### SWAG: Item Recommendations using Convolutions on Weighted Graphs. (arXiv:1911.10232v1 [cs.IR])

Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. In this work, we present a Graph Convolutional Network (GCN) algorithm SWAG (Sample Weight and AGgregate), which combines efficient random walks and graph convolutions on weighted graphs to generate embeddings for nodes (items) that incorporate both graph…

### Dynamic Control of a Fiber Manufacturing Process using Deep Reinforcement Learning. (arXiv:1911.10286v1 [eess.SY])

This paper presents a deep reinforcement learning (DRL) approach for controlling a compact fiber drawing system. The compact fiber drawing system is smaller and less expensive than industrial draw towers. It is suitable for prototyping novel variable diameter polymer fibers. A controller for the system was developed using DRL. Especially, we focused on regulating the…

### New Transceiver Designs for Interleaved Frequency Division Multiple Access. (arXiv:1911.10303v1 [eess.SP])

This paper puts forth a class of new transceiver designs for interleaved frequency division multiple access (IFDMA) systems. These transceivers are significantly less complex than conventional IFDMA transceiver. The simple new designs are founded on a key observation that multiplexing and demultiplexing of IFDMA data streams of different sizes are coincident with the IFFTs and…

### PlantDoc: A Dataset for Visual Plant Disease Detection. (arXiv:1911.10317v1 [cs.CV])

India loses 35% of the annual crop yield due to plant diseases. Early detection of plant diseases remains difficult due to the lack of lab infrastructure and expertise. In this paper, we explore the possibility of computer vision approaches for scalable and early plant disease detection. The lack of availability of sufficiently large-scale non-lab data…

### Interarea Oscillations & Chimera in Power Systems. (arXiv:1911.10338v1 [eess.SY])

This paper proposes a novel second order mathematical model in the Kuramoto framework to simulate and study low frequency oscillations in power systems. This model facilitates better understanding of the complex dynamics of a power network. A standard four generator power system with all-to-all connectivity is considered and results obtained from the proposed model are…

### On Topological Classification of Morse-Smale Diffeomorphisms on the Sphere $S^n$. (arXiv:1911.10234v1 [math.DS])

We consider a class $G(S^n)$ of orientation preserving Morse-Smale diffeomorphisms of the sphere $S^{n}$ of dimension $n>3$ in assumption that invariant manifolds of different saddle periodic points have no intersection. We put in a correspondence for every diffeomorphism $f\in G(S^n)$ a colored graph $\Gamma_f$ enriched by an automorphism $P_f$. Then we define the notion of…

### Sequentially congruent partitions and partitions into squares. (arXiv:1911.10236v1 [math.NT])

In recent work, M. Schneider and the first author studied a curious class of integer partitions called “sequentially congruent” partitions: the $m$th part is congruent to the $(m+1)$th part modulo $m$, with the smallest part congruent to zero modulo the number of parts. Let $p_{\mathcal S}(n)$ be the number of sequentially congruent partitions of $n,$…

### Hull and Geodetic Numbers for Some Classes of Oriented Graphs. (arXiv:1911.10240v1 [math.CO])

An oriented graph $D$ is an orientation of a simple graph, i.e. a directed graph whose underlying graph is simple. A directed path from $u$ to $v$ with minimum number of arcs in $D$ is an $(u,v)$-geodesic, for every $u,v\in V(D)$. A set $S \subseteq V(D)$ is (geodesically) convex if, for every \$ u,v \in…

### Optimal-order preconditioners for the Morse-Ingard equations. (arXiv:1911.10247v1 [math.NA])

The Morse-Ingard equations of thermoacoustics are a system of coupled time-harmonic equations for the temperature and pressure of an excited gas. They form a critical aspect of modeling trace gas sensors. In this paper, we analyze a reformulation of the system that has a weaker coupling between the equations than the original form. We give…

### 3rd-order Spectral Representation Method: Part II — Ergodic Multi-variate random processes with fast Fourier transform. (arXiv:1911.10251v1 [math.ST])

The second in a two-part series, this paper extends the 3rd-order Spectral Representation Method for simulation of ergodic multi-variate stochastic processes according to a prescribed cross power spectral density and cross bispectral density. The 2nd and 3rd order ensemble properties of the simulated stochastic vector processes are shown to satisfy the target cross correlation properties…