Miracle-dz/UniFAR-FTUs huggingface dataset:
Three functionally distinct kinds of pelagic development

Interest in pelagic embryos and larvae of benthic marine animals has centered on dispersal and its consequences, but longer pelagic larval durations appear to have evolved and to persist for other reasons than dispersal advantages. Here are explanatory hypotheses for three kinds of pelagic development. (1) Pelagic development prior to ability to feed or settle avoids constraints of oxygen supply to aggregated embryos and scarcity of deposition sites. Also, costs of protection may be greater and survival benefits less than indicated by present evidence. (2) Obligately feeding pelagic larvae migrate to the plankton for greater growth or lower mortality. Under this hypothesis, the observed large scale dispersal is an incidental byproduct of a migration from benthic to pelagic habitat for greater growth or survival. Given pelagic development, unevenly favorable benthic environments and varying currents might result in selection for spread of sibling larvae as a form of bet hedging, but selection for dispersal as a result of pelagic development does not explain evolution of pelagic development. (3) For larvae released near competence for settlement and metamorphosis, the function of the pelagic stage is dispersal and habitat selection. For these larvae, the observed precompetent pelagic periods of seconds to a day may indicate pelagic durations adapted for dispersal because offspring could be protected until release at competence.
Study on photosynthetic compensatory effects of PEG osmotic stress and rewatering on maize

Simulated water stress experiment on the five-leaf stage of maize growth with different concentrations of polyethylene glycol(PEG) in nutrient solution was performed to study the effects of water stress and rewatering on chlorophyll content,chlorophyll fluorescence kinetic parameters,photosynthetic parameters and soluble sugar content.The results indicated that the chlorophyll content,the maximum quantum yield of PS II primary photochemistry(Fv/Fm) and potential activity(Fv/Fo),and photosynthetic rate of leaves decreased,but soluble sugar content increased under water stress,and the tendency was positively related to the intensity of water stress.Meanwhile,the chlorophyll content,Fv/Fm and Fv/Fo,photosynthetic rate,and soluble sugar content gradually recovered after rewatering,resulting in compensation and overcompensation effects,and the time for recovery was positively related to the intensity of water stress.It is concluded that water stress restrains plant growth,decreases the consumption of assimilation,and leads to the accumulation of assimilation in leaves,and thus results in the decrease of photosynthetic rate.The results above suggested that there might be a positive feedback between compensatory growth and compensation of photosynthetic rate after rewatering.
The Distribution of Leopard Frogs (Rana blairi and Rana pipiens) (Amphibia, Anura, Ranidae) in Nebraska

Extensive collecting of leopard frogs (Rana blairi and R. pipiens) in Nebraska reveals that the large zone of sympatry heretofore recognized in central and eastern Nebraska is fragmented. Rana pipiens occurs as disjunct populations on the isolated Sandhills formations in south-central Nebraska. Within the largest zone of sympatry (in northeast Nebraska) hybridization is infrequent (hybrids comprise usually less than 5% of the pooled populations). Ecologic isolation is in part effected by selection of different substrates by the two species. Rana blairi occurs in loess soil areas whereas R,. pipiens predominates in areas of sandy soils.
Crystal orientation and detector distance effects on resolving pseudosymmetry by electron backscatter diffraction

Accurately indexing pseudosymmetric materials has long proven challenging for electron backscatter diffraction. The recent emergence of intensity-based indexing approaches promises an enhanced ability to resolve pseudosymmetry compared to traditional Hough-based indexing approaches. However, little work has been done to understand the effects of sample position and orientation on the ability to resolve pseudosymmetry, especially for intensity-based indexing approaches. Thus, in this work we quantitatively investigate the effects of crystal orientation and detector distance in a model tetragonal ZrO2 (c/a=1.0185) material. We identify orientations that are easiest and most difficult to correctly index, characterize the effect of detector distance on indexing confidence, and analyze these trends based on the appearance of specific zone axes in the diffraction patterns. Our findings also point to the clear benefit of shorter detector distances for resolving pseudosymmetry using intensity-based indexing approaches.
Tunneling through an eternal traversable wormhole

The Maldancena-Qi model describes two copies of the Sachdev-Ye-Kitaev model coupled with an additional coupling, and is dual to the Jackiw-Teitelboim gravity which exhibits eternal traversable wormhole in the low-temperature limit. In this work, we study the experimental consequence of the existence of the traversable wormhole by considering the tunneling spectroscopy for the Maldancena-Qi model. Comparing to the high-temperature black hole phase where the bulk geometry is disconnected, we find both the tunneling probability and the differential conductance in the low-temperature wormhole phase show non-trivial oscillation, which directly provides an unambiguous signature of the underlying $SL(2)$ symmetry of the bulk geometry. We also perform bulk calculations in both high and low-temperature phases, which match the results from the boundary quantum theory.
Molecular characterization, spread and evolution of quinolones resistance in E. coli of avian origin.

Resistance to flruroquinolones encouraged us to study the resistance of E. coli isolates from avian origin in Egypt. A total 65 from 150 samples were positive for E coli isolates was screened against 5 quinolones discs and also for five representing genes (qnrA, qnrB, qnrS, qepA and aac(6′)-Ib-cr). The absolute resistance to three out of five antimicrobial discs appeared in duck isolates; also chicken isolates showed resistance 100% to nalidixic acid and as totally the higher percentage for the examined resistance genes especially that has the role in decreasing the sensitivity to nofloxacin, cibrofloxacin and enrofloxacin. Regarding the total incidence of isolation of E. coli was 43.3 %. And serotyping of the isolates revealed 19, 13 and 7 O groups in chickens, Ducks and turkeys respectively.
False discovery rate control with e‐values

E‐values have gained attention as potential alternatives to p‐values as measures of uncertainty, significance and evidence. In brief, e‐values are realized by random variables with expectation at most one under the null; examples include betting scores, (point null) Bayes factors, likelihood ratios and stopped supermartingales. We design a natural analogue of the Benjamini‐Hochberg (BH) procedure for false discovery rate (FDR) control that utilizes e‐values, called the e‐BH procedure, and compare it with the standard procedure for p‐values. One of our central results is that, unlike the usual BH procedure, the e‐BH procedure controls the FDR at the desired level—with no correction—for any dependence structure between the e‐values. We illustrate that the new procedure is convenient in various settings of complicated dependence, structured and post‐selection hypotheses, and multi‐armed bandit problems. Moreover, the BH procedure is a special case of the e‐BH procedure through calibration between p‐values and e‐values. Overall, the e‐BH procedure is a novel, powerful and general tool for multiple testing under dependence, that is complementary to the BH procedure, each being an appropriate choice in different applications.
Investigation of the Elliptic Flow Fluctuations of the Identified Particles Using the a Multi-Phase Transport Model

A Multi-Phase Transport (AMPT) model is used to study the elliptic flow fluctuations of identified particles using participant and spectator event planes. The elliptic flow measured using the first order spectator event plane is expected to give the elliptic flow relative to the true reaction plane which suppresses the flow fluctuations. However, the elliptic flow measured using the second-order participant plane is expected to capture the elliptic flow fluctuations. Our study shows that the first order spectator event plane could be used to study the elliptic flow fluctuations of the identified particles in the AMPT model. The elliptic flow fluctuations magnitude shows weak particle species dependence and transverse momentum dependence. Such observation will have important implications for understanding the source of the elliptic flow fluctuations.
Sparse Systolic Tensor Array for Efficient CNN Hardware Acceleration

Convolutional neural network (CNN) inference on mobile devices demands efficient hardware acceleration of low-precision (INT8) general matrix multiplication (GEMM). Exploiting data sparsity is a common approach to further accelerate GEMM for CNN inference, and in particular, structural sparsity has the advantages of predictable load balancing and very low index overhead. In this paper, we address a key architectural challenge with structural sparsity: how to provide support for a range of sparsity levels while maintaining high utilization of the hardware. We describe a time unrolled formulation of variable density-bound block (VDBB) sparsity that allows for a configurable number of non-zero elements per block, at constant utilization. We then describe a systolic array microarchitecture that implements this scheme, with two data reuse optimizations. Firstly, we increase reuse in both operands and partial products by increasing the number of MACs per PE. Secondly, we introduce a novel approach of moving the IM2COL transform into the hardware, which allows us to achieve a 3x data bandwidth expansion just before the operands are consumed by the datapath, reducing the SRAM power consumption. The optimizations for weight sparsity, activation sparsity and data reuse are all interrelated and therefore the optimal combination is not obvious. Therefore, we perform an design space evaluation to find the pareto-optimal design characteristics. The resulting design achieves 16.8 TOPS/W in 16nm with modest 50% model sparsity and scales with model sparsity up to 55.7TOPS/W at 87.5%. As well as successfully demonstrating the variable DBB technique, this result significantly outperforms previously reported sparse CNN accelerators.
Uncertainty Inspired RGB-D Saliency Detection

We propose the first stochastic framework to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Existing RGB-D saliency detection models treat this task as a point estimation problem by predicting a single saliency map following a deterministic learning pipeline. We argue that, however, the deterministic solution is relatively ill-posed. Inspired by the saliency data labeling process, we propose a generative architecture to achieve probabilistic RGB-D saliency detection which utilizes a latent variable to model the labeling variations. Our framework includes two main models: 1) a generator model, which maps the input image and latent variable to stochastic saliency prediction, and 2) an inference model, which gradually updates the latent variable by sampling it from the true or approximate posterior distribution. The generator model is an encoder-decoder saliency network. To infer the latent variable, we introduce two different solutions: i) a Conditional Variational Auto-encoder with an extra encoder to approximate the posterior distribution of the latent variable; and ii) an Alternating Back-Propagation technique, which directly samples the latent variable from the true posterior distribution. Qualitative and quantitative results on six challenging RGB-D benchmark datasets show our approach's superior performance in learning the distribution of saliency maps. The source code is publicly available via our project page: https://github.com/JingZhang617/UCNet.
Inheritance of low erucic acid in Indian mustard [Brassica juncea (L.) Czern. and Coss.]

Genetics of erucic acid content was studied using six generations viz., P1, P2, F1, F2, B1 and B2 derived from the crosses, Varuna × LES-39 and Varuna × LES-1-27. The trait high erucic acid content was partially dominant over low erucic acid content in both the crosses. In both the crosses, adequacy of additive – dominance model suggested absence of non-allelic interactions. The study revealed that selection for low erucic acid would result in isolation of plants with high oleic and linoleic acids and hence, it should be possible to develop high oleic acid lines having low erucic acid content in mustard.
Simultaneous Energy Harvesting and Gait Recognition Using Piezoelectric Energy Harvester

Piezoelectric energy harvester (PEH), which generates electricity from stress or vibrations, is attracting tremendous attention as a viable solution to extend battery life of wearable devices. More interestingly, besides the energy harvesting capability, recent research has demonstrated the feasibility of leveraging PEH as an power-free sensor for gait recognition as its stress or vibration patters are significantly influenced by the gait. However, as PEHs are not designed for precise motion sensing, the gait recognition accuracy remains low with conventional classification algorithms. The accuracy deteriorates further when the generated electricity is stored simultaneously. In this work, to achieve high performance gait recognition and efficient energy harvesting at the same time, we make two distinct contributions. First, we propose a preprocessing algorithm to filter out the effect of energy storage on PEH electricity signals. Second, we propose long short-term memory (LSTM) network-based classifiers to accurately capture temporal information in gait-induced electricity generation. We prototype the proposed gait recognition architecture in the form factor of an insole and evaluate its gait recognition as well as energy harvesting performance with 20 subjects. Our results show that the proposed architecture detects human gait with 12 percent higher recall and harvests up to 127 percent more energy while consuming 38 percent less power compared to the state-of-the-art.
A Thread-Local Semantics and Efficient Static Analyses for Race Free Programs

Data race free (DRF) programs constitute an important class of concurrent programs. In this paper we provide a framework for designing and proving the correctness of data flow analyses that target this class of programs. These analyses are in the same spirit as the "sync-CFG" analysis proposed in earlier literature. To achieve this, we first propose a novel concrete semantics for DRF programs, called L-DRF, that is thread-local in nature---each thread operates on its own copy of the data state. We show that abstractions of our semantics allow us to reduce the analysis of DRF programs to a sequential analysis. This aids in rapidly porting existing sequential analyses to sound and scalable analyses for DRF programs. Next, we parameterize L-DRF with a partitioning of the program variables into "regions" which are accessed atomically. Abstractions of the region-parameterized semantics yield more precise analyses for "region-race" free concurrent programs. We instantiate these abstractions to devise efficient relational analyses for race free programs, which we have implemented in a prototype tool called RATCOP. On the benchmarks, RATCOP was able to prove up to 65% of the assertions, in comparison to 25% proved by our baseline. Moreover, in a comparative study with a recent concurrent static analyzer, RATCOP was up to 5 orders of magnitude faster.
Giant orbital polarization of 
Ni2+
 in a square planar environment

Understanding the electronic behavior of Ni$^{2+}$ in a square planar environment of oxygen is the key to unravel the origin of the recently discovered superconductivity in the hole doped nickelate Nd$_{0.8}$Sr$_{0.2}$NiO$_2$. To identify the major similarities/dissimilarities between nickelate and cuprate superconductivity, the study of the electronic structure of Ni$^{2+}$ and Cu$^{2+}$ in an identical square planar environment is essential. In order to address these questions, we investigate the electronic structure of Sr$_2$CuO$_3$ and Ni doped Sr$_2$CuO$_3$ single crystals containing (Cu/Ni)O$_4$ square planar units. Our polarization dependent X-ray absorption spectroscopy experiments for Ni in Sr$_2$Cu$_{0.9}$Ni$_{0.1}$O$_3$ have revealed very large orbital polarization, which is a characteristic feature of high $T_c$ cuprate. This arises due to the low spin $S$=0 configuration with two holes in Ni 3$d_{x^2-y^2}$ orbitals - in contrast to the expected high spin $S$=1 state from Hund's first rule. The presence of such $S$=0 Ni$^{2+}$ in hole doped nickelate would be analogous to the Zhang Rice singlet. However, the Mott Hubbard insulating nature of the NiO$_4$ unit would point towards a different electronic phase space of nickelates, compared to high $T_c$ cuprates.
A Grant-Based Random Access Protocol in Extra-Large Massive MIMO System

Extra-large massive multiple-input multiple-output (XL-MIMO) systems is a new concept, where spatial non-stationarities allow activate a high number of user equipments (UEs). This letter focuses on a grant-based random access (RA) approach in the novel XL-MIMO channel scenarios. Modifications in the classical Strongest User Collision Resolution (SUCRe) protocol have been aggregated to explore the visibility regions (VRs) overlapping in XL-MIMO. The proposed grant-based RA protocol takes advantage of this new degree of freedom for improving the number of access attempts and accepted UEs. As a result, the proposed grant-based protocol for XL-MIMO systems is capable of reducing latency in the pilot allocation step.
Class Interference Regularization

Contrastive losses yield state-of-the-art performance for person re-identification, face verification and few shot learning. They have recently outperformed the cross-entropy loss on classification at the ImageNet scale and outperformed all self-supervision prior results by a large margin (SimCLR). Simple and effective regularization techniques such as label smoothing and self-distillation do not apply anymore, because they act on multinomial label distributions, adopted in cross-entropy losses, and not on tuple comparative terms, which characterize the contrastive losses. Here we propose a novel, simple and effective regularization technique, the Class Interference Regularization (CIR), which applies to cross-entropy losses but is especially effective on contrastive losses. CIR perturbs the output features by randomly moving them towards the average embeddings of the negative classes. To the best of our knowledge, CIR is the first regularization technique to act on the output features. In experimental evaluation, the combination of CIR and a plain Siamese-net with triplet loss yields best few-shot learning performance on the challenging tieredImageNet. CIR also improves the state-of-the-art technique in person re-identification on the Market-1501 dataset, based on triplet loss, and the state-of-the-art technique in person search on the CUHK-SYSU dataset, based on a cross-entropy loss. Finally, on the task of classification CIR performs on par with the popular label smoothing, as demonstrated for CIFAR-10 and -100.
Open Multi-Agent Systems with Variable Size: the Case of Gossiping

We consider open multi-agent systems, which are systems subject to frequent arrivals and departures of agents while the process studied takes place. We study the behavior of all-to-all pairwise gossip interactions in such open systems. Arrivals and departures of agents imply that the composition and size of the system evolve with time, and in particular prevent convergence. We describe the expected behavior of the system by showing that the evolution of scale-independent quantities can be characterized exactly by a fixed size linear dynamical system. We apply this approach to characterize the evolution of the two first moments (and thus also of the variance) for open systems of both fixed and variable size. Our approach is based on the continuous time modelling of random asynchronous events, namely gossip steps, arrivals, departures, and replacements.
Non-exponentially weighted aggregation: regret bounds for unbounded loss functions

We tackle the problem of online optimization with a general, possibly unbounded, loss function. It is well known that the exponentially weighted aggregation strategy (EWA) leads to a regret in $\sqrt{T}$ after $T$ steps, under the assumption that the loss is bounded. The online gradient algorithm (OGA) has a regret in $\sqrt{T}$ when the loss is convex and Lipschitz. In this paper, we study a generalized aggregation strategy, where the weights do no longer necessarily depend exponentially on the losses. Our strategy can be interpreted as the minimization of the expected losses plus a penalty term. When the penalty term is the Kullback-Leibler divergence, we obtain EWA as a special case, but using alternative divergences lead to a regret bounds for unbounded, not necessarily convex losses. However, the cost is a worst regret bound in some cases.
Hyperfine-mediated effects in a 
Lu+
 optical clock

We consider hyperfine-mediated effects for clock transitions in $^{176}$Lu$^+$. Mixing of fine structure levels due to the hyperfine interaction bring about modifications to Lande $g$-factors and the quadrupole moment for a given state. Explicit expressions are derived for both $g$-factor and quadrupole corrections, for which leading order terms arise from the nuclear magnetic dipole coupling. High accuracy measurements of the $g$-factors for the $^1S_0$ and $^3D_1$ hyperfine levels are carried out, which provide an experimental determination of the leading order correction terms.