Miracle-dz/UniFAR-FTUs huggingface dataset:
eSampling: Energy Harvesting ADCs

Analog-to-digital converters (ADCs) allow physical signals to be processed using digital hardware. The power consumed in conversion grows with the sampling rate and quantization resolution, imposing a major challenge in power-limited systems. A common ADC architecture is based on sample-and-hold (S/H) circuits, where the analog signal is being tracked only for a fraction of the sampling period. In this paper, we propose the concept of eSampling ADCs, which harvest energy from the analog signal during the time periods where the signal is not being tracked. This harvested energy can be used to supplement the ADC itself, paving the way to the possibility of zero-power consumption and power-saving ADCs. We analyze the tradeoff between the ability to recover the sampled signal and the energy harvested, and provide guidelines for setting the sampling rate in the light of accuracy and energy constraints. Our analysis indicates that eSampling ADCs operating with up to 12 bits per sample can acquire bandlimited analog signals such that they can be perfectly recovered without requiring power from the external source. Furthermore, our theoretical results reveal that eSampling ADCs can in fact save power by harvesting more energy than they consume. To verify the feasibility of eSampling ADCs, we present a circuit-level design using standard complementary metal oxide semiconductor (CMOS) 65 nm technology. An eSampling 8-bit ADC which samples at 40 MHZ is designed on a Cadence Virtuoso platform. Our experimental study involving Nyquist rate sampling of bandlimited signals demonstrates that such ADCs are indeed capable of harvesting more energy than that spent during analog-to-digital conversion, without affecting the accuracy.
Temporal distribution of ichthyoplankton in the Ivinhema River (Mato Grosso do Sul State/ Brazil): influence of environmental variables

Information on ichthyoplankton is an important tool in determining reproduction periods that - if associated to environmental variables - allows for inferences about the factors that regulate their intensity, beginning, and ending. In this context, this study aims to establish (i) ichthyoplankton composition; (ii) temporal variations in the overall density of eggs and larvae and among the most important taxa; and (iii) the influence of some abiotic and biotic variables on these organisms' abundance. Ichthyoplankton sampling was undertaken during the period between April 2005 and March 2006 at the Ivinhema River, upper Parana River basin (MS/Brazil). Differences in the ichthyoplankton's temporal variation were evaluated using unifactorial ANOVAs. Principal Component Analysis and Pearson's correlation were used for the relationships between eggs and larvae densities and environmental variables. A total of 3,341 eggs and 2,896 larvae were captured during the period studied, and most of them were medium-sized and large species that carried out some type of reproductive migration. The highest densities of eggs and larvae occurred during the months of spring and summer, except for those of Bryconamericus stramineus, which was most abundant during the winter. The density of ichthyoplankton was most positively correlated with the water's outflow and temperature. However, Plagioscion squamosissimus was positively related to the increase in pH and in zooplanktonic organisms, while B. stramineus was inversely correlated with water outflow and temperature. The study concludes that spawning is most intense in the spring and summer, especially between November and January, and is related to the greatest values of water temperature and outflow. Nevertheless, the response and reproductive intensity in relation to the environmental variables vary according to the species.
Performance analysis of a distributed algorithm for admission control in wireless networks under the 2-hop interference model

A general open problem in networking is: what are the fundamental limits to the performance that is achievable with some given amount of resources? More specifically, if each node in the network has information about only its 1-hop neighborhood, then what are the limits to performance? This problem is considered for wireless networks where each communication link has a minimum bandwidth quality-of-service (QoS) requirement. Links in the same vicinity contend for the shared wireless medium. The conflict graph captures which pairs of links interfere with each other and depends on the MAC protocol. In IEEE 802.11 MAC protocol-based networks, when communication between nodes i and j takes place, the neighbors of both i and j remain silent. This model of interference is called the 2-hop interference model because the distance in the network graph between any two links that can be simultaneously active is at least 2. In the admission control problem studied in the present paper, the objective is to determine, using only localized information, whether a given set of flow rates is feasible. In the present work, a distributed algorithm is proposed for this problem, where each node has information only about its 1-hop neighborhood. The worst-case performance of the distributed algorithm, i.e. the largest factor by which the performance of this distributed algorithm is away from that of an optimal, centralized algorithm, is analyzed. Lower and upper bounds on the suboptimality of the distributed algorithm are obtained, and both bounds are shown to be tight. The exact worst-case performance is obtained for some ring topologies. While distance-d distributed algorithms have been analyzed for the 1-hop interference model, an open problem in the literature is to extend these results to the K-hop interference model, and the present work initiates the generalization to the K-hop interference model.
Role of ancillary variables in the design, analysis, and interpretation of animal experiments.

During the course of an experiment using animals, many variables (e.g., age, body weight at several times, food and water consumption, hematology, and clinical biochemistry) and other characteristics are often recorded in addition to the primary response variable(s) specified by the experimenter. These additional variables have an important role in the design and interpretation of the experiment. They may be formally incorporated into the design and/or analysis and thus increase precision and power. However, even if these variables are not incorporated into the primary statistical design or into the formal analysis of the experiment, they may nevertheless be used in an ancillary or exploratory way to provide valuable information about the experiment, as shown by various examples. Used in this way, ancillary variables may improve analysis and interpretation by providing an assessment of the randomization process and an approach to the identification of outliers, lead to the generation of new hypotheses, and increase generality of results or account for differences in results when compared across different experiments. Thus, appropriate use of additional variables may lead to reduction in the number of animals required to achieve the aims of the experiment and may provide additional scientific information as an extra benefit. Unfortunately, this type of information is sometimes effectively discarded because its potential value is not recognized. Guidelines for use of animals include, in addition to the obligation to follow humane procedures, the obligation to use no more animals than necessary. Ethical experimental practice thus requires that all information be properly used and reported.
A Lagrange Multiplier Expression Method for Bilevel Polynomial Optimization

This paper studies bilevel polynomial optimization problems. To solve them, we give a method based on polynomial optimization relaxations. Each relaxation is obtained from the Kurash-Kuhn-Tucker (KKT) conditions for the lower level optimization and the exchange technique for semi-infinite programming. For KKT conditions, Lagrange multipliers are represented as polynomial or rational functions. The Moment-SOS relaxations are used to solve the polynomial optimizattion relaxations. Under some general assumptions, we prove the convergence of the algorithm for solving bilevel polynomial optimization problems. Numerical experiments are presented to show the efficiency of the method.
Mixture of Step Returns in Bootstrapped DQN

The concept of utilizing multi-step returns for updating value functions has been adopted in deep reinforcement learning (DRL) for a number of years. Updating value functions with different backup lengths provides advantages in different aspects, including bias and variance of value estimates, convergence speed, and exploration behavior of the agent. Conventional methods such as TD-lambda leverage these advantages by using a target value equivalent to an exponential average of different step returns. Nevertheless, integrating step returns into a single target sacrifices the diversity of the advantages offered by different step return targets. To address this issue, we propose Mixture Bootstrapped DQN (MB-DQN) built on top of bootstrapped DQN, and uses different backup lengths for different bootstrapped heads. MB-DQN enables heterogeneity of the target values that is unavailable in approaches relying only on a single target value. As a result, it is able to maintain the advantages offered by different backup lengths. In this paper, we first discuss the motivational insights through a simple maze environment. In order to validate the effectiveness of MB-DQN, we perform experiments on the Atari 2600 benchmark environments, and demonstrate the performance improvement of MB-DQN over a number of baseline methods. We further provide a set of ablation studies to examine the impacts of different design configurations of MB-DQN.
Proteolytic Adaptor for Transfer-Messenger RNA-Tagged Proteins from α-Proteobacteria

ABSTRACT We have identified an analog of SspB, the proteolytic adaptor for transfer-messenger RNA (tmRNA)-tagged proteins, in Caulobacter crescentus. C. crescentus SspB shares limited sequence similarity with Escherichia coli SspB but binds the tmRNA tag in vitro and is required for optimal proteolysis of tagged proteins in vivo.
On the Magnetic Fields, Beaming Fractions, and Fastness Parameters of Pulsating Ultraluminous X-Ray Sources

The discovery of pulsating ultraluminous X-ray sources (PULX) suggests that neutron stars are presumably common within the ultraluminous X-ray source (ULX) population though the majority of the population members currently lack pulsations. These systems are likely to host neutron stars accreting mass at super-Eddington (supercritical) rates from their massive companion in high-mass X-ray binaries. Taking into account the spherization of the accretion flow in the supercritical regime, the beaming of X-ray emission, and the reduction of the scattering cross section in a strong magnetic field, we infer the ranges for the neutron-star surface magnetic dipole field strengths, beaming fractions, and fastness parameters in the PULX M82 X-2, ULX NGC 5907, ULX NGC 7793 P13, NGC 300 ULX1, M51 ULX-7, NGC 1313 X-2, and Swift J0243.6+6124 from a set of conditions based on a variety of combinations of different spin and luminosity states. Using the observed spin-up rates under the critical luminosity condition, we estimate the surface-field strengths in the ∼1011–1013 G range for all PULX. In general, the results of our analysis under the subcritical luminosity condition indicate surface-field strengths in the ∼1011–1015 G range. We argue that PULX do not require magnetar-strength surface dipole fields if beaming is taken into account, yet the fields are strong enough for the neutron stars in ULX to magnetically channel the accretion flow in supercritical accretion disks.
An Eyring–Kramers law for slowly oscillating
bistable diffusions

We consider two-dimensional stochastic differential equations, describing the motion of a slowly and periodically forced overdamped particle in a double-well potential, subjected to weak additive noise. We give sharp asymptotics of Eyring--Kramers type for the expected transition time from one potential well to the other one. Our results cover a range of forcing frequencies that are large with respect to the maximal transition rate between potential wells of the unforced system. The main difficulty of the analysis is that the forced system is non-reversible, so that standard methods from potential theory used to obtain Eyring--Kramers laws for reversible diffusions do not apply. Instead, we use results by Landim, Mariani and Seo that extend the potential-theoretic approach to non-reversible systems.
ACCOUNTING FOR MULTI TRAITS IN RECOMMENDING RICE VARIETIES FOR DIVERSE ENVIRONMENTS

Recommendations of crop varieties are usually done on the basis of grain yield. However, there are other important traits related to quality and agronomy that need to be considered. This study has clearly shown that grain yield is often not related to some important traits of the crop. Under such circumstances, selection based on yield can lead to the loss of these important characters. Recent research has also indicated the need for a multi-trait approach to recommending crop varieties. However, such approaches, as suggested in the literature, are subjective and do not have a sufficient statistical basis. This study proposes a methodology for varietal recommendations by taking account of all important traits. It is a multivariate approach considered to be an improvement on the univariate method previously proposed. Data from rice varieties of 3, 3½ and 4½ month maturity groups, cultivated across diverse environments, over two seasons in Sri Lanka were used to illustrate the proposed methodology. The results suggest that the new method will be appropriate for taking in to account all important traits along with yield.
Memory Based Attentive Fusion

The use of multi-modal data for deep machine learning has shown promise when compared to uni-modal approaches, where fusion of multi-modal features has resulted in improved performance. However, most state-of-the-art methods use naive fusion which processes feature streams from a given time-step and ignores long-term dependencies within the data during fusion. In this paper, we present a novel Memory Based Attentive Fusion (MBAF) layer, which fuses modes by incorporating both the current features and long-term dependencies in the data, thus allowing the model to understand the relative importance of modes over time. We define an explicit memory block within the fusion layer which stores features containing long-term dependencies of the fused data. The inputs to our layer are fused through attentive composition and transformation, and the transformed features are combined with the input to generate the fused layer output. Following existing state-of-the-art methods, we have evaluated the performance and the generalizability of the proposed approach on the IEMOCAP and PhysioNet-CMEBS datasets with different modalities. In our experiments, we replace the naive fusion layer in benchmark networks with our proposed layer to enable a fair comparison. Experimental results indicate that MBAF layer can generalise across different modalities and networks to enhance the fusion and improve performance.
Image De-Quantization Using Generative Models as Priors

Image quantization is used in several applications aiming in reducing the number of available colors in an image and therefore its size. De-quantization is the task of reversing the quantization effect and recovering the original multi-chromatic level image. Existing techniques achieve de-quantization by imposing suitable constraints on the ideal image in order to make the recovery problem feasible since it is otherwise ill-posed. Our goal in this work is to develop a de-quantization mechanism through a rigorous mathematical analysis which is based on the classical statistical estimation theory. In this effort we incorporate generative modeling of the ideal image as a suitable prior information. The resulting technique is simple and capable of de-quantizing successfully images that have experienced severe quantization effects. Interestingly, our method can recover images even if the quantization process is not exactly known and contains unknown parameters.
Amended Cross Entropy Cost: Framework For Explicit Diversity Encouragement

Cross Entropy (CE) has an important role in machine learning and, in particular, in neural networks. It is commonly used in neural networks as the cost between the known distribution of the label and the Softmax/Sigmoid output. In this paper we present a new cost function called the Amended Cross Entropy (ACE). Its novelty lies in its affording the capability to train multiple classifiers while explicitly controlling the diversity between them. We derived the new cost by mathematical analysis and "reverse engineering" of the way we wish the gradients to behave, and produced a tailor-made, elegant and intuitive cost function to achieve the desired result. This process is similar to the way that CE cost is picked as a cost function for the Softmax/Sigmoid classifiers for obtaining linear derivatives. By choosing the optimal diversity factor we produce an ensemble which yields better results than the vanilla one. We demonstrate two potential usages of this outcome, and present empirical results. Our method works for classification problems analogously to Negative Correlation Learning (NCL) for regression problems.
Transferred Energy Management Strategies for Hybrid Electric Vehicles Based on Driving Conditions Recognition

Energy management strategies (EMSs) are the most significant components in hybrid electric vehicles (HEVs) because they decide the potential of energy conservation and emission reduction. This work presents a transferred EMS for a parallel HEV via combining the reinforcement learning method and driving conditions recognition. First, the Markov decision process (MDP) and the transition probability matrix are utilized to differentiate the driving conditions. Then, reinforcement learning algorithms are formulated to achieve power split controls, in which Q-tables are tuned by current driving situations. Finally, the proposed transferred framework is estimated and validated in a parallel hybrid topology. Its advantages in computational efficiency and fuel economy are summarized and proved.
Hypotheses for the Colonization of the Caribbean Basin by Two Genera of the Rubiaceae: Erithalis and Ernodea

Abstract Ernodea Swartz (Tribe Spermacoceae) and Erithalis P. Browne (Tribe Chiococceae) are two genera of Rubiaceae that are endemic to the Caribbean, composed of four and nine species, respectively. Molecular phylogenies were analyzed in a biogeographic context using Brooks Parsimony Analysis (BPA) and Fitch Parsimony methods. A geographic association of Cuba and Dominican Republic was supported by area cladograms generated by BPA for Erithalis and by the combined data matrix. Additionally, the two parts of Hispaniola (Dominican Republic and Haiti) appear in two different places on the cladogram suggesting that Hispaniola represents a composite of geological areas. Fitch analyses support a Greater Antillean origin for Erithalis, however, it was ambiguously resolved for Ernodea. Overall, biogeographic analyses explain the present-day distribution of Ernodea as a product of dispersal, whereas a combination of vicariance and dispersal events appears to be involved in Erithalis biogeographical history. In general, the study implies that trans-oceanic seed dispersal plays an important role in the distribution of these genera, in addition to Tertiary vicariance events for Erithalis.
How to Morph Graphs on the Torus

We present the first algorithm to morph graphs on the torus. Given two isotopic essentially 3-connected embeddings of the same graph on the Euclidean flat torus, where the edges in both drawings are geodesics, our algorithm computes a continuous deformation from one drawing to the other, such that all edges are geodesics at all times. Previously even the existence of such a morph was not known. Our algorithm runs in $O(n^{1+\omega/2})$ time, where $\omega$ is the matrix multiplication exponent, and the computed morph consists of $O(n)$ parallel linear morphing steps. Existing techniques for morphing planar straight-line graphs do not immediately generalize to graphs on the torus; in particular, Cairns' original 1944 proof and its more recent improvements rely on the fact that every planar graph contains a vertex of degree at most 5. Our proof relies on a subtle geometric analysis of 6-regular triangulations of the torus. We also make heavy use of a natural extension of Tutte's spring embedding theorem to torus graphs.
Notaspidean Opisthobranchiate Molluscs from Southern Africa.

The order Notaspidea of the sub-class Opisthobranchia comprises two families, Umbraculidae and Pleurobranchidae; and some would divide the former family into two separating the Tylodinidae. From southern Africa thirteen species of these families have been recorded and as a result of the present study seven are added of which three are descriptionbed as new.
Auto Adaptive Irregular OpenMP Loops

OpenMP is a standard for the parallelization due to the ease in programming parallel-for loops in a fork-join manner. Many shared-memory applications are implemented using this model despite not being ideal for applications with high load imbalance, such as those that make irregular memory accesses. One parameter, i.e., chunk size, is made available to users in order to mitigate performance loss. However, this parameter is dependent on architecture, system load, application, and input; making it difficult to tune. We present an OpenMP scheduler that does an adaptive tuning for chunk size for unbalanced applications that make irregular memory accesses. In particular, this method(iCh) uses work-stealing for imbalance and adapts chunk size using a force-feedback model that approximates variance of task length in a chunk. This scheduler has low overhead and allows for active load balancing while the applications are running. We demonstrate this using both sparse matrix-vector multiplication (spmv) and Betweenness Centrality (bc) and show that iCh can achieve average speedups close (i.e., within 1.061x for spmv and 1.092x for bc) of either OpenMP loops scheduled with dynamic or work-stealing methods that had chunk size tuned offline.
Lockdown Impact & Exit Strategy for COVID-19 Pandemic in India

COVID-19–a viral infectious disease–has quickly emerged as a global pandemic infecting millions of people with a significant number of deaths across the globe. The symptoms of this disease vary widely. Depending on the symptoms an infected person is broadly classified into two categories namely, asymptomatic and symptomatic. Asymptomatic individuals display mild or no symptoms but continue to transmit the infection to otherwise healthy individuals. This particular aspect of asymptomatic infection poses a major obstacle in managing and controlling the transmission of the infectious disease. In this paper, we attempt to mathematically model the spread of COVID-19 in India under various intervention strategies. We consider SEIR type epidemiological models, incorporated with India specific social contact matrix representing contact structures among different age groups of the population, to study the impact of various factors, namely, presence of asymptotic individuals, lockdown strategies, social distancing, quarantine, and hospitalization, on the disease transmission. Our model is seen to match with the real COVID-19 data of India till May 15, 2020.