Passively Mobile Communicating Logarithmic Space Machines

We propose a new theoretical model for passively mobile Wireless Sensor Networks. We call it the PALOMA model, standing for PAssively mobile LOgarithmic space MAchines. The main modification w.r.t. the Population Protocol model is that agents now, instead of being automata, are Turing Machines whose memory is logarithmic in the population size n. Note that the new model is still easily implementable with current technology. We focus on complete communication graphs. We define the complexity class PLM, consisting of all symmetric predicates on input assignments that are stably computable by the PALOMA model. We assume that the agents are initially identical. Surprisingly, it turns out that the PALOMA model can assign unique consecutive ids to the agents and inform them of the population size! This allows us to give a direct simulation of a Deterministic Turing Machine of O(nlogn) space, thus, establishing that any symmetric predicate in SPACE(nlogn) also belongs to PLM. We next prove that the PALOMA model can simulate the Community Protocol model, thus, improving the previous lower bound to all symmetric predicates in NSPACE(nlogn). Going one step further, we generalize the simulation of the deterministic TM to prove that the PALOMA model can simulate a Nondeterministic TM of O(nlogn) space. Although providing the same lower bound, the important remark here is that the bound is now obtained in a direct manner, in the sense that it does not depend on the simulation of a TM by a Pointer Machine. Finally, by showing that a Nondeterministic TM of O(nlogn) space decides any language stably computable by the PALOMA model, we end up with an exact characterization for PLM: it is precisely the class of all symmetric predicates in NSPACE(nlogn).Comment: 22 page

Gradient boosting models for photovoltaic power estimation under partial shading conditions

The energy yield estimation of a photovoltaic (PV) system operating under partially shaded conditions is a challenging task and a very active area of research. In this paper, we attack this problem with the aid of machine learning techniques. Using data simulated by the equivalent circuit of a PV string operating under partial shading, we train and evaluate three different gradient boosted regression tree models to predict the global maximum power point (MPP). Our results show that all three approaches improve upon the state-of-the-art closed-form estimates, in terms of both average and worst-case performance. Moreover, we show that even a small number of training examples is sufficient to achieve improved global MPP estimation. The methods proposed are fast to train and deploy and allow for further improvements in performance should more computational resources be available

Autoencoder-based multimodal prediction of non-small cell lung cancer survival

The ability to accurately predict non-small cell lung cancer (NSCLC) patient survival is crucial for informing physician decision-making, and the increasing availability of multi-omics data offers the promise of enhancing prognosis predictions. We present a multimodal integration approach that leverages microRNA, mRNA, DNA methylation, long non-coding RNA (lncRNA) and clinical data to predict NSCLC survival and identify patient subtypes, utilizing denoising autoencoders for data compression and integration. Survival performance for patients with lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) was compared across modality combinations and data integration methods. Using The Cancer Genome Atlas data, our results demonstrate that survival prediction models combining multiple modalities outperform single modality models. The highest performance was achieved with a combination of only two modalities, lncRNA and clinical, at concordance indices (C-indices) of 0.69 ± 0.03 for LUAD and 0.62 ± 0.03 for LUSC. Models utilizing all five modalities achieved mean C-indices of 0.67 ± 0.04 and 0.63 ± 0.02 for LUAD and LUSC, respectively, while the best individual modality performance reached C-indices of 0.64 ± 0.03 for LUAD and 0.59 ± 0.03 for LUSC. Analysis of biological differences revealed two distinct survival subtypes with over 900 differentially expressed transcripts

Critical Life Experiences that Mold a Person into a Global Scholar

Global Scholar Toni Fuss Kirkwood Tucker shares her experiences in Nazi Germany. This column contains an excerpt of Toni's presentation her award luncheon

Prevention of fish photobacteriosis. Comparison of the efficacy of intraperitoneally administered commercial and experimental vaccines

Two commercial multivalent vaccines against vibriosis, caused by Vibrio anguillarum serotype(s) and photobacteriosis, caused by Photobacterium damsela subsp. piscicida, one with oil adjuvant (AJ) and the other,being an aqueous solution (AV), and an experimental monovalent (Ph. damselae subsp. piscicida) vaccine inactivated with formalin or heat, namely EVF and EVH, were tested in laboratory trials on sea bass (Dicentrarchus labrax) in respect to their efficacy against experimentally induced photobacteriosis. The first trial aiming at high bacterial pressure was carried out 34 days post-vaccination and resulted in 90% mortalities in the control. The relative per cent survival (RPS) of vaccinated fish was calculated at 24, 3.7, 0 and 0 for the AJ, AV, EVF and EVH formulations, respectively. The second trial aiming at medium bacterial pressure was carried out 49 days post-vaccination and resulted in 45% mortalities in the control. The relative per cent survival (RPS) of vaccinated fish was calculated at 100, 92.7, 77.8 and 66.7 for the AJ, EVF, EVH and AV, formulations, respectively. Apparently, under both these high and medium bacterial pressure conditions, the commercial vaccine AJ performed better than the commercial vaccine AV, while under medium pressure there was no statistical difference between the performance of EVF and AJ. The measurement of specific antibody titers in sera collected from all fish groups 49 days post-vaccination, showed high levels in the fish vaccinated with the AJ vaccine, almost three times lower levels for the AV and EVF vaccines and even lower levels for the EVH vaccine. Results are discussed in respect to the choices mariculture companies have in selecting a commercial vaccine against photobacteriosis and possible alternatives, which, if commercially developed, may reduce vaccine cost

Immunotherapy of Cancer: Developments and Reference Points, an Unorthodox Approach.

Oncology is currently a sector of medical science with accelerated progress due to rapid technological development, the advancement in molecular biology, and the invention of many innovative therapies. Immunotherapy partially accounts for this advance, since it is increasingly playing an important role in the treatment of cancer patients, bringing on a sense of hope and optimism through a series of clinical studies and cases with spectacular results. Immunotherapy, after the initial successes it experienced in the early 20th century, was forgotten after chemotherapy and radiotherapy prevailed and developed slowly in the background. Today, it is the new hope for cancer treatment, despite the unorthodox path it has followed. In this article, we study the course and key points of the discovery of immune-oncology from the oncologist's point of view. We also record the questions that have been posed about immunotherapy that sometimes lead to confusion or stalemate

Community perceptions of local enterprises in environmentally degraded areas

Local enterprises can play a key role in the economic development of communities in which they are situated but simultaneously, they are often a contributor to negative impacts on the natural environment. Several studies have highlighted the importance of Corporate Social Responsibility (CSR) activities in order to strike a balance between socio-economic and environmental impacts in such communities. However, there is very limited literature exploring community perceptions of local businesses. We consider this to be a key topic as such information can be used in order to develop socio-economic and environmental policies based on the principles of sustainable development. In this paper, the results of an empirical study examining local community perceptions of business activities are presented and also perceptions regarding the contribution of firms, through CSR actions, to environmental quality restoration. The empirical study was conducted in communities located in the environmentally degraded area of the Asopos river in Greece

Peeking inside the Black Box: Interpreting Deep-learning Models for Exoplanet Atmospheric Retrievals

Deep-learning algorithms are growing in popularity in the field of exoplanetary science due to their ability to model highly nonlinear relations and solve interesting problems in a data-driven manner. Several works have attempted to perform fast retrievals of atmospheric parameters with the use of machine-learning algorithms like deep neural networks (DNNs). Yet, despite their high predictive power, DNNs are also infamous for being "black boxes." It is their apparent lack of explainability that makes the astrophysics community reluctant to adopt them. What are their predictions based on? How confident should we be in them? When are they wrong, and how wrong can they be? In this work, we present a number of general evaluation methodologies that can be applied to any trained model and answer questions like these. In particular, we train three different popular DNN architectures to retrieve atmospheric parameters from exoplanet spectra and show that all three achieve good predictive performance. We then present an extensive analysis of the predictions of DNNs, which can inform us–among other things–of the credibility limits for atmospheric parameters for a given instrument and model. Finally, we perform a perturbation-based sensitivity analysis to identify to which features of the spectrum the outcome of the retrieval is most sensitive. We conclude that, for different molecules, the wavelength ranges to which the DNNs predictions are most sensitive do indeed coincide with their characteristic absorption regions. The methodologies presented in this work help to improve the evaluation of DNNs and to grant interpretability to their predictions

Peeking inside the Black Box: Interpreting Deep-learning Models for Exoplanet Atmospheric Retrievals

Deep-learning algorithms are growing in popularity in the field of exoplanetary science due to their ability to model highly nonlinear relations and solve interesting problems in a data-driven manner. Several works have attempted to perform fast retrievals of atmospheric parameters with the use of machine-learning algorithms like deep neural networks (DNNs). Yet, despite their high predictive power, DNNs are also infamous for being "black boxes." It is their apparent lack of explainability that makes the astrophysics community reluctant to adopt them. What are their predictions based on? How confident should we be in them? When are they wrong, and how wrong can they be? In this work, we present a number of general evaluation methodologies that can be applied to any trained model and answer questions like these. In particular, we train three different popular DNN architectures to retrieve atmospheric parameters from exoplanet spectra and show that all three achieve good predictive performance. We then present an extensive analysis of the predictions of DNNs, which can inform us–among other things–of the credibility limits for atmospheric parameters for a given instrument and model. Finally, we perform a perturbation-based sensitivity analysis to identify to which features of the spectrum the outcome of the retrieval is most sensitive. We conclude that, for different molecules, the wavelength ranges to which the DNNs predictions are most sensitive do indeed coincide with their characteristic absorption regions. The methodologies presented in this work help to improve the evaluation of DNNs and to grant interpretability to their predictions