Miracle-dz/UniFAR-FTUs huggingface dataset:
Confidence Intervals for Selected Parameters

Practical or scientific considerations often lead to selecting a subset of parameters as ``important. '' Inferences about those parameters often are based on the same data used to select them in the first place. That can make the reported uncertainties deceptively optimistic: confidence intervals that ignore selection generally have less than their nominal coverage probability. Controlling the probability that one or more intervals for selected parameters do not cover---the ``simultaneous over the selected'' (SoS) error rate---is crucial in many scientific problems. Intervals that control the SoS error rate can be constructed in ways that take advantage of knowledge of the selection rule. We construct SoS-controlling confidence intervals for parameters deemed the most ``important'' $k$ of $m$ shift parameters because they are estimated (by independent estimators) to be the largest. The new intervals improve substantially over Sidak intervals when $k$ is small compared to $m$, and approach the standard Bonferroni-corrected intervals when $k \approx m$. Standard, unadjusted confidence intervals for location parameters have the correct coverage probability for $k=1$, $m=2$ if, when the true parameters are zero, the estimators are exchangeable and symmetric.
Association between gastric intramucosal pH and splanchnic endotoxin, antibody to endotoxin, and tumor necrosis factor‐α concentrations in patients undergoing cardiopulmonary bypass

ObjectivesTo determine the association between gastric intramucosal pH, a minimally invasive marker reflecting the adequacy of oxygen delivery to the gastrointestinal tract, and splanchnic endotoxin, antibody to endotoxin, and tumor necrosis factor (TNF)-α concentrations in patients undergoing cardiopulmonary bypass. DesignSingle-arm, prospective study. SettingUniversity hospital. PatientsAdults (n = 10) free of hepatic, pulmonary, and renal disease undergoing non-emergent coronary artery bypass surgery. InterventionsAfter induction of general anesthesia and endotracheal intubation, a tonometer nasogastric tube was positioned in the stomach, and triple-lumen fiberoptic catheters were inserted into the hepatic vein and pulmonary artery. Hepatic venous and mixed venous blood samples were analyzed for endotoxin, antibody to endotoxin, and TNF-α at six times: 30 mins after induction of anesthesia (time 1); during vena caval cannulation (time 2); after 15 mins of hypothermic cardiopulmonary bypass (time 3); during spontaneous left ventricular ejection after release of the aortic cross-clamp, but before termination of cardiopulmonary bypass (time 4); 15 mins after termination of cardiopulmonary bypass (time 5); and 1 hr after termination of cardiopulmonary bypass (time 6). Gastric intramucosal pH, systemic oxygen delivery (Do2), mixed venous oxygen saturation, hepatic venous oxygen saturation, and hepatic venous lactate concentrations were recorded at these same times. Data for each variable were compared with baseline values (time 1) for statistical significance. Measurements and Main ResultsCardiopulmonary bypass was associated with an increase (p < .05) in systemic endotoxin concentrations from ventricular ejection until the end of the study. Virtually identical changes in the splanchnic circulation at this time approached, but did not reach, statistical significance, because hepatic venous endotoxin concentrations were higher than the mixed venous endotoxin concentrations at baseline (41.6 ± 11.2 vs. 16.9 ± 49 pg/mL). Gastric intramucosal pH was abnormal (<7.35) at 15 mins (p > .05) and at 1 hr after termination of cardiopulmonary bypass (p > .05). The relationship between endotoxin and gastric intramucosal pH was not statistically significant (p = .15). The decrease in endotoxin antibody was small and statistically insignificant. TNF-α was not detected in any patient Systemic Do2 decreased (p < .05) after 15 mins of hypothermic cardiopulmonary bypass, but returned to baseline values thereafter. There were no significant changes in mixed venous and hepatic venous oxygen saturation values. Splanchnic lactate concentrations increased at cannulation (p < .05), after 15 mins of hypothermic cardiopulmonary bypass (p < .05), and IS mins after termination of cardiopulmonary bypass (p < .05). ConclusionsThese observations are consistent with the hypothesis that impaired gut-barrier function is responsible for endotoxemia
Training Detection-Range-Frugal Cooperative Collision Avoidance Models for Quadcopters via Neuroevolution

Cooperative autonomous approaches to avoiding collisions among small Unmanned Aerial Vehicles (UAVs) is central to safe integration of UAVs within the civilian airspace. One potential online cooperative approach is the concept of reciprocal actions, where both UAVs take pre-trained mutually coherent actions that do not require active online coordination (thereby avoiding the computational burden and risk associated with it). This paper presents a learning based approach to train such reciprocal maneuvers. Neuroevolution, which uses evolutionary algorithms to simultaneously optimize the topology and weights of neural networks, is used as the learning method -- which operates over a set of sample approach scenarios. Unlike most existing work (that minimize travel distance, energy or risk), the training process here focuses on the objective of minimizing the required detection range; this has important practical implications w.r. t. alleviating the dependency on sophisticated sensing and their reliability under various environments. A specialized design of experiments and line search is used to identify the minimum detection range for each sample scenarios. In order to allow an efficient training process, a classifier is used to discard actions (without simulating them) where the controller would fail. The model obtained via neuroevolution is observed to generalize well to (i.e., successful collision avoidance over) unseen approach scenarios.
New technologies and new horizons

Greetings from the desk of the Editor and I welcome you with the current issue of your journal Tropical Parasitology. With the advent of molecular methods, the discipline of parasitology is undergoing a sea change compared to the knowledge base of the past which had relied heavily on gross morphology and microscopic features of the parasites. The genomic, proteomic, metabolomic, and other – omic studies are opening new horizons in the understanding of the parasites. These studies have the potential of wide‐ranging applications ranging from taxonomy and understanding the pathogenesis to newer diagnostic methods, drug target‐identification, and future vaccine development. Taxonomic reclassification and application of molecular targets for genus and species identification and pinpointing the drug resistance genes of a number of parasites are a few benefits which we have already derived from the research application of the new technologies. Modern epidemiological studies are increasingly relying on molecular typing and fingerprinting of pathogens, and slowly, it is being used for various parasites for a better understanding of the spread of these parasites in the community which will help in better control measures in the coming days.
Performance Evaluation for the Co-Existence of eMBB and URLLC Networks: Synchronized versus Unsynchronized TDD

To ensure the high level of automation required in todayâ€™s industrial applications, next-generation wireless networks must enable real-time control and automation of dynamic processes with the requirements of extreme low-latency and ultra- reliable communications. In this paper, we provide a performance assessment for the co-existence of a macro (eMBB) and a local factory (URLLC) network and evaluate the network conditions under which the latency and reliability requirements of factory automation applications are met. In particular, we evaluate the co-existence of the eMBB and URLLC networks under two scenarios: (i) synchronized TDD, in which both networks follow the same TDD pattern, and (ii) unsynchronized TDD, in which the eMBB and URLLC networks follow different TDD patterns. Simulation results show that the high downlink interference from the macro base stations towards the factory results in a reduction of the downlink URLLC capacity and service availability in case of synchronized TDD and a reduction of the uplink URLLC capacity and service availability in case of unsynchronized TDD. Finally, it is shown that a promising case for co-existence is the adjacent channel allocation, for both synchronized and unsynchronized TDD deployments. Here, the required isolation to protect the URLLC network in the worst-case scenario where the factory is located next to a macro site can be handled via the factory wall penetration loss (e.g., considering high concrete or metal-coated building walls) along with other solutions such as filters, larger separation distance, and band pairing.
Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

Sharing of the frequency bands between radar and communication systems has attracted substantial attention, as it can avoid under-utilization of otherwise permanently allocated spectral resources, thus improving efficiency. Further, there is increasing demand for radar and communication systems that share the hardware platform as well as the frequency band, as this not only decongests the spectrum, but also benefits both sensing and signaling operations via the full cooperation between both functionalities. Nevertheless, the success of spectrum and hardware sharing between radar and communication systems critically depends on high-quality joint radar and communication designs. In the first part of this paper, we overview the research progress in the areas of radar-communication coexistence and dual-functional radar-communication (DFRC) systems, with particular emphasis on application scenarios and technical approaches. In the second part, we propose a novel transceiver architecture and frame structure for a DFRC base station (BS) operating in the millimeter wave (mmWave) band, using the hybrid analog-digital (HAD) beamforming technique. We assume that the BS is serving a multi-antenna user equipment (UE) over a mmWave channel, and at the same time it actively detects targets. The targets also play the role of scatterers for the communication signal. In that framework, we propose a novel scheme for joint target search and communication channel estimation, which relies on omni-directional pilot signals generated by the HAD structure. Given a fully-digital communication precoder and a desired radar transmit beampattern, we propose to design the analog and digital precoders under non-convex constant-modulus (CM) and power constraints, such that the BS can formulate narrow beams towards all the targets, while pre-equalizing the impact of the communication channel. Furthermore, we design a HAD receiver that can simultaneously process signals from the UE and echo waves from the targets. By tracking the angular variation of the targets, we show that it is possible to recover the target echoes and mitigate the resulting interference to the UE signals, even when the radar and communication signals share the same signal-to-noise ratio (SNR). The feasibility and efficiency of the proposed approaches in realizing DFRC are verified via numerical simulations. Finally, the paper concludes with an overview of the open problems in the research field of communication and radar spectrum sharing (CRSS).
Influence of discharge quantity on SF6 decomposition under needle-plate dc partial discharge

In order to investigate the influence of discharge quantity on SF6 decomposition under dc partial discharge (PD), a needle-plate model was adopted, and SF6 decomposition experiments have been conducted with different dc experiment voltages. Finally, the influence and SF6 decomposition mechanism were achieved. The studies provide experimental and theoretical basis for establishing and improving SF6 decomposed components analysis (DCA) for identifying dc equipment fault.
Copula-based functional Bayes classification with principal components and partial least squares

We present a new functional Bayes classifier that uses principal component (PC) or partial least squares (PLS) scores from the common covariance function, that is, the covariance function marginalized over groups. When the groups have different covariance functions, the PC or PLS scores need not be independent or even uncorrelated. We use copulas to model the dependence. Our method is semiparametric; the marginal densities are estimated nonparametrically by kernel smoothing and the copula is modeled parametrically. We focus on Gaussian and t-copulas, but other copulas could be used. The strong performance of our methodology is demonstrated through simulation, real data examples, and asymptotic properties.
Nonparametric Functional Approximation with Delaunay Triangulation

We propose a differentiable nonparametric algorithm, the Delaunay triangulation learner (DTL), to solve the functional approximation problem on the basis of a $p$-dimensional feature space. By conducting the Delaunay triangulation algorithm on the data points, the DTL partitions the feature space into a series of $p$-dimensional simplices in a geometrically optimal way, and fits a linear model within each simplex. We study its theoretical properties by exploring the geometric properties of the Delaunay triangulation, and compare its performance with other statistical learners in numerical studies.
An extensive review of computational dance automation techniques and applications

Dance is an art and when technology meets this kind of art, it is a novel attempt in itself. Many researchers have attempted to automate several aspects of dance, right from dance notation to choreography; from dance capturing to dance generation. We define and illustrate the concept of ‘Dance Automation’ in this paper. Furthermore, we have encountered several applications of dance automation like e-learning, heritage preservation, medical therapy, etc. Despite decades of continuous attempts by many researchers in various styles of dance all round the world, we found a review paper that portrays the research status in this area of ‘dance and computers’ dating to 1990 (Leonardo 1990 Computers and dance: A bibliography, pp. 87–90). Hence, we decided to compose a comprehensive review article that showcases several aspects of dance automation and document contributions of researchers in marrying creativity with logic. This paper is an attempt to review research work reported in the literature, categorize and group significant research work completed in a span of 1967–2020 in the field of automating dance. We have explicitly identified six major categories corresponding to the use of computers in dance automation, namely, dance representation, dance capturing, dance semantics, dance generation, dance processing approaches and applications of dance automation systems. We classified several research papers under these categories according to their research approach and functionality. With the help of proposed categories and subcategories, one can easily determine the state of research and the new avenues left for exploration in the field of dance automation.
Stochastic Time-of-Use-Type Constraints for Uninterruptible Services

In this paper, a mixed integer linear formulation for problems considering time-of-use-type constraints for uninterruptible services is presented. This paper is motivated by demand response (DR) problems in power systems, in which certain devices must remain online once they are switched on, along with a certain number of periods. Classically, this kind of constraints are modeled as a summation over a rolling time windows. This makes it difficult to consider this time-of-use parameter as uncertain. We propose an alternative formulation in which the time of use is on the right-hand side of a constraint instead on the limit of a summation. This allows applying existing stochastic optimization methodologies easily. An illustrative model for the optimal allocation of an uninterruptible load for the DR problem supports the proposed formulation.
JETLAG Resets the Drosophila Circadian Clock by Promoting Light-Induced Degradation of TIMELESS

Organisms ranging from bacteria to humans synchronize their internal clocks to daily cycles of light and dark. Photic entrainment of the Drosophila clock is mediated by proteasomal degradation of the clock protein TIMELESS (TIM). We have identified mutations in jetlag—a gene coding for an F-box protein with leucine-rich repeats—that result in reduced light sensitivity of the circadian clock. Mutant flies show rhythmic behavior in constant light, reduced phase shifts in response to light pulses, and reduced light-dependent degradation of TIM. Expression of JET along with the circadian photoreceptor cryptochrome (CRY) in cultured S2R cells confers light-dependent degradation onto TIM, thereby reconstituting the acute response + of the circadian clock to light in a cell culture system. Our results suggest that JET is essential for resetting the clock by transmitting light signals from CRY to TIM.
Discovery of Four Apparently Cold Dusty Galaxies at z = 3.62–5.85 in the COSMOS Field: Direct Evidence of Cosmic Microwave Background Impact on High-redshift Galaxy Observables

We report Atacama Large Millimeter Array (ALMA) observations of four high-redshift dusty star-forming galaxy candidates selected from far-infrared (FIR)/submillimeter observations in the COSMOS field. We securely detect all galaxies in the continuum and spectroscopically confirm them at z = 3.62–5.85 using ALMA 3 mm line scans, detecting multiple CO and/or [C i] transitions. This includes the most distant dusty galaxy currently known in the COSMOS field, ID85001929 at z = 5.847. These redshifts are lower than we had expected, as these galaxies have substantially colder dust temperatures (i.e., their spectral energy distributions peak at longer rest-frame wavelengths) than most literature sources at z > 4. The observed cold dust temperatures are best understood as evidence for optically thick dust continuum in the FIR, rather than the result of low star formation efficiency with rapid metal enrichment. We provide direct evidence that, given their cold spectral energy distributions, cosmic microwave background (CMB) plays a significant role in biasing their observed Rayleigh–Jeans (RJ) slopes to unlikely steep values and, possibly, reducing their CO fluxes by a factor of two. We recover standard RJ slopes when the CMB contribution is taken into account. High-resolution ALMA imaging shows compact morphology and evidence for mergers. This work reveals a population of cold dusty star-forming galaxies that were underrepresented in current surveys and are even colder than typical main-sequence galaxies at the same redshift. High FIR dust optical depth might be a widespread feature of compact starbursts at any redshift.
How Much Does Audio Matter to Recognize Egocentric Object Interactions?

Sounds are an important source of information on our daily interactions with objects. For instance, a significant amount of people can discern the temperature of water that it is being poured just by using the sense of hearing. However, only a few works have explored the use of audio for the classification of object interactions in conjunction with vision or as single modality. In this preliminary work, we propose an audio model for egocentric action recognition and explore its usefulness on the parts of the problem (noun, verb, and action classification). Our model achieves a competitive result in terms of verb classification (34.26% accuracy) on a standard benchmark with respect to vision-based state of the art systems, using a comparatively lighter architecture.
A Semi-Supervised Approach for Low-Resourced Text Generation

Recently, encoder-decoder neural models have achieved great success on text generation tasks. However, one problem of this kind of models is that their performances are usually limited by the scale of well-labeled data, which are very expensive to get. The low-resource (of labeled data) problem is quite common in different task generation tasks, but unlabeled data are usually abundant. In this paper, we propose a method to make use of the unlabeled data to improve the performance of such models in the low-resourced circumstances. We use denoising auto-encoder (DAE) and language model (LM) based reinforcement learning (RL) to enhance the training of encoder and decoder with unlabeled data. Our method shows adaptability for different text generation tasks, and makes significant improvements over basic text generation models.
Stochastic Computing for Hardware Implementation of Binarized Neural Networks

Binarized neural networks, a recently discovered class of neural networks with minimal memory requirements and no reliance on multiplication, are a fantastic opportunity for the realization of compact and energy efficient inference hardware. However, such neural networks are generally not entirely binarized: their first layer remains with fixed point input. In this paper, we propose a stochastic computing version of binarized neural networks, where the input is also binarized. The simulations on the example of the Fashion-MNIST and CIFAR-10 datasets show that such networks can approach the performance of conventional binarized neural networks. We evidence that the training procedure should be adapted for use with stochastic computing. Finally, the ASIC implementation of our scheme is investigated, in a system that closely associates logic and memory, implemented by spin torque magnetoresistive random access memory. This analysis shows that the stochastic computing approach can allow considerable savings with regards to conventional binarized neural networks in terms of area (62% area reduction on the Fashion-MNIST task). It can also allow important savings in terms of energy consumption if we accept a reasonable reduction of accuracy: for example a factor 2.1 can be saved, with the cost of 1.4% in Fashion-MNIST test accuracy. These results highlight the high potential of binarized neural networks for hardware implementation, and that adapting them to hardware constraints can provide important benefits.
cGANs with Conditional Convolution Layer

Conditional generative adversarial networks (cGANs) have been widely researched to generate class conditional images using a single generator. However, in the conventional cGANs techniques, it is still challenging for the generator to learn condition-specific features, since a standard convolutional layer with the same weights is used regardless of the condition. In this paper, we propose a novel convolution layer, called the conditional convolution layer, which directly generates different feature maps by employing the weights which are adjusted depending on the conditions. More specifically, in each conditional convolution layer, the weights are conditioned in a simple but effective way through filter-wise scaling and channel-wise shifting operations. In contrast to the conventional methods, the proposed method with a single generator can effectively handle condition-specific characteristics. The experimental results on CIFAR, LSUN and ImageNet datasets show that the generator with the proposed conditional convolution layer achieves a higher quality of conditional image generation than that with the standard convolution layer.