Neuron Clustering for Mitigating Catastrophic Forgetting in Supervised and Reinforcement Learning

Neural networks have had many great successes in recent years, particularly with the advent of deep learning and many novel training techniques. One issue that has affected neural networks and prevented them from performing well in more realistic online environments is that of catastrophic forgetting. Catastrophic forgetting affects supervised learning systems when input samples are temporally correlated or are non-stationary. However, most real-world problems are non-stationary in nature, resulting in prolonged periods of time separating inputs drawn from different regions of the input space. Reinforcement learning represents a worst-case scenario when it comes to precipitating catastrophic forgetting in neural networks. Meaningful training examples are acquired as the agent explores different regions of its state/action space. When the agent is in one such region, only highly correlated samples from that region are typically acquired. Moreover, the regions that the agent is likely to visit will depend on its current policy, suggesting that an agent that has a good policy may avoid exploring particular regions. The confluence of these factors means that without some mitigation techniques, supervised neural networks as function approximation in temporal-difference learning will be restricted to the simplest test cases. This work explores catastrophic forgetting in neural networks in terms of supervised and reinforcement learning. A simple mathematical model is introduced to argue that catastrophic forgetting is a result of overlapping representations in the hidden layers in which updates to the weights can affect multiple unrelated regions of the input space. A novel neural network architecture, dubbed cluster-select, is introduced which utilizes online clustering for the selection of a subset of hidden neurons to be activated in the feedforward and backpropagation stages. Clusterselect is demonstrated to outperform leading techniques in both classification nd regression. In the context of reinforcement learning, cluster-select is studied for both fully and partially observable Markov decision processes and is demonstrated to converge faster and behave in a more stable manner when compared to other state-of-the-art algorithms

Analytical and numerical monotonicity results for discrete fractional sequential differences with negative lower bound

We investigate the relationship between the sign of the discrete fractional sequential difference(Δv1+a-μ Δaμf)(t) and the monotonicity of the function t→f(t). More precisely, we consider the special case in which this fractional difference can be negative and satisfies the lower bound (Δv1+a-μ Δaμf)(t) ≥ -εf(a), for some ε \u3e0. We prove that even though the fractional difference can be negative, the monotonicity of the function f, nonetheless, is still implied by the above inequality. This demonstrates a significant dissimilarity between the fractional and non-fractional cases. Because of the challenges of a purely analytical approach, our analysis includes numerical simulation

Analytical and numerical convexity results for discrete fractional sequential differences with negative lower bound

We investigate relationships between the sign of the discrete fractional sequential difference (Δv 1+a-μ Δμaf)(t) and the convexity of the function t→f(t). In particular, we consider the case in which the bound (Δv 1+a-μ Δμaf)(t) ≥εf(a), for some ε \u3e 0 and where f(a) \u3c 0 is satisfied. Thus, we allow for the case in which the sequential difference may be negative, and we show that even though the fractional difference can be negative, the convexity of the function f can be implied by the above inequality nonetheless. This demonstrates a significant dissimilarity between the fractional and non-fractional cases. We use a combination of both hard analysis and numerical simulation

Computational Ranking of Yerba Mate Small Molecules Based on Their Predicted Contribution to Antibacterial Activity against Methicillin-Resistant Staphylococcus aureus

The aqueous extract of yerba mate, a South American tea beverage made from Ilex paraguariensis leaves, has demonstrated bactericidal and inhibitory activity against bacterial pathogens, including methicillin-resistant Staphylococcus aureus (MRSA). The gas chromatography-mass spectrometry (GC-MS) analysis of two unique fractions of yerba mate aqueous extract revealed 8 identifiable small molecules in those fractions with antimicrobial activity. For a more comprehensive analysis, a data analysis pipeline was assembled to prioritize compounds for antimicrobial testing against both MRSA and methicillin-sensitive S.aureus using forty-two unique fractions of the tea extract that were generated in duplicate, assayed for activity, and analyzed with GC-MS. As validation of our automated analysis, we checked our predicted active compounds for activity in literature references and used authentic standards to test for antimicrobial activity. 3,4-dihydroxybenzaldehyde showed the most antibacterial activity against MRSA at low concentrations in our bioassays. In addition, quinic acid and quercetin were identified using random forests analysis and 5-hydroxy pipecolic acid was identified using linear discriminant analysis. We also generated a ranked list of unidentified compounds that may contribute to the antimicrobial activity of yerba mate against MRSA. Here we utilized GC-MS data to implement an automated analysis that resulted in a ranked list of compounds that likely contribute to the antimicrobial activity of aqueous yerba mate extract against MRSA

Evidence for binarity in the bipolar planetary nebulae A79, He2-428 and M1-91

We present low and high resolution long-slit spectra of three bipolar planetary nebulae (PNe) with bright central cores: A79, He2-428 and M1-91. He2-428 and M1-91 have high density (from 10^3.3 to 10^6.5 cm^-3) unresolved nebular cores that indicate that strong mass loss/exchange phenomena are occurring close to their central stars. An F0 star is found at the centre of symmetry of A79; its reddening and distance are consistent with the association of the star with the nebula. The spectrum of the core of He2-428 shows indications of the presence of a hot star with red excess emission, probably arising in a late-type companion. A79 is one of the richest PNe in N and He, the abundances of M1-91 are at the lower end of the range spanned by bipolar PNe, and He2-428 shows very low abundances, similar to those measured for halo PNe. The extended nebulae of A79 and He2-428 have inclined equatorial rings expanding at a velocity of approx. 15 km/s, with kinematical ages 10^4 yr. The association of these aged, extended nebulae with a dense nebular core (He2-428) or a relatively late type star (A79) is interpreted as evidence for the binarity of their nuclei.Comment: 13 pages including 8 tables. A&A accepted; also available at http://www.iac.es/publicaciones/preprints.htm

Biliary reconstructive techniques and associated anatomic variants in adult living donor liver transplantations: The adult‐to‐adult living donor liver transplantation cohort study experience

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140020/1/lt24872.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/140020/2/lt24872_am.pd

Streaming Readout and Data-Stream Processing With ERSAP

With the exponential growth in the volume and complexity of data generated at high-energy physics and nuclear physics research facilities, there is an imperative demand for innovative strategies to process this data in real or near-real-time. Given the surge in the requirement for high-performance computing, it becomes pivotal to reassess the adaptability of current data processing architectures in integrating new technologies and managing streaming data. This paper introduces the ERSAP framework, a modern solution that synergizes flow-based programming with the reactive actor model, paving the way for distributed, reactive, and high performance in data stream processing applications. Additionally, we unveil a novel algorithm focused on time-based clustering and event identification in data streams. The efficacy of this approach is further exemplified through the data-stream processing outcomes obtained from the recent beam tests of the EIC prototype calorimeter at DESY

Identification of the Transgenic Integration Site in Immunodeficient tgε26 Human CD3ε Transgenic Mice

A strain of human CD3ε transgenic mice, tgε26, exhibits severe immunodeficiency associated with early arrest of T cell development. Complete loss of T cells is observed in homozygous tgε26 mice, but not in heterozygotes, suggesting that genomic disruption due to transgenic integration may contribute to the arrest of T cell development. Here we report the identification of the transgenic integration site in tgε26 mice. We found that multiple copies of the human CD3ε transgene are inserted between the Sstr5 and Metrn loci on chromosome 17, and that this is accompanied by duplication of the neighboring genomic region spanning 323 kb. However, none of the genes in this region were abrogated. These results suggest that the severe immunodeficiency seen in tgε26 mice is not due to gene disruption resulting from transgenic integration