam kategori baik. Nilai total ekonomi ekosistem terumbu karang Pulau Badi Desa Mattiro Deceng sebesar Rp. 10.567.286.000/tahun, dimana untuk nilai manfaat langsung sebesar Rp. 9.213.714.286/tahun untuk nilai manfaat tidak langsung sebesar Rp. 1.353.572.000/tahun

Are there any ‘object detectors’ in the hidden layers of CNNs trained to identify objects or scenes?

Various methods of measuring unit selectivity have been developed with the aim of better understanding how neural networks work. But the different measures provide divergent estimates of selectivity, and this has led to different conclusions regarding the conditions in which selective object representations are learned and the functional relevance of these representations. In an attempt to better characterize object selectivity, we undertake a comparison of various selectivity measures on a large set of units in AlexNet, including localist selectivity, precision, class-conditional mean activity selectivity (CCMAS), network dissection,the human interpretation of activation maximization (AM) images, and standard signal-detection measures. We find that the different measures provide different estimates of object selectivity, with precision and CCMAS measures providing misleadingly high estimates. Indeed, the most selective units had a poor hit-rate or a high false-alarm rate (or both) in object classification, making them poor object detectors. We fail to find any units that are even remotely as selective as the 'grandmother cell' units reported in recurrent neural networks. In order to generalize these results, we compared selectivity measures on units in VGG-16 and GoogLeNet trained on the ImageNet or Places-365 datasets that have been described as 'object detectors'. Again, we find poor hit-rates and high false-alarm rates for object classification. We conclude that signal-detection measures provide a better assessment of single-unit selectivity compared to common alternative approaches, and that deep convolutional networks of image classification do not learn object detectors in their hidden layers.Comment: Published in Vision Research 2020, 19 pages, 8 figure

Protein Engineering of a Spectroscopic Probe into Malate Dehydrogenase (MDH)

Malate dehydrogenase (MDH) is an enzyme that has a key role in biological processes, like the Krebs cycle. Specifically, it reversibly catalyzes the interconversion of (S)-malate with NAD+ to oxaloacetate and NADH. Once oxaloacetate is synthesized, MDH dispatches it to citrate synthase, but it is not clear how this happens. One theory is that MDH channels it to citrate synthase by forming a metabolon, a mechanism for direct channeling, preventing diffusion of reaction intermediates into a bulk matrix. There is a lack of research in this area due to the absence of a spectroscopic probe necessary to visualize MDH’s conformational changes. Therefore, a method was tested to incorporate a fluorescent landmark into MDH’s structure and thus be used in future research to reveal the interactions between MDH and citrate synthase. Specific amino acids of MDH were mutated to tryptophan, an amino acid known to fluoresce (V189, I319, A120, I136, P119, G218). The coding sequence for the wildtype MDH and mutant MDHs were incorporated into plasmids and bacterially transformed into Escherichia coli. Both wildtype and mutant proteins were over-expressed, then purified by nickel affinity chromatography using a hexahistidine tag on the N-terminus of MDH. Data will demonstrate that I139W, V189W, and A120W had significantly lower activity than wildtype MDH, and the same is predicted for I136W. I139W and V189W emitted fluorescence at 290 nm, but I136W did not. The mutations P119W and G218W could not be overexpressed or purified. Next steps in design of a fluorescent, active MDH will be discussed

Stellar Image Interpretation System using Artificial Neural Networks: Unipolar Function Case

An artificial neural network based system for interpreting astronomical images has been developed. The system is based on feed-forward Artificial Neural Networks (ANNs) with error back-propagation learning. Knowledge about images of stars, cosmic ray events and noise found in images is used to prepare two sets of input patterns to train and test our approach. The system has been developed and implemented to scan astronomical digital images in order to segregate stellar images from other entities. It has been coded in C language for users of personal computers. An astronomical image of a star cluster from other objects is undertaken as a test case. The obtained results are found to be in very good agreement with those derived from the DAOPHOTII package, which is widely used in the astronomical community. It is proved that our system is simpler, much faster and more reliable. Moreover, no prior knowledge, or initial data from the frame to be analysed is required

Factors associated with deliberate self-harm among Irish adolescents

Background. Deliberate self-harm (DSH) is a major public health problem, with young people most at risk. Lifetime prevalence of DSH in Irish adolescents is between 8% and 12%, and it is three times more prevalent among girls than boys. The aim of the study was to identify the psychological, lifestyle and life event factors associated with self-harm in Irish adolescents. Method. A cross-sectional study was conducted, with 3,881 adolescents in 39 schools completing an anonymous questionnaire as part of the Child and Adolescent Self-harm in Europe (CASE) study. There was an equal gender balance and 53.1% of students were 16 years old. Information was obtained on history of self-harm life events, and demographic, psychological and lifestyle factors. Results. Based on multi-variate analyses, important factors associated with DSH among both genders were drug use and knowing a friend who had engaged in self-harm. Among girls, poor self-esteem, forced sexual activity, self-harm of a family member, fights with parents and problems with friendships also remained in the final model. For boys, experiencing bullying, problems with schoolwork, impulsivity, and anxiety remained. Conclusions. Distinct profiles of boys and girls who engage in self-harm were identified. Associations between DSH and some lifestyle and life event factors suggest that mental health factors are not the sole indicators of risk of self-harm. The importance of school-related risk factors underline the need to develop gender-specific initiatives in schools to reduce the prevalence of self-harm