Concurrent Classifier Error Detection (CCED) in Large Scale Machine Learning Systems

The complexity of Machine Learning (ML) systems increases each year, with current implementations of large language models or text-to-image generators having billions of parameters and requiring billions of arithmetic operations. As these systems are widely utilized, ensuring their reliable operation is becoming a design requirement. Traditional error detection mechanisms introduce circuit or time redundancy that significantly impacts system performance. An alternative is the use of Concurrent Error Detection (CED) schemes that operate in parallel with the system and exploit their properties to detect errors. CED is attractive for large ML systems because it can potentially reduce the cost of error detection. In this paper, we introduce Concurrent Classifier Error Detection (CCED), a scheme to implement CED in ML systems using a concurrent ML classifier to detect errors. CCED identifies a set of check signals in the main ML system and feeds them to the concurrent ML classifier that is trained to detect errors. The proposed CCED scheme has been implemented and evaluated on two widely used large-scale ML models: Contrastive Language Image Pretraining (CLIP) used for image classification and Bidirectional Encoder Representations from Transformers (BERT) used for natural language applications. The results show that more than 95 percent of the errors are detected when using a simple Random Forest classifier that is order of magnitude simpler than CLIP or BERT. These results illustrate the potential of CCED to implement error detection in large-scale ML models

CERKL regulates autophagy via the NAD-dependent deacetylase SIRT1

<p>Macroautophagy/autophagy is an important intracellular mechanism for the maintenance of cellular homeostasis. Here we show that the <i>CERKL</i> (ceramide kinase like) gene, a retinal degeneration (RD) pathogenic gene, plays a critical role in regulating autophagy by stabilizing SIRT1. <i>In vitro</i> and <i>in vivo</i>, suppressing CERKL results in impaired autophagy. SIRT1 is one of the main regulators of acetylation/deacetylation in autophagy. In CERKL-depleted retinas and cells, SIRT1 is downregulated. ATG5 and ATG7, 2 essential components of autophagy, show a higher degree of acetylation in CERKL-depleted cells. Overexpression of SIRT1 rescues autophagy in CERKL-depleted cells, whereas CERKL loses its function of regulating autophagy in SIRT1-depleted cells, and overexpression of CERKL upregulates SIRT1. Finally, we show that CERKL directly interacts with SIRT1, and may regulate its phosphorylation at Ser27 to stabilize SIRT1. These results show that CERKL is an important regulator of autophagy and it plays this role by stabilizing the deacetylase SIRT1.</p

Analysis of the Global Warming Potential of Biogenic CO2 Emission in Life Cycle Assessments

Biomass is generally believed to be carbon neutral. However, recent studies have challenged the carbon neutrality hypothesis by introducing metric indicators to assess the global warming potential of biogenic CO2 (GWPbio). In this study we calculated the GWPbio factors using a forest growth model and radiative forcing effects with a time horizon of 100 years and applied the factors to five life cycle assessment (LCA) case studies of bioproducts. The forest carbon change was also accounted for in the LCA studies. GWPbio factors ranged from 0.13–0.32, indicating that biomass could be an attractive energy resource when compared with fossil fuels. As expected, short rotation and fast-growing biomass plantations produced low GWPbio. Long-lived wood products also allowed more regrowth of biomass to be accounted as absorption of the CO2 emission from biomass combustion. The LCA case studies showed that the total life cycle GHG emissions were closely related to GWPbio and energy conversion efficiency. By considering the GWPbio factors and the forest carbon change, the production of ethanol and bio-power appeared to have higher GHG emissions than petroleum-derived diesel at the highest GWPbio

Decision ambiguity is mediated by a late positive potential originating from cingulate cortex

People often make decisions in the face of ambiguous information, but it remains unclear how ambiguity is represented in the brain. We used three types of ambiguous stimuli and combined EEG and fMRI to examine the neural representation of perceptual decisions under ambiguity. We identified a late positive potential, the LPP, which differentiated levels of ambiguity, and which was specifically associated with behavioral judgments about choices that were ambiguous, rather than passive perception of ambiguous stimuli. Mediation analyses together with two further control experiments confirmed that the LPP was generated only when decisions are made (not during mere perception of ambiguous stimuli), and only when those decisions involved choices on a dimension that is ambiguous. A further control experiment showed that a stronger LPP arose in the presence of ambiguous stimuli compared to when only unambiguous stimuli were present. Source modeling suggested that the LPP originated from multiple loci in cingulate cortex, a finding we further confirmed using fMRI and fMRI-guided ERP source prediction. Taken together, our findings argue for a role of an LPP originating from cingulate cortex in encoding decisions based on task-relevant perceptual ambiguity, a process that may in turn influence confidence judgment, response conflict, and error correction

Peran Gaya Kepemimpinan Transformasional Memoderasi Pengaruh Motivasi Intrinsik dan Kecerdasan Emosional terhadap Kinerja Guru (Studi Kasus pada SMA Negeri di Kecamatan Pati Kabupaten Pati)

This research is intended to examine the influence of motivation intrinsic and emotional intelligence to the state senior high school teachers\u27 work performance with the moderation of transformational leadership style. The specific purpose of this research is to examine the role of transformational leadership style moderates the influence of intrinsic motivation and emotional intelligence to the teachers\u27 work performance. The USAge of this research is to explain and expand the previous research about the role of transformational leadership style moderates the influence of intrinsic motivation and emotional intelligence to the teachers\u27 work performance. This research used the population of 116 teachers of state senior high school in Pati District, Pati Regency. The technique of sample collection used in this research is non-probability sampling with the purposive method. The analysis technique used in this research is regression model moderate quasi. Based on the research result can be conduded that: intrinsic motivation influences teachers\u27 work performance, emotional intelligence influences the teachers\u27 work performance, transformational leadership style do not moderate the influence of intrinsic motivation to teachers\u27 work performance, , transformational leadership style strengthen the influence of emotional intelligence to the teachers\u27 workperformance

Pterostilbene Activates the Nrf2-Dependent Antioxidant Response to Ameliorate Arsenic-Induced Intracellular Damage and Apoptosis in Human Keratinocytes

The NF-E2 p45-related factor 2 (Nrf2), a transcription factor that regulates the cellular adaptive response to oxidative stress, is a target for limiting tissue damage from exposure to environmental toxins, including arsenic. In the current study, we determine whether Pterostilbene (Pts), as a potent activator of Nrf2, has a protective effect on arsenic-induced cytotoxicity and apoptosis in human keratinocytes. Human keratinocytes (HaCaT) or mouse epidermal cells (JB6) were pretreated with Pts for 24 h prior to arsenic treatment. Harvested cells were analyzed by MTT, DCFH-DA, commercial kits, Flow cytometry assay and western blot analysis. Our results demonstrated that Pts effectively regulated the viability in HaCaT and JB6 cells, decreased the reactive oxygen species (ROS) generation and lipid peroxidation (MDA), and improved the NaAsO2-induced depletion of superoxide dismutase (SOD). Moreover, Pts treatment further dramatically inhibited NaAsO2-induced apoptosis, specifically the mitochondrial mediation of apoptosis, which coincided with the effective recovery of NaAsO2-induced mitochondrial membrane potential (ΔΨm) depolarization and cytochrome c release from the mitochondria. Furthermore, arsenic-induced decrease of anti-apoptotic factor Bcl-2 and Bcl-xl, and increase of pro-apoptotic factor Bax and Bad, as well as survival signal related factor caspase 3 activation were reversed by Pts treatment. Further mechanistic studies confirmed that Pts increased antioxidant enzyme expression in a dose-dependent manner, which was related to Nrf2 nuclear translocation. In addition, the effects of Pts on NaAsO2-induced cell viability were largely weakened when Nrf2 was knocked down. Together, our results provide evidence for the use of Pts to activate the Nrf2 pathway to alleviate arsenic-induced dermal damage

Artificial Intelligence-Enabled ECG Algorithm Based on Improved Residual Network for Wearable ECG

Heart disease is the leading cause of death for men and women globally. The residual network (ResNet) evolution of electrocardiogram (ECG) technology has contributed to our understanding of cardiac physiology. We propose an artificial intelligence-enabled ECG algorithm based on an improved ResNet for a wearable ECG. The system hardware consists of a wearable ECG with conductive fabric electrodes, a wireless ECG acquisition module, a mobile terminal App, and a cloud diagnostic platform. The algorithm adopted in this study is based on an improved ResNet for the rapid classification of different types of arrhythmia. First, we visualize ECG data and convert one-dimensional ECG signals into two-dimensional images using Gramian angular fields. Then, we improve the ResNet-50 network model, add multistage shortcut branches to the network, and optimize the residual block. The ReLu activation function is replaced by a scaled exponential linear units (SELUs) activation function to improve the expression ability of the model. Finally, the images are input into the improved ResNet network for classification. The average recognition rate of this classification algorithm against seven types of arrhythmia signals (atrial fibrillation, atrial premature beat, ventricular premature beat, normal beat, ventricular tachycardia, atrial tachycardia, and sinus bradycardia) is 98.3%

Physics perspectives of heavy-ion collisions at very high energy

Heavy-ion collisions at very high colliding energies are expected to produce a quark-gluon plasma (QGP) at the highest temperature obtainable in a laboratory setting. Experimental studies of these reactions can provide an unprecedented range of information on properties of the QGP at high temperatures. We report theoretical investigations of the physics perspectives of heavy-ion collisions at a future high-energy collider. These include initial parton production, collective expansion of the dense medium, jet quenching, heavy-quark transport, dissociation and regeneration of quarkonia, photon and dilepton production. We illustrate the potential of future experimental studies of the initial particle production and formation of QGP at the highest temperature to provide constraints on properties of strongly interaction matter.Comment: 35 pages in Latex, 29 figure

The human amygdala parametrically encodes the intensity of specific facial emotions and their categorical ambiguity

The human amygdala is a key structure for processing emotional facial expressions, but it remains unclear what aspects of emotion are processed. We investigated this question with three different approaches: behavioural analysis of 3 amygdala lesion patients, neuroimaging of 19 healthy adults, and single-neuron recordings in 9 neurosurgical patients. The lesion patients showed a shift in behavioural sensitivity to fear, and amygdala BOLD responses were modulated by both fear and emotion ambiguity (the uncertainty that a facial expression is categorized as fearful or happy). We found two populations of neurons, one whose response correlated with increasing degree of fear, or happiness, and a second whose response primarily decreased as a linear function of emotion ambiguity. Together, our results indicate that the human amygdala processes both the degree of emotion in facial expressions and the categorical ambiguity of the emotion shown and that these two aspects of amygdala processing can be most clearly distinguished at the level of single neurons