Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconfigurable Intelligent Surfaces Aided mmWave NOMA: Joint Power Allocation, Phase Shifts, and Hybrid Beamforming Optimization

In this paper, a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) non-orthogonal multiple access (NOMA) system is analyzed. In particular, we consider an RIS-aided mmWave-NOMA downlink system with a hybrid beamforming structure. To maximize the achievable sum-rate under a minimum rate constraint for the users and a maximum transmit power constraint, a joint RIS phase shifts, hybrid beamforming, and power allocation problem is formulated. To solve this non-convex optimization problem, we develop an alternating optimization (AO) algorithm. Specifically, first, the non-convex problem is transformed into three subproblems, i.e., power allocation, joint phase shifts and analog beamforming optimization, and digital beamforming design. Then, we solve the power allocation problem by keeping fixed the phase shifts of the RIS and the hybrid beamforming. Finally, given the power allocation matrix, an alternating manifold optimization (AMO)-based method and a successive convex approximation (SCA)-based method are utilized to design the phase shifts, analog beamforming, and transmit beamforming, respectively. Numerical results reveal that the proposed AO algorithm outperforms existing schemes in terms of sum-rate. Moreover, compared to a conventional mmWave-NOMA system without RIS, the proposed RIS-aided mmWave-NOMA system is capable of improving the achievable sum-rate.
Exact first-passage time distributions for three random diffusivity models

We study the extremal properties of a stochastic process x t defined by a Langevin equation ẋt=2D0V(Bt)ξt , where ξ t is a Gaussian white noise with zero mean, D 0 is a constant scale factor, and V(B t ) is a stochastic ‘diffusivity’ (noise strength), which itself is a functional of independent Brownian motion B t . We derive exact, compact expressions in one and three dimensions for the probability density functions (PDFs) of the first passage time (FPT) t from a fixed location x 0 to the origin for three different realisations of the stochastic diffusivity: a cut-off case V(B t ) = Θ(B t ) (model I), where Θ(z) is the Heaviside theta function; a geometric Brownian motion V(B t ) = exp(B t ) (model II); and a case with V(Bt)=Bt2 (model III). We realise that, rather surprisingly, the FPT PDF has exactly the Lévy–Smirnov form (specific for standard Brownian motion) for model II, which concurrently exhibits a strongly anomalous diffusion. For models I and III either the left or right tails (or both) have a different functional dependence on time as compared to the Lévy–Smirnov density. In all cases, the PDFs are broad such that already the first moment does not exist. Similar results are obtained in three dimensions for the FPT PDF to an absorbing spherical target.
Quantitative Propagation of Chaos for SGD in Wide Neural Networks

In this paper, we investigate the limiting behavior of a continuous-time counterpart of the Stochastic Gradient Descent (SGD) algorithm applied to two-layer overparameterized neural networks, as the number or neurons (ie, the size of the hidden layer) $N \to +\infty$. Following a probabilistic approach, we show 'propagation of chaos' for the particle system defined by this continuous-time dynamics under different scenarios, indicating that the statistical interaction between the particles asymptotically vanishes. In particular, we establish quantitative convergence with respect to $N$ of any particle to a solution of a mean-field McKean-Vlasov equation in the metric space endowed with the Wasserstein distance. In comparison to previous works on the subject, we consider settings in which the sequence of stepsizes in SGD can potentially depend on the number of neurons and the iterations. We then identify two regimes under which different mean-field limits are obtained, one of them corresponding to an implicitly regularized version of the minimization problem at hand. We perform various experiments on real datasets to validate our theoretical results, assessing the existence of these two regimes on classification problems and illustrating our convergence results.
A Novel Dimension Reduction Scheme for Intrusion Detection Systems in IoT Environments

Internet of Things (IoT) brings new challenges to the security solutions of computer networks. So far, intrusion detection system (IDS) is one of the effective security tools, but the vast amount of data that is generated by heterogeneous protocols and "things" alongside the constrained resources of the hosts, make some of the present IDS schemes defeated. To grant IDSs the ability of working in the IoT environments, in this paper, we propose a new distributed dimension reduction scheme which addresses the limited resources challenge. A novel autoencoder (AE) designed, and it learns to generate a latent space. Then, the constrained hosts/probes use the generated weights to lower the dimension with a single operation. The compressed data is transferred to a central IDS server to verify the traffic type. This scheme aims to lower the needed bandwidth to transfer data by compressing it and also reduce the overhead of the compression task in the hosts. The proposed scheme is evaluated on three well-known network traffic datasets (UNSW-NB15, TON\_IoT20 and NSL-KDD), and the results show that we can have a 3-dimensional latent space (about 90\% compression) without any remarkable fall in IDS detection accuracy.
Chern-Dold character in complex cobordisms and abelian varieties

We show that the theta divisors of general principally polarised abelian varieties can be chosen as smooth irreducible algebraic representatives of the coefficients of the Chern-Dold character in complex cobordisms and describe the action on them of the Landweber-Novikov operations. The link with Milnor-Hirzebruch problem about algebraic representatives in the complex cobordisms is discussed.
Competition in Electric Autonomous Mobility-on-Demand Systems

This article investigates the impacts of competition in autonomous mobility-on-demand systems. By adopting a network-flow based formulation, we first determine the optimal strategies of profit-maximizing platform operators in monopoly and duopoly markets, including the optimal prices of rides. Furthermore, we characterize the platform operator’s profits and the consumer surplus. We show that for the duopoly, the equilibrium prices for rides have to be symmetric between the firms. Then, in order to study the benefits of introducing competition in the market, we derive universal theoretical bounds on the ratio of prices for rides, aggregate demand served, profits of the firms, and consumer surplus between the monopolistic and the duopolistic setting. We discuss how consumers’ firm loyalty affects each of the aforementioned metrics. Finally, using the Manhattan network and demand data, we quantify the efficacy of static pricing and routing policies and compare it to real-time model predictive policies.
Nondeterministic Automata and JSL-dfas

We introduce the category of dependency automata. A dependency automaton consists of two nondeterministic finite automata, with a relation between their states satisfying conditions. This category is equivalent to deterministic finite automata interpreted in join-semilattices i.e. JSL-dfas. The canonical dependency automaton accepting $L$ amounts to the state-minimal dfas for $L$ and $rev(L)$ connected by the `dependency relation'. We describe many canonical JSL-dfas as dependency automata and also explain/extend Brzozowski's algorithm. Call an nfa `subatomic' if its individual states accept a language in the closure of $\{L\}$ under left/right quotients and set-theoretic boolean operations. We prove an nfa $N$ is subatomic iff $rsc(rev(N))$'s transition monoid is syntactic.
Impact of poultry extension services for the rural women

A research in poultry extension services was conducted between August, 2010 to February, 2011 to assess the impact of poultry extension services on the rural women. Participation of rural women is very little to register in the official papers of government in spite of their role in the farm and non-farm activities. All these activities are much more restricted by rural customs, traditions and values etc. Okara district of the Punjab province was selected as universe of the study area. A number of 750 respondents were involved in the data. The data was collected with the help of pre-test and validated interview schedule. The paper recommends a fair treatment to the rural women and calls for social upliftment by enhancing education, employment, training, and poultry production and management. It was concluded that a large majority of the rural women showed an excellent interest in gaining agricultural extension education services related to poultry production. Key words: Agricultural extension services, poultry, rural women, education.
Functions with average smoothness: structure, algorithms, and learning

We initiate a program of average-smoothness analysis for efficiently learning real-valued functions on metric spaces. Rather than using the (global) Lipschitz constant as the regularizer, we define a local slope at each point and gauge the function complexity as the average of these values. Since the average is often much smaller than the maximum, this complexity measure can yield considerably sharper generalization bounds --- assuming that these admit a refinement where the global Lipschitz constant is replaced by our average of local slopes. Our first major contribution is to obtain just such distribution-sensitive bounds. This required overcoming a number of technical challenges, perhaps the most significant of which was bounding the {\em empirical} covering numbers, which can be much worse-behaved than the ambient ones. This in turn is based on a novel Lipschitz-type extension, which is a pointwise minimizer of the local slope, and may be of independent interest. Our combinatorial results are accompanied by efficient algorithms for denoising the random sample, as well as guarantees that the extension from the sample to the whole space will continue to be, with high probability, smooth on average. Along the way we discover a surprisingly rich combinatorial and analytic structure in the function class we define.
Hongo endomicorrízico y bacteria fijadora de nitrógeno inoculadas a Coffea arabica en vivero

Conel objetivo de determinar el efecto de la inoculacion con Azospirillum brasilense y Glomus intraradice en cafe, se establecio la presente investigacion en un vivero en el Campo Experimental Rosario Izapa, Chiapas, Mexico de septiembre de 2005 a abril de 2006. Se utilizo un suelo Andosol–molicotipico de la region cafetalera del Soconusco Chiapas, Mexico. El A. brasilense tuvo una concentracion de 100 x106 bacterias por gramo de turba y el hongo micorrizico, cuarenta esporas por gramo de suelo con infeccion de 95%en el sistema radical de cebolla. Los tratamientos utilizados fueron los microorganismos solos, la combinacion de ambos y el testigo sin inocular que se distribuyeron en un diseno de bloques completos al azar. Se evaluaron variables morfologicas y fisiologicas del rendimiento cada treinta dias apartir del segundo mes de siembra durante siete meses y se determino el contenido de N, P y Ca en el tejido vegetal. Los resultados se analizaron estadisticamente y las diferencias entre tratamientos se compararon de acuerdo a Tukey 5%. Los resultados indican una respuesta diferencial entre los microorganismos. A. brasilense indujo mayor desarrollo radical y la simbiosis doble G. intraradices + A. brasilense mejoro el desarrollo del tallo y lamina foliar. El contenido de N, P y Ca se presento de forma consistente con G. intraradices.
EMIXER: End-to-end Multimodal X-ray Generation via Self-supervision

Deep generative models have enabled the automated synthesis of high-quality data for diverse applications. However, the most effective generative models are specialized to data from a single domain (e.g., images or text). Real-world applications such as healthcare require multi-modal data from multiple domains (e.g., both images and corresponding text), which are difficult to acquire due to limited availability and privacy concerns and are much harder to synthesize. To tackle this joint synthesis challenge, we propose an End-to-end MultImodal X-ray genERative model (EMIXER) for jointly synthesizing x-ray images and corresponding free-text reports, all conditional on diagnosis labels. EMIXER is an conditional generative adversarial model by 1) generating an image based on a label, 2) encoding the image to a hidden embedding, 3) producing the corresponding text via a hierarchical decoder from the image embedding, and 4) a joint discriminator for assessing both the image and the corresponding text. EMIXER also enables self-supervision to leverage vast amount of unlabeled data. Extensive experiments with real X-ray reports data illustrate how data augmentation using synthesized multimodal samples can improve the performance of a variety of supervised tasks including COVID-19 X-ray classification with very limited samples. The quality of generated images and reports are also confirmed by radiologists. We quantitatively show that EMIXER generated synthetic datasets can augment X-ray image classification, report generation models to achieve 5.94% and 6.9% improvement on models trained only on real data samples. Taken together, our results highlight the promise of state of generative models to advance clinical machine learning.
Constraining primordial black hole fraction at the galactic centre using radio observational data

Recent gamma-ray and cosmic-ray observations have put strong constraints on the amount of primordial black holes (PBHs) in our universe. In this article, we use the archival radio data of the inner Galactic Centre to constrain the PBH to dark matter ratio for three different PBH mass distributions including monochromatic, log-normal and power-law. We show that the amount of PBHs only constitutes a very minor component of dark matter at the Galactic Centre for a large parameter space.
Probabilistic bounds on data sensitivity in deep rectifier networks

Neuron death is a complex phenomenon with implications for model trainability, but until recently it was measured only empirically. Recent articles have claimed that, as the depth of a rectifier neural network grows to infinity, the probability of finding a valid initialization decreases to zero. In this work, we provide a simple and rigorous proof of that result. Then, we show what happens when the width of each layer grows simultaneously with the depth. We derive both upper and lower bounds on the probability that a ReLU network is initialized to a trainable point, as a function of model hyperparameters. Contrary to previous claims, we show that it is possible to increase the depth of a network indefinitely, so long as the width increases as well. Furthermore, our bounds are asymptotically tight under reasonable assumptions: first, the upper bound coincides with the true probability for a single-layer network with the largest possible input set. Second, the true probability converges to our lower bound when the network width and depth both grow without limit. Our proof is based on the striking observation that very deep rectifier networks concentrate all outputs towards a single eigenvalue, in the sense that their normalized output variance goes to zero regardless of the network width. Finally, we develop a practical sign flipping scheme which guarantees with probability one that for a $k$-layer network, the ratio of living training data points is at least $2^{-k}$. We confirm our results with numerical simulations, suggesting that the actual improvement far exceeds the theoretical minimum. We also discuss how neuron death provides a theoretical interpretation for various network design choices such as batch normalization, residual layers and skip connections, and could inform the design of very deep neural networks.
Localization of ribulose 1,5‐bisphosphate carboxylase/oxygenase in the N2‐fixing cyanobacterium Anabaena cylindrica

Monospecific antiserum against ribulose 1,5-bisphosphate carboxylase/oxygenase (RuBisCO) of Microcystis PCC7820 cross-reacted with cell-free extracts of the heterocystous cyanobacterium Anabaena cylindrica. This antiserum was used in conjunction with colloidal gold to localize RuBisCO protein in filaments of A. cylindrica. Labelling occurred in the vegetative cells where it was associated particularly with carboxysomes in stationary phase cultures and with both carboxysomes and chromatoplasm in continuous cultures, with the proportion of RuBisCO in the chromatoplasm increasing with increasing growth rate. The protein was undetectable in mature heterocysts.
Estructura espacial de la abundancia y diversidad de los ciliados del microplancton en una laguna costera

El espacio tridimensional es el escenario donde ocurren los procesos ecologicos relacionados con poblaciones y comunidades, aunque solo en las dos ultimas decadas ha sido incorporado explicitamente en estudios ecologicos. En este trabajo se presenta una sintesis del estudio de estructuras espaciales en la distribucion de ciliados del microplancton, usando metodos geoestadisticos, en la laguna costera de Chautengo, Mexico. Los objetivos son 1) caracterizar la distribucion de la abundancia y diversidad de los ciliados a distintas escalas espaciales, y 2) relacionar los patrones espaciales encontrados y su variabilidad con procesos ecologicos de la laguna. Las distribuciones se modelaron y se hicieron mapas de kriging y de simulacion condicional de la abundancia y la diversidad de ciliados del plancton, estimada con el indice de Shannon, en temporada seca y lluviosa. La abundancia y la diversidad se estructuraron espacialmente en manchones, a distintas escalas (de m a km). La presencia y las caracteristicas de las estructuras espaciales variaron dependiendo de 1) la composicion taxonomica de los ciliados, 2) las escalas espaciales consideradas, y 3) las estaciones climaticas. Se discuten caracteristicas ecologicas de la laguna relacionadas con los procesos espaciales descritos.
Quantization in Relative Gradient Angle Domain For Building Polygon Estimation

Building footprint extraction in remote sensing data benefits many important applications, such as urban planning and population estimation. Recently, rapid development of convolutional neural networks (CNNs) and open-sourced high resolution satellite building image datasets have pushed the performance boundary further for automated building extractions. However, CNN approaches often generate imprecise building morphologies including noisy edges and round corners. In this paper, we leverage the performance of CNNs, and propose a module that uses prior knowledge of building corners to create angular and concise building polygons from CNN segmentation outputs. We describe a new transform, Relative Gradient Angle Transform (RGA Transform) that converts object contours from time vs. space to time vs. angle. We propose a new shape descriptor, Boundary Orientation Relation Set (BORS), to describe angle relationship between edges in RGA domain, such as orthogonality and parallelism. Finally, we develop an energy minimization framework that makes use of the angle relationship in BORS to straighten edges and reconstruct sharp corners, and the resulting corners create a polygon. Experimental results demonstrate that our method refines CNN output from a rounded approximation to a more clear-cut angular shape of the building footprint.
Conditional Lower Bound for Inclusion-Based Points-to Analysis

Inclusion-based (i.e., Andersen-style) points-to analysis is a fundamental static analysis problem. The seminal work of Andersen gave a worst-case cubic $O(n^3)$ time points-to analysis algorithm for C, where $n$ is proportional to the number of program variables. An algorithm is truly subcubic if it runs in $O(n^{3-\delta})$ time for some $\delta > 0$. Despite decades of extensive effort on improving points-to analysis, the cubic bound remains unbeaten. The best combinatorial analysis algorithms have a ``slightly subcubic'' $O(n^3 / \text{log } n)$ complexity. It is an interesting open problem whether points-to analysis can be solved in truly subcubic time. In this paper, we prove that a truly subcubic $O(n^{3-\delta})$ time combinatorial algorithm for inclusion-based points-to analysis is unlikely: a truly subcubic combinatorial points-to analysis algorithm implies a truly subcubic combinatorial algorithm for Boolean Matrix Multiplication (BMM). BMM is a well-studied problem, and no truly subcubic combinatorial BMM algorithm has been known. The fastest combinatorial BMM algorithms run in time $O(n^3/ \text{log}^4 n)$. Our result includes a simplified proof of the BMM-hardness of Dyck-reachability. The reduction is interesting in its own right. First, it is slightly stronger than the existing BMM-hardness results because our reduction only requires one type of parenthesis in Dyck-reachability ($D_1$-reachability). Second, we formally attribute the ``cubic bottleneck'' to the need to solve $D_1$-reachability, which captures the semantics of pointer references/dereferences. This new perspective enables a more general reduction that applies to programs with arbitrary pointer statements types. Last, our reduction based on $D_1$-reachability shows that demand-driven points-to analysis is as hard as the exhaustive counterpart.
Regulation of nitrate reductase expression in leaves by nitrate and nitrogen metabolism is completely overridden when sugars fall below a critical level

These experiments use Nia30(145), a tobacco nialnia2 double null mutant transformed with a NIA2 construct, to define when sugar supply plays the dominating role in the regulation of nitrate reductase (NIA) expression. The null alleles of Nia30(145) are transcribed and translated to produce non-functional NIA transcript and NIA protein, providing an endogenous reporter system to track NIA expression at the transcript and protein level. The reintroduced NIA2 construct is expressed at low efficiency, providing a background in which the response to changes in sugar status is not complicated by simultaneous changes in the rate of nitrate assimilation and the levels of nitrate and glutamine. In an alternating light-dark regime, Nia30(145) contained high levels of nitrate and low levels of glutamine and other amino acids. This drives constitutive overexpression of NIA. After transfer of Nia30(145) to continuous darkness, nitrate remains high and glutamine low, but the NIA transcript level and NIA protein decreased significantly within 24 h and were undetectable from 48 h onwards. The decrease of the NIA transcript level was fully reversed and the decrease of NIA protein was partly reversed when leaves were detached from the pre-darkened plants and supplied with sucrose in the dark. The decrease was not reversed by nitrate or cytokinin. The NIA transcript disappeared when the leaf sugar content fell below 4 μmol hexose equivalents g -1 FW, and recovered when sugars rose above 8 μmol hexose equivalents g -1 FW. It is concluded that low sugar represses NIA, completely overriding signals derived from nitrate and nitrogen metabolism.
Visualizing Classification Structure of Large-Scale Classifiers

We propose a measure to compute class similarity in large-scale classification based on prediction scores. Such measure has not been formally pro-posed in the literature. We show how visualizing the class similarity matrix can reveal hierarchical structures and relationships that govern the classes. Through examples with various classifiers, we demonstrate how such structures can help in analyzing the classification behavior and in inferring potential corner cases. The source code for one example is available as a notebook at this https URL