Random Hyper-parameter Search-Based Deep Neural Network for Power Consumption Forecasting

In this paper, we introduce a deep learning approach, based on feed-forward neural networks, for big data time series forecasting with arbitrary prediction horizons. We firstly propose a random search to tune the multiple hyper-parameters involved in the method perfor-mance. There is a twofold objective for this search: firstly, to improve the forecasts and, secondly, to decrease the learning time. Next, we pro-pose a procedure based on moving averages to smooth the predictions obtained by the different models considered for each value of the pre-diction horizon. We conduct a comprehensive evaluation using a real-world dataset composed of electricity consumption in Spain, evaluating accuracy and comparing the performance of the proposed deep learning with a grid search and a random search without applying smoothing. Reported results show that a random search produces competitive accu-racy results generating a smaller number of models, and the smoothing process reduces the forecasting error.Ministerio de Economía y Competitividad TIN2017-88209-C2-1-

On the possibility of a very light A^0 at low \tan\beta

The searches at LEP II for the processes e^+e^-\to h^0Z and e^+e^-\to h^0A^0 in the Minimal Supersymmetric Standard Model (MSSM) fail to exclude regions of the m_h,m_A plane where \tan\beta <1, thus allowing a very light A^0 (m_A< 20 GeV). Such a parameter choice would predict a light H^\pm with m_{H^\pm}< m_W. Although the potentially large branching ratio for H^\pm \to A^0 W^* would ensure that H^\pm also escaped detection in direct searches at LEP II and the Tevatron Run I, we show that this elusive parameter space is overwhelmingly disfavoured by electroweak precision measurements.Comment: 11 pages, 2 figures, Revtex, references added, minor additions to tex

A phase 1b study evaluating the safety and preliminary efficacy of berzosertib in combination with gemcitabine in patients with advanced non-small cell lung cancer

OBJECTIVES: Berzosertib (formerly M6620, VX-970) is an intravenous, highly potent and selective, first-in-class ataxia telangiectasia and Rad3-related (ATR) protein kinase inhibitor. We assessed the safety, tolerability, preliminary efficacy, and pharmacokinetics (PK) of berzosertib plus gemcitabine in an expansion cohort of patients with advanced non-small cell lung cancer (NSCLC). The association of efficacy with TP53 status and other tumor markers was also explored. MATERIALS AND METHODS: Adult patients with advanced histologically confirmed NSCLC received berzosertib 210 mg/m2 (days 2 and 9) and gemcitabine 1000 mg/m2 (days 1 and 8) at the recommended phase 2 dose established in the dose escalation part of the study. RESULTS: Thirty-eight patients received at least one dose of study treatment. The most common treatment-emergent adverse events were fatigue (55.3%), anemia (52.6%), and nausea (39.5%). Gemcitabine had no apparent effect on the PK of berzosertib. The objective response rate (ORR) was 10.5% (4/38, 90% confidence interval [CI]: 3.7–22.5%). In the exploratory analysis, the ORR was 30.0% (3/10, 90% CI: 9.0–61.0%) in patients with high loss of heterozygosity (LOH) and 11.0% (1/9, 90% CI: 1.0–43.0%) in patients with low LOH. The ORR was 33.0% (2/6, 90% CI: 6.0–73.0%) in patients with high tumor mutational burden (TMB), 12.5% (2/16, 90% CI: 2.0–34.0%) in patients with intermediate TMB, and 0% (0/3, 90% CI: 0.0–53.6%) in patients with low TMB. CONCLUSIONS: Berzosertib plus gemcitabine was well tolerated in patients with advanced, pre-treated NSCLC. Based on the observed clinical efficacy, future clinical trials should involve genomically selected patients

A Neural Network for Stance Phase detection in smart cane users

Slides from conferencePersons with disabilities often rely on assistive devices to carry on their Activities of Daily Living. Deploying sensors on these devices may provide continuous valuable knowledge on their state and condition. Canes are among the most frequently used assistive devices, regularly employed for ambulation by persons with pain on lower limbs and also for balance. Load on canes is reportedly a meaningful condition indicator. Ideally, it corresponds to the time cane users support weight on their lower limb (stance phase). However, in reality, this relationship is not straightforward. We present a Multilayer Perceptron to reliably predict the Stance Phase in cane users using a simple support detection module on commercial canes. The system has been successfully tested on five cane users in care facilities in Spain. It has been optimized to run on a low cost microcontroller.This work has been supported by: Proyectos Puente and programa operativo de empleo juvenil (UMAJI58) and Plan Propio de Investigación at University of Malaga and the Swedish Knowledge Foundation (KKS) through the research profile Embedded Sensor Systems for Health (ESS−H) at Malardalen University, Sweden. Authors would like to ac- knowledge PONIENTE and LOS NARANJOS senior centers for their support during the tests. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Intrinsic gain modulation and adaptive neural coding

In many cases, the computation of a neural system can be reduced to a receptive field, or a set of linear filters, and a thresholding function, or gain curve, which determines the firing probability; this is known as a linear/nonlinear model. In some forms of sensory adaptation, these linear filters and gain curve adjust very rapidly to changes in the variance of a randomly varying driving input. An apparently similar but previously unrelated issue is the observation of gain control by background noise in cortical neurons: the slope of the firing rate vs current (f-I) curve changes with the variance of background random input. Here, we show a direct correspondence between these two observations by relating variance-dependent changes in the gain of f-I curves to characteristics of the changing empirical linear/nonlinear model obtained by sampling. In the case that the underlying system is fixed, we derive relationships relating the change of the gain with respect to both mean and variance with the receptive fields derived from reverse correlation on a white noise stimulus. Using two conductance-based model neurons that display distinct gain modulation properties through a simple change in parameters, we show that coding properties of both these models quantitatively satisfy the predicted relationships. Our results describe how both variance-dependent gain modulation and adaptive neural computation result from intrinsic nonlinearity.Comment: 24 pages, 4 figures, 1 supporting informatio

Observation of a J^PC = 1-+ exotic resonance in diffractive dissociation of 190 GeV/c pi- into pi- pi- pi+

The COMPASS experiment at the CERN SPS has studied the diffractive dissociation of negative pions into the pi- pi- pi+ final state using a 190 GeV/c pion beam hitting a lead target. A partial wave analysis has been performed on a sample of 420000 events taken at values of the squared 4-momentum transfer t' between 0.1 and 1 GeV^2/c^2. The well-known resonances a1(1260), a2(1320), and pi2(1670) are clearly observed. In addition, the data show a significant natural parity exchange production of a resonance with spin-exotic quantum numbers J^PC = 1-+ at 1.66 GeV/c^2 decaying to rho pi. The resonant nature of this wave is evident from the mass-dependent phase differences to the J^PC = 2-+ and 1++ waves. From a mass-dependent fit a resonance mass of 1660 +- 10+0-64 MeV/c^2 and a width of 269+-21+42-64 MeV/c^2 is deduced.Comment: 7 page, 3 figures; version 2 gives some more details, data unchanged; version 3 updated authors, text shortened, data unchange

A novel TOPSIS–CBR goal programming approach to sustainable healthcare treatment

Cancer is one of the most common diseases worldwide and its treatment is a complex and time-consuming process. Specifically, prostate cancer as the most common cancer among male population has received the attentions of many researchers. Oncologists and medical physicists usually rely on their past experience and expertise to prescribe the dose plan for cancer treatment. The main objective of dose planning process is to deliver high dose to the cancerous cells and simultaneously minimize the side effects of the treatment. In this article, a novel TOPSIS case based reasoning goal-programming approach has been proposed to optimize the dose plan for prostate cancer treatment. Firstly, a hybrid retrieval process TOPSIS–CBR [technique for order preference by similarity to ideal solution (TOPSIS) and case based reasoning (CBR)] is used to capture the expertise and experience of oncologists. Thereafter, the dose plans of retrieved cases are adjusted using goal-programming mathematical model. This approach will not only help oncologists to make a better trade-off between different conflicting decision making criteria but will also deliver a high dose to the cancerous cells with minimal and necessary effect on surrounding organs at risk. The efficacy of proposed method is tested on a real data set collected from Nottingham City Hospital using leave-one-out strategy. In most of the cases treatment plans generated by the proposed method is coherent with the dose plan prescribed by an experienced oncologist or even better. Developed decision support system can assist both new and experienced oncologists in the treatment planning process

Hyper-parameter Optimisation by Restrained Stochastic Hill Climbing

Abstract. Machine learning practitioners often refer to hyper-parameter optimisation (HPO) as an art form and a skill that requires intuition and experience; Neuroevolution (NE) typically employs a combination of manual and evolutionary approaches for HPO. This paper explores the integration of a stochastic hill climbing approach for HPO within a NE algorithm. We empirically show that HPO by restrained stochastic hill climbing (HORSHC) is more effective than manual and pure evolutionary HPO. Empirical evidence is derived from a comparison of: (1) a NE algorithm that solely optimises hyper-parameters through evolution and (2) a number of derived algorithms with random search optimisation integration for optimising the hyper-parameters of a Neural Network. Through statistical analysis of the experimental results it has been revealed that random initialisation of hyper-parameters does not significantly affect the final performance of the Neural Networks evolved. However, HORSHC, a novel optimisation approach proposed in this paper has been proven to significantly out-perform the NE control algorithm. HORSHC presents itself as a solution that is computationally comparable in terms of both time and complexity as well as outperforming the control algorithm

Salivary Glucose Oxidase from Caterpillars Mediates the Induction of Rapid and Delayed-Induced Defenses in the Tomato Plant

Caterpillars produce oral secretions that may serve as cues to elicit plant defenses, but in other cases these secretions have been shown to suppress plant defenses. Ongoing work in our laboratory has focused on the salivary secretions of the tomato fruitworm, Helicoverpa zea. In previous studies we have shown that saliva and its principal component glucose oxidase acts as an effector by suppressing defenses in tobacco. In this current study, we report that saliva elicits a burst of jasmonic acid (JA) and the induction of late responding defense genes such as proteinase inhibitor 2 (Pin2). Transcripts encoding early response genes associated with the JA pathway were not affected by saliva. We also observed a delayed response to saliva with increased densities of Type VI glandular trichomes in newly emerged leaves. Proteomic analysis of saliva revealed glucose oxidase (GOX) was the most abundant protein identified and we confirmed that it plays a primary role in the induction of defenses in tomato. These results suggest that the recognition of GOX in tomato may represent a case for effector-triggered immunity. Examination of saliva from other caterpillar species indicates that saliva from the noctuids Spodoptera exigua and Heliothis virescens also induced Pin2 transcripts