Network Evolution Induced by the Dynamical Rules of Two Populations

We study the dynamical properties of a finite dynamical network composed of two interacting populations, namely; extrovert ($a$) and introvert ($b$). In our model, each group is characterized by its size ($N_a$ and $N_b$) and preferred degree ($\kappa_a$ and $\kappa_b\ll\kappa_a$). The network dynamics is governed by the competing microscopic rules of each population that consist of the creation and destruction of links. Starting from an unconnected network, we give a detailed analysis of the mean field approach which is compared to Monte Carlo simulation data. The time evolution of the restricted degrees \moyenne{k_{bb}} and \moyenne{k_{ab}} presents three time regimes and a non monotonic behavior well captured by our theory. Surprisingly, when the population size are equal $N_a=N_b$, the ratio of the restricted degree \theta_0=\moyenne{k_{ab}}/\moyenne{k_{bb}} appears to be an integer in the asymptotic limits of the three time regimes. For early times (defined by $t<t_1=\kappa_b$) the total number of links presents a linear evolution, where the two populations are indistinguishable and where $\theta_0=1$. Interestingly, in the intermediate time regime (defined for $t_1<t<t_2\propto\kappa_a$ and for which $\theta_0=5$), the system reaches a transient stationary state, where the number of contacts among introverts remains constant while the number of connections is increasing linearly in the extrovert population. Finally, due to the competing dynamics, the network presents a frustrated stationary state characterized by a ratio $\theta_0=3$.Comment: 21 pages, 6 figure

A generative adversarial network for single and multi-hop distributional knowledge base completion

Knowledge bases (KBs) inherently lack reasoning ability, limiting their effectiveness for tasks such as question-answering and query expansion. Machine-learning is hence commonly employed for representation learning in order to learn semantic features useful for generalization. Most existing methods utilize discriminative models that require both positive and negative samples to learn a decision boundary. KBs, by contrast, contain only positive samples, necessitating that negative samples are generated by replacing the head/tail of predicates with randomly-chosen entities. They are thus frequently easily discriminable from positive samples, which can prevent learning of sufficiently robust classifiers. Generative models, however, do not require negative samples to learn the distribution of positive samples; stimulated by recent developments in Generative Adversarial Networks (GANs), we propose a novel framework, Knowledge Completion GANs (KCGANs), for competitively training generative link prediction models against discriminative belief prediction models. KCGAN thus invokes a game between generator-network G and discriminator-networkD in which G aims to understand underlying KB structure by learning to perform link prediction while D tries to gain knowledge about the KB by learning predicate/triplet classification. Two key challenges are addressed: 1) Classical GAN architectures’ inability to easily generate samples over discrete entities; 2) the inefficiency of softmax for learning distributions over large sets of entities. As a step toward full first-order logical reasoning we further extend KCGAN to learn multi-hop logical entailment relations between entities by enabling G to compose a multi-hop relational path between entities and D to discriminate between real and fake paths. KCGAN is tested on benchmarks WordNet and FreeBase datasets and evaluated on link prediction and belief prediction tasks using MRR and HIT@10, achieving best-in-class performance

A generative adversarial strategy for modeling relation paths in knowledge base representation learning

Enabling neural networks to perform multi-hop (mh) reasoning over knowledge bases (KBs) is vital for tasks such as question-answering and query expansion. Typically, recurrent neural networks (RNNs) trained with explicit objectives are used to model mh relation paths (mh-RPs). In this work, we hypothesize that explicit objectives are not the most effective strategy effective for learning mh-RNN reasoning models, proposing instead a generative adversarial network (GAN) based approach. The proposed model – mh Relation GAN (mh-RGAN) – consists of two networks; a generator $G$, and discriminator $D$. $G$ is tasked with composing a mh-RP and $D$ with discriminating between real and fake paths. During training, $G$ and $D$ contest each other adversarially as follows: $G$ attempts to fool $D$ by composing an indistinguishably invalid mh-RP given a head entity and a relation, while $D$ attempts to discriminate between valid and invalid reasoning chains until convergence. The resulting model is tested on benchmarks WordNet and FreeBase datasets and evaluated on the link prediction task using MRR and HIT@ 10, achieving best-in-class performance in all cases

Multi-view convolutional recurrent neural networks for lung cancer nodule identification

Screening via low-dose Computer Tomography (CT) has been shown to reduce lung cancer mortality rates by at least 20%. However, the assessment of large numbers of CT scans by radiologists is cost intensive, and potentially produces varying and inconsistent results for differing radiologists (and also for temporally-separated assessments by the same radiologist). To overcome these challenges, computer aided diagnosis systems based on deep learning methods have proved an effective in automatic detection and classification of lung cancer. Latterly, interest has focused on the full utilization of the 3D information in CT scans using 3D-CNNs and related approaches. However, such approaches do not intrinsically correlate size and shape information between slices. In this work, an innovative approach to Multi-view Convolutional Recurrent Neural Networks (MV-CRecNet) is proposed that exploits shape, size and cross-slice variations while learning to identify lung cancer nodules from CT scans. The multiple-views that are passed to the model ensure better generalization and the learning of robust features. We evaluate the proposed MV-CRecNet model on the reference Lung Image Database Consortium and Image Database Resource Initiative and Early Lung Cancer Action Program datasets; six evaluation metrics are applied to eleven comparison models for testing. Results demonstrate that proposed methodology outperforms all of the models against all of the evaluation metrics

The Effects of Next-Nearest-Neighbor Interactions on the Orientation Dependence of Step Stiffness: Reconciling Theory with Experiment for Cu(001)

Within the solid-on-solid (SOS) approximation, we carry out a calculation of the orientational dependence of the step stiffness on a square lattice with nearest and next-nearest neighbor interactions. At low temperature our result reduces to a simple, transparent expression. The effect of the strongest trio (three-site, non pairwise) interaction can easily be incorporated by modifying the interpretation of the two pairwise energies. The work is motivated by a calculation based on nearest neighbors that underestimates the stiffness by a factor of 4 in directions away from close-packed directions, and a subsequent estimate of the stiffness in the two high-symmetry directions alone that suggested that inclusion of next-nearest-neighbor attractions could fully explain the discrepancy. As in these earlier papers, the discussion focuses on Cu(001).Comment: 8 pages, 3 figures, submitted to Phys. Rev.

VANT-GAN: adversarial learning for discrepancy-based visual attribution in medical imaging

Visual attribution (VA) in relation to medical images is an essential aspect of modern automation-assisted diagnosis. Since it is generally not straightforward to obtain pixel-level ground-truth labelling of medical images, classification-based interpretation approaches have become the de facto standard for automated diagnosis, in which the ability of classifiers to make categorical predictions based on class-salient regions is harnessed within the learning algorithm. Such regions, however, typically constitute only a small subset of the full range of features of potential medical interest. They may hence not be useful for VA of medical images where capturing all of the disease evidence is a critical requirement. This hence motivates the proposal of a novel strategy for visual attribution that is not reliant on image classification. We instead obtain normal counterparts of abnormal images and find discrepancy maps between the two. To perform the abnormal-to-normal mapping in unsupervised way, we employ a Cycle-Consistency Generative Adversarial Network, thereby formulating visual attribution in terms of a discrepancy map that, when subtracted from the abnormal image, makes it indistinguishable from the counterpart normal image. Experiments are performed on three datasets including a synthetic, Alzheimer’s disease Neuro imaging Initiative and, BraTS dataset. We outperform baseline and related methods in both experiments

FITOPLANKTON SEBAGAI BIOINDIKATOR PENCEMARAN ORGANIK DI PERAIRAN SUNGAI MUSI BAGIAN HILIR SUMATRA SELATAN

Sungai Musi merupakan sungai terbesar dan terpanjang di Sumatra Selatan. Berkembangnya kegiatan penduduk di Daerah Aliran Sungai (DAS) Musi dapat berpengaruh terhadap kualitas air sungai dan dapat menyebabkan terjadinya pencemaran. Tingginya aktivitas industri maupun rumah tangga di sepanjang Sungai Musi menyebabkan menurunnya kualitas lingkungan di DAS Musi. Berdasarkan hal tersebut, maka perlu dilakukan penelitian lebih lanjut untuk mengetahui seberapa besar tingkat pencemaran yang terjadi di DAS Musi. Tujuan dari penelitian ini adalah untuk mengkaji dan mengetahui tingkat saprobitas di sepanjang DAS Musi bagian hilir berdasarkan nilai SI (Saprobik Indeks), serta mengetahui tingkat pencemaran air menggunakan penilaian saprobitas perairan. Penelitian ini menggunakan plankton sebagai bioindikator pencemaran organik perairan. Penelitian ini menggunakan rancangan eksplorasi dengan metode survei, dan penetapan stasiun pengambilan sampel dengan metode purposive sampling. Hasil penelitian menunjukkan kelimpahan fitoplankton di perairan Sungai Musi pada rentang 123-2581 sel/liter atau rata-rata sebesar 1397 sel/liter. Indeks Saprobik di perairan Sungai Musi berkisar antara 0,63-1, digolongkan pada fasesaprobik, yaitu β-Mesosaprobik, sehingga pada perairan Sungai Musi digolongkan pada tingkat pencemaran ringan.The Musi River is the largest and longest river in South Sumatra. The development of population activities in the Musi River Basin can affect river water quality and can cause pollution. The high level of industrial activity and households along the Musi River causes a decrease in environmental quality in the Musi River Basin. The declining quality of aquatic environment can be seen from the presence of phytoplankton. Based on this, further research is needed to determine the extent of pollution in the Musi River Basin. The purpose of this study is to assess saprobitas along the Musi River Basin based on SI (Saprobic Index) value and knowing the level of water pollution using saprobitas water assessment. This study uses plankton as a bioindicator of aquatic organic pollution. This study uses an exploratory design with survey methods, and the determination of sampling stations by purposive sampling method. The results showed abundance of phytoplankton in the waters of the Musi River in the range of 123 to 2581 cells.liter-1 or an average of 1397 cells.liter-1. The Saprobic index in the waters of the Musi River ranges from 0.631 to 1, classified in the phases of the microbial, namely β-Mesosaprobic, so that the waters of the Musi River are classified as mild