A framework for the local information dynamics of distributed computation in complex systems

The nature of distributed computation has often been described in terms of the component operations of universal computation: information storage, transfer and modification. We review the first complete framework that quantifies each of these individual information dynamics on a local scale within a system, and describes the manner in which they interact to create non-trivial computation where "the whole is greater than the sum of the parts". We describe the application of the framework to cellular automata, a simple yet powerful model of distributed computation. This is an important application, because the framework is the first to provide quantitative evidence for several important conjectures about distributed computation in cellular automata: that blinkers embody information storage, particles are information transfer agents, and particle collisions are information modification events. The framework is also shown to contrast the computations conducted by several well-known cellular automata, highlighting the importance of information coherence in complex computation. The results reviewed here provide important quantitative insights into the fundamental nature of distributed computation and the dynamics of complex systems, as well as impetus for the framework to be applied to the analysis and design of other systems.Comment: 44 pages, 8 figure

Richness of Deep Echo State Network Dynamics

Reservoir Computing (RC) is a popular methodology for the efficient design of Recurrent Neural Networks (RNNs). Recently, the advantages of the RC approach have been extended to the context of multi-layered RNNs, with the introduction of the Deep Echo State Network (DeepESN) model. In this paper, we study the quality of state dynamics in progressively higher layers of DeepESNs, using tools from the areas of information theory and numerical analysis. Our experimental results on RC benchmark datasets reveal the fundamental role played by the strength of inter-reservoir connections to increasingly enrich the representations developed in higher layers. Our analysis also gives interesting insights into the possibility of effective exploitation of training algorithms based on stochastic gradient descent in the RC field.Comment: Preprint of the paper accepted at IWANN 201

Negative Refraction Angular Characterization in One-Dimensional Photonic Crystals

Background: Photonic crystals are artificial structures that have periodic dielectric components with different refractive indices. Under certain conditions, they abnormally refract the light, a phenomenon called negative refraction. Here we experimentally characterize negative refraction in a one dimensional photonic crystal structure; near the low frequency edge of the fourth photonic bandgap. We compare the experimental results with current theory and a theory based on the group velocity developed here. We also analytically derived the negative refraction correctness condition that gives the angular region where negative refraction occurs. Methodology/Principal Findings: By using standard photonic techniques we experimentally determined the relationship between incidence and negative refraction angles and found the negative refraction range by applying the correctness condition. In order to compare both theories with experimental results an output refraction correction was utilized. The correction uses Snell’s law and an effective refractive index based on two effective dielectric constants. We found good agreement between experiment and both theories in the negative refraction zone. Conclusions/Significance: Since both theories and the experimental observations agreed well in the negative refraction region, we can use both negative refraction theories plus the output correction to predict negative refraction angles. This can be very useful from a practical point of view for space filtering applications such as a photonic demultiplexer or fo

Assessment of a novel smartglass-based point-of-care fusion approach for mixed reality-assisted targeted prostate biopsy: A pilot proof-of-concept study

PurposeWhile several biopsy techniques and platforms for magnetic resonance imaging (MRI)-guided targeted biopsy of the prostate have been established, none of them has proven definite superiority. Augmented and virtual reality (mixed reality) smartglasses have emerged as an innovative technology to support image-guidance and optimize accuracy during medical interventions. We aimed to investigate the benefits of smartglasses for MRI-guided mixed reality-assisted cognitive targeted biopsy of the prostate.MethodsFor prospectively collected patients with suspect prostate PIRADS lesions, multiparametric MRI was uploaded to a smartglass (Microsoft® Hololens I), and smartglass-assisted targeted biopsy (SMART TB) of the prostate was executed by generation of a cognitive fusion technology at the point-of-care. Detection rates of prostate cancer (PCA) were compared between SMART TB and 12-core systematic biopsy. Assessment of SMART-TB was executed by the two performing surgeons based on 10 domains on a 10-point scale ranging from bad (1) to excellent (10).ResultsSMART TB and systematic biopsy of the prostate were performed for 10 patients with a total of 17 suspect PIRADS lesions (PIRADS 3, n = 6; PIRADS 4, n = 6; PIRADS 5, n = 5). PCA detection rate per core was significant (p < 0.05) higher for SMART TB (47%) than for systematic biopsy (19%). Likelihood for PCA according to each core of a PIRADS lesion (17%, PIRADS 3; 58%, PIRADS 4; 67%, PIRADS 5) demonstrated convenient accuracy. Feasibility scores for SMART TB were high for practicality (10), multitasking (10), execution speed (9), comfort (8), improvement of surgery (8) and image quality (8), medium for physical stress (6) and device handling (6) and low for device weight (5) and battery autonomy (4).ConclusionSMART TB has the potential to increase accuracy for PCA detection and might enhance cognitive MRI-guided targeted prostate biopsy in the future

IMPLEMENTASI PEMBERIAN GANTI KERUGIAN LAYANAN PAKET DI PT TIKI JALUR NUGRAHA EKAKURIR (JNE) CABANG SURAKARTA DITINJAU DARI UNDANG-UNDANG NOMOR 38 TAHUN 2009 TENTANG POS

ABSTRAK Yusuf Bintang Syaifinuha, E0012414. 2016. IMPLEMENTASI PEMBERIAN GANTI KERUGIAN LAYANAN PAKET DI PT TIKI JALUR NUGRAHA EKAKURIR (JNE) CABANG SURAKARTA DITINJAU DARI UNDANG-UNDANG NOMOR 38 TAHUN 2009 TENTANG POS. Fakultas Hukum Universitas Sebelas Maret Surakarata. Penelitian ini bertujuan untuk mengetahui implementasi pemberian ganti kerugian di PT Tiki Jalur Nugraha Ekakurir (JNE) Cabang Surakarta ditinjau dari UU Pos dan untuk mengetahui cara penyelesaian sengketa yang timbul dari implementasi pemberian ganti kerugian di PT Tiki Jalur Nugraha Ekakurir (JNE) Cabang Surakarta. Metode penelitian yang digunakan dalam penelitian ini adalah deskriptif kualitatif. Jenis penelitian ini adalah penelitian empiris. Data yang digunakan terdiri dari dua data yaitu data primer dan data sekunder. Teknik pengumpulan data adalah dengan metode wawancara dan studi pustaka. Berdasarkan hasil analisis penelitian, dapat diambil kesimpulan bahwa implementasi pemberian ganti kerugian di JNE kurang sesuai dengan Pasal 28 UU Pos karena jenis ganti kerugian hanya untuk kehilangan kiriman dan kerusakan isi kiriman. Pengirim yang mengajukan klaim ganti kerugian harus memenuhi syarat administrasi yang telah ditetapkan oleh JNE. Nilai ganti kerugian yang diberikan JNE adalah 10 kali biaya pengiriman atau sesuai harga barang yang hilang dan/atau rusak jika menggunakan asuransi. JNE memilih upaya hukum diluar pengadilan (nonlitigasi) berupa negosiasi dalam menyelesaikan sengketa yang terjadi dalam implementasi pemberian ganti kerugian. Kata kunci :Perjanjian, Wanprestasi, Implementasi ganti kerugian

Deep Randomized Neural Networks

Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of weights inherently characterizes the class of Randomized Neural Networks with a number of intriguing features. Among them, the extreme efficiency of the resulting learning processes is undoubtedly a striking advantage with respect to fully trained architectures. Besides, despite the involved simplifications, randomized neural systems possess remarkable properties both in practice, achieving state-of-the-art results in multiple domains, and theoretically, allowing to analyze intrinsic properties of neural architectures (e.g. before training of the hidden layers’ connections). In recent years, the study of Randomized Neural Networks has been extended towards deep architectures, opening new research directions to the design of effective yet extremely efficient deep learning models in vectorial as well as in more complex data domains. This chapter surveys all the major aspects regarding the design and analysis of Randomized Neural Networks, and some of the key results with respect to their approximation capabilities. In particular, we first introduce the fundamentals of randomized neural models in the context of feed-forward networks (i.e., Random Vector Functional Link and equivalent models) and convolutional filters, before moving to the case of recurrent systems (i.e., Reservoir Computing networks). For both, we focus specifically on recent results in the domain of deep randomized systems, and (for recurrent models) their application to structured domains

Deep Randomized Neural Networks

Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of weights inherently characterizes the class of Randomized Neural Networks with a number of intriguing features. Among them, the extreme efficiency of the resulting learning processes is undoubtedly a striking advantage with respect to fully trained architectures. Besides, despite the involved simplifications, randomized neural systems possess remarkable properties both in practice, achieving state-of-the-art results in multiple domains, and theoretically, allowing to analyze intrinsic properties of neural architectures (e.g. before training of the hidden layers' connections). In recent years, the study of Randomized Neural Networks has been extended towards deep architectures, opening new research directions to the design of effective yet extremely efficient deep learning models in vectorial as well as in more complex data domains. This chapter surveys all the major aspects regarding the design and analysis of Randomized Neural Networks, and some of the key results with respect to their approximation capabilities. In particular, we first introduce the fundamentals of randomized neural models in the context of feed-forward networks (i.e., Random Vector Functional Link and equivalent models) and convolutional filters, before moving to the case of recurrent systems (i.e., Reservoir Computing networks). For both, we focus specifically on recent results in the domain of deep randomized systems, and (for recurrent models) their application to structured domains

Incident type 2 diabetes attributable to suboptimal diet in 184 countries

The global burden of diet-attributable type 2 diabetes (T2D) is not well established. This risk assessment model estimated T2D incidence among adults attributable to direct and body weight-mediated effects of 11 dietary factors in 184 countries in 1990 and 2018. In 2018, suboptimal intake of these dietary factors was estimated to be attributable to 14.1 million (95% uncertainty interval (UI), 13.814.4 million) incident T2D cases, representing 70.3% (68.871.8%) of new cases globally. Largest T2D burdens were attributable to insufficient whole-grain intake (26.1% (25.027.1%)), excess refined rice and wheat intake (24.6% (22.327.2%)) and excess processed meat intake (20.3% (18.323.5%)). Across regions, highest proportional burdens were in central and eastern Europe and central Asia (85.6% (83.487.7%)) and Latin America and the Caribbean (81.8% (80.183.4%)); and lowest proportional burdens were in South Asia (55.4% (52.160.7%)). Proportions of diet-attributable T2D were generally larger in men than in women and were inversely correlated with age. Diet-attributable T2D was generally larger among urban versus rural residents and higher versus lower educated individuals, except in high-income countries, central and eastern Europe and central Asia, where burdens were larger in rural residents and in lower educated individuals. Compared with 1990, global diet-attributable T2D increased by 2.6 absolute percentage points (8.6 million more cases) in 2018, with variation in these trends by world region and dietary factor. These findings inform nutritional priorities and clinical and public health planning to improve dietary quality and reduce T2D globally. (c) 2023, The Author(s)