Miracle-dz/UniFAR-FTUs huggingface dataset:
Scheme for the generation of hybrid entanglement between time-bin and wavelike encodings

We propose a scheme for the generation of hybrid states entangling a single photon time-bin qubit with a coherent state qubit encoded on phases. Compared to other reported solutions, time-bin encoding makes hybrid entanglement particularly well adapted to applications involving long distance propagation in optical fibres. This makes our proposal a promising resource for future out-of-the-laboratory quantum communication. In this perspective, we analyse our scheme by taking into account realistic experimental resources and discuss the impact of their imperfections on the quality of the obtained hybrid state.
Testing KiDS cross-correlation redshifts with simulations

Measuring cosmic shear in wide-field imaging surveys requires accurate knowledge of the redshift distribution of all sources. The clustering-redshift technique exploits the angular cross-correlation of a target galaxy sample with unknown redshifts and a reference sample with known redshifts. It represents an attractive alternative to colour-based methods of redshift calibration. Here we test the performance of such clustering redshift measurements using mock catalogues that resemble the Kilo-Degree Survey (KiDS). These mocks are created from the MICE simulation and closely mimic the properties of the KiDS source sample and the overlapping spectroscopic reference samples. We quantify the performance of the clustering redshifts by comparing the cross-correlation results with the true redshift distributions in each of the five KiDS photometric redshift bins. Such a comparison to an informative model is necessary due to the incompleteness of the reference samples at high redshifts. Clustering mean redshifts are unbiased at |Δz|< 0.006 under these conditions. The redshift evolution of the galaxy bias of the reference and target samples represents one of the most important systematic errors when estimating clustering redshifts. It can be reliably mitigated at this level of precision using auto-correlation measurements and self-consistency relations, and will not become a dominant source of systematic error until the arrival of Stage-IV cosmic shear surveys. Using redshift distributions from a direct colour-based estimate instead of the true redshift distributions as a model for comparison with the clustering redshifts increases the biases in the mean to up to |Δz|∼0.04. This indicates that the interpretation of clustering redshifts in real-world applications will require more sophisticated (parameterised) models of the redshift distribution in the future. If such better models are available, the clustering-redshift technique promises to be a highly complementary alternative to other methods of redshift calibration.
The Combined Basic LP and Affine IP Relaxation for Promise VCSPs on Infinite Domains

Convex relaxations have been instrumental in solvability of constraint satisfaction problems (CSPs), as well as in the three different generalisations of CSPs: valued CSPs, infinite-domain CSPs, and most recently promise CSPs. In this work, we extend an existing tractability result to the three generalisations of CSPs combined: We give a sufficient condition for the combined basic linear programming and affine integer programming relaxation for exact solvability of promise valued CSPs over infinite-domains. This extends a result of Brakensiek and Guruswami (SODA’20) for promise (non-valued) CSPs (on finite domains).
The Antiinflammatory Sesquiterpene Lactone Parthenolide Inhibits NF-κB by Targeting the IκB Kinase Complex

The transcription factor NF-κB is a key regulator of the cellular inflammatory and immune response. Therefore, components of the NF-κB-activating signaling pathways are frequent targets for antiinflammatory agents. This study shows that the sesquiterpene lactone parthenolide inhibits a common step in NF-κB activation by preventing the TNF-α-induced induction of IκB kinase (IKK) and IKKβ, without affecting the activation of p38 and c-Jun N-terminal kinase. Parthenolide impairs NF-κB-dependent transcription triggered by expression of TNFR-associated factor-2, mitogen-activated protein kinase/extracellular signal-regulated kinase kinase (MEKK1), and NF-κB-inducing kinase. This compound also prevents activation of both IKKs and DNA binding of NF-κB induced by MEKK and NF-κB-inducing kinase. Parthenolide targets a component of the IκB kinase complex without directly inhibiting IKKα, IKKβ, or MEKK1. Therefore, this sesquiterpene lactone could serve as a lead compound for the development of antiinflammatory remedies and is suitable as a molecular tool, allowing the dissection of TNF-α-derived signaling pathways leading to the activation of NF-κB, c-Jun N-terminal kinase, and p38.
Distribution and trend in abundance of the porbeagle (Lamna nasus) in the southern hemisphere

Knowledge of a species’ distribution is an important element in its effective management and conservation. The porbeagle (Lamna nasus) is a common by-catch shark in the tuna longline fishery in the southern hemisphere, but its distribution and abundance are largely unknown. The investigation of observer data from the tuna longline fishery and other fishery survey data has revealed that (1) porbeagles are distributed in the pelagic waters across the oceans of the southern hemisphere, (2) juveniles and adults are distributed in cooler environments than are neonates, (3) pregnant females occur in the pelagic waters of the Indian Ocean and the Tasman Sea, most being frequently recorded around the Cape of Good Hope between June and July and (4) the standardised catch per unit effort (CPUE) based on tuna longline fishery (1994–2011) and driftnet survey (1982–1990) data indicate no continuous decreasing trend in the abundance of the southern porbeagle, contrary to the declining trend reported in a limited region in the South Atlantic. Considering its circumglobal distribution, stock status of this population should be assessed using information from the areas of its major distribution, including pelagic waters, and international coordination across oceans is necessary for the effective management of this population.
Towards Robust Deep Learning with Ensemble Networks and Noisy Layers

In this paper we provide an approach for deep learning that protects against adversarial examples in image classification-type networks. The approach relies on two mechanisms:1) a mechanism that increases robustness at the expense of accuracy, and, 2) a mechanism that improves accuracy but does not always increase robustness. We show that an approach combining the two mechanisms can provide protection against adversarial examples while retaining accuracy. We formulate potential attacks on our approach and provide experimental results to demonstrate the effectiveness of our approach.
Dueling Deep Q-Network For Unsupervised Inter-Frame Eye Movement Correction In Optical Coherence Tomography Volumes

In optical coherence tomography (OCT) volumes of retina, the sequential acquisition of the individual slices makes this modality prone to motion artifacts, misalignments between adjacent slices being the most noticeable. Any distortion in OCT volumes can bias structural analysis and influence the outcome of longitudinal studies. The presence of speckle noise characteristic of this imaging modality leads to inaccuracies when traditional registration techniques are employed. Also, the lack of a well-defined ground truth makes supervised deep-learning techniques ill-posed to tackle the problem. In this paper, we tackle these issues by using deep reinforcement learning to correct inter-frame movements in an unsupervised manner. Specifically, we use dueling deep Q-network to train an artificial agent to find the optimal policy, i.e. a sequence of actions, that best improves the alignment by maximizing the sum of reward signals. Instead of relying on the ground-truth of transformation parameters to guide the rewarding system, for the first time, we use a combination of intensity based image similarity metrics. Further, to avoid the agent bias towards speckle noise, we ensure the agent can see retinal layers as part of the interacting environment. For quantitative evaluation, we simulate the eye movement artifacts by applying 2D rigid transformations on individual B-scans. The proposed model achieves an average of 0.985 and 0.914 for normalized mutual information and correlation coefficient, respectively. We also compare our model with elastix intensity based medical image registration approach, where significant improvement is achieved by our model for both noisy and denoised volumes.
Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers

Choosing the optimizer is among the most crucial decisions of deep learning engineers, and it is not an easy one. The growing literature now lists literally hundreds of optimization methods. In the absence of clear theoretical guidance and conclusive empirical evidence, the decision is often done according to personal anecdotes. In this work, we aim to replace these anecdotes, if not with evidence, then at least with heuristics. To do so, we perform an extensive, standardized benchmark of more than a dozen particularly popular deep learning optimizers while giving a concise overview of the wide range of possible choices. Analyzing almost 35 000 individual runs, we contribute the following three points: Optimizer performance varies greatly across tasks. We observe that evaluating multiple optimizers with default parameters works approximately as well as tuning the hyperparameters of a single, fixed optimizer. While we can not identify an individual optimization method clearly dominating across all tested tasks, we identify a significantly reduced subset of specific algorithms and parameter choices that generally provided competitive results in our experiments. This subset includes popular favorites and some less well-known contenders. We have open-sourced all our experimental results, making it available to use as well-tuned baselines when evaluating novel optimization methods and therefore reducing the necessary computational efforts.
Joint Frequency- and Image-Space Learning for Fourier Imaging

We propose a neural network layer structure that combines frequency and image feature representations for robust Fourier image reconstruction. Our work is motivated by the challenges in magnetic resonance imaging (MRI) where the acquired signal is a corrupted Fourier transform of the desired image. The proposed layer structure enables both correction of artifacts native to the frequency-space and manipulation of image-space representations to reconstruct coherent image structures. This is in contrast to the current deep learning approaches for image reconstruction that manipulate data solely in the frequency-space or solely in the image-space. We demonstrate the advantages of the proposed joint learning on three diverse tasks including image reconstruction from undersampled acquisitions, motion correction, and image denoising in brain MRI. Unlike purely image based and purely frequency based architectures, the proposed joint model produces consistently high quality output images. The resulting joint frequency- and image-space feature representations promise to significantly improve modeling and reconstruction of images acquired in the frequency-space. Our code is available at this https URL.
Modelling forest loss and other land use change dynamics in Ashanti Region of Ghana

Abstract Forest losses amid land use dynamics have become issues of outermost concern in the light of climate change phenomenon which has captivated the world’s attention. It is imperative to monitor land use change and to forecast forms of future land use change on a temporal and spatial basis. The main thrust of this study is to assess land use change in the lower half of the Ashanti Region of Ghana within a 40 year period. The analysis of land use change uses a combination method in Remote Sensing (RS) and Geographic Information System (GIS). Cellular Automata and Markov Chain (Cellular Automata-Markov) are utilized to predict for land use land cover (LULC) change for 2020 and 2030. The processes used include: (i) a data pre-processing (geometric corrections, radiometric corrections, subset creation and image enhancement) of epoch Landsat images acquired in 1990, 2000, and Disaster Monitoring Constellation (DMC) 2010; (ii) classification of multispectral imagery (iii) Change detection mapping (iv) using Cellular Automata-Markov to generate land use change in the next 20 years. The results illustrate that in years 2020 to 2030 in the foreseeable future, there will an upsurge in built up areas, while a decline in agricultural land use is envisaged. Agricultural land use would still be the dominant land use type. Forests would be drastically reduced from close to 50% in 1990 to just fewer than 10% in 2030. Land use decision making must be very circumspect, especially in an era where Ghana has opted to take advantage of REDD+. Studies such as this provide vital pieces of information which may be used to monitor, direct and influence land use change to a more beneficial and sustainable manner
Leaf and Soil Nutrient Diagnostic Norms for Pomegranate (Punica granatum L)

A survey was conducted in pomegranate orchards in Bijapur district of northern Karnataka and the diagnostic norms for plant and soil were developed using Diagnosis and Recommendation Integrated System (DRIS). The optimum range of N in leaf (8th leaf pair from tip collected in the month of August) were as follows: 0.91 to 1.66%; P 0.12 to 0.18% and K0.61 to 1.59%. The optimum ranges in soil were: pH 8.1 to 8.6; available N 44 to 103 mg kg−1; available P 10 to 20 mg kg−1; and available K 73 to 115 mg kg−1. An yield level of pomegranate ranging from 15.5 to 18.8 t ha−1 was recorded for the optimum ranges of nutrients indicated above. The norms for other nutrients, the evaluation and classification are reported and discussed.
Language-agnostic BERT Sentence Embedding

While BERT is an effective method for learning monolingual sentence embeddings for semantic similarity and embedding based transfer learning BERT based cross-lingual sentence embeddings have yet to be explored. We systematically investigate methods for learning multilingual sentence embeddings by combining the best methods for learning monolingual and cross-lingual representations including: masked language modeling (MLM), translation language modeling (TLM), dual encoder translation ranking, and additive margin softmax. We show that introducing a pre-trained multilingual language model dramatically reduces the amount of parallel training data required to achieve good performance by 80%. Composing the best of these methods produces a model that achieves 83.7% bi-text retrieval accuracy over 112 languages on Tatoeba, well above the 65.5% achieved by LASER, while still performing competitively on monolingual transfer learning benchmarks. Parallel data mined from CommonCrawl using our best model is shown to train competitive NMT models for en-zh and en-de. We publicly release our best multilingual sentence embedding model for 109+ languages at https://tfhub.dev/google/LaBSE.
Timing and Resource-Aware Mapping of Quantum Circuits to Superconducting Processors

Quantum algorithms need to be compiled to respect the constraints imposed by quantum processors, which is known as the mapping problem. The mapping procedure will result in an increase of the number of gates and of the circuit latency, decreasing the algorithm’s success rate. It is crucial to minimize mapping overhead, especially for noisy intermediate-scale quantum (NISQ) processors that have relatively short qubit coherence times and high gate error rates. Most of prior mapping algorithms have only considered constraints, such as the primitive gate set and qubit connectivity, but the actual gate duration and the restrictions imposed by the use of shared classical control electronics have not been taken into account. In this article, we present a mapper called Qmap to make quantum circuits executable on scalable processors with the objective of achieving the shortest circuit latency. In particular, we propose an approach to formulate the classical control restrictions as resource constraints in a conventional list scheduler with polynomial complexity. Furthermore, we implement a routing heuristic to cope with the connectivity limitation. This router finds a set of movement operations that minimally extends circuit latency. To analyze the mapping overhead and evaluate the performance of different mappers, we map 56 quantum benchmarks onto a superconducting processor named Surface-17. Compared to a prior mapping strategy that minimizes the number of operations, Qmap can reduce the latency overhead (LtyOH) up to 47.3% and operation overhead up to 28.6%, respectively.
INDUCTION OF SOMATIC EMBRYOGENESIS FROM LEAF EXPLANTS OF Stevia rebaudiana

Somatic embryos of Stevia rebaudiana (Bert.) Bertoni were induced when in vitro leaves were cultivated on MS media with 2,4-D (10 or 25 μM), BA (l μM) in a high sucrose medium (120 g/L). The embryos appeared to be formed directly without callus formation. Somatic embryos failled to mature and developed roots but not shoots on a MS medium without growth regulators. Additional index terms: BA, 2,4-D, estevioside.
Factors Influencing Recovery of Heat‐injured Bacillus thuringiensis Spores. Statistical Approach

The influence of several factors on recovery of heat-treated Bacillus thuringiensis spores was examined including treatment conditions (time/temperature), dilution buffer (phosphate buffer, peptone water at 15 mg/L), recovery temperature and enumeration medium constituents (lysozyme, catalase, lactic acid hemicalcium salt, glucose, MgSO 4 , sodium chloride). A fractional factorial design was used to determine the incidence and interactions of these factors. Specific significant factors (treatment conditions, dilution buffer, recovery temperature, glucose and NaCl addition) were derived from this analysis and then tested in a general linear regression model which assessed their influences. The maximal count was obtained when spores diluted in peptone buffer were incubated at 25 C in nutrient agar containing NaCl and glucose.
Occurrence of ocean sunfish, Mola mola near fronts in the western English Channel

Surface occurrence of ocean sunfish (Mola mola) was recorded during summer (May–September) in the western English Channel off Plymouth over a six-year period between 1995 and 2001. Fifteen individuals of between 0.5–0.7 m estimated total length were sighted during 1651 hours of observation. Nearly all sightings (93%) occurred in June and July in water between 13 and 17°C. Sunfish were mostly associated with frontal and stratified water masses (86%) rather than in cooler, mixed water.
Parasites of the American Kestrel (Falco sparverius) in South-Central Chile

PARASITOS DE FALCO SPARVERIUS EN EL SUR DE CHILE Durante 9 anos, desde marzo de 2001 hasta diciembre de 2009, colectamos los parasitos externos y gastrointestinales de 15 halcones Falco sparverius cinnamominus de un centro de rehabilitacion en la Region del Biobio en el centro-sur de Chile. Los helmintos colectados incluyeron una especie de trematodo (Ascocotyle felippei), una especie de cestodo (Cladotaenia sp.), y tres especies de nematodos (Baruscapillaria falconis, Physaloptera sp. y Procyrnea sp.). Los ectoparasitos colectados incluyeron una especie de acaro (Epoplichus minor) y tres especies de piojos (Degeeriella carruthi, Laemobothrion tinnunculi y Colpocephalum subzerafae). Seis de las nueve especies de parasitos descritos son nuevos registros para Chile, asi mismo las especies A. felippei, Cladotaenia sp. y E. minor son nuevos registros para el F. sparverius cinnamominus. [Traduccion del equipo editorial]
Predicting Chinese Bond Market Turbulences: Attention-BiLSTM Based Early Warning System

The study aims to construct an effective early warning system (EWS) to predict the crisis triggered turbulence in Chinese bond market by integrating the volatility regime switching model, SWARCH, to improve the crisis classifying precision, and the stylized predictive model, Attention-BiLSTM of attention mechanism based deep neural networks, to resolve the predicting hysteresis. The model versatility and comparability are investigated and testified by applying multiple prominent EWS models to bonds with different credit rating levels. The hybrid EWS also specifies the leading factors relating to the bond credit rating, that will practically instruct governors and market participants to focus on either the national economy associated or the corporate finance concerned factors according to the bond varying credit risks to make more effective predictions.
Person Identification Based On Sign Language Motion: Insights From Human Perception And Computational Modeling

Previous research has shown that human perceivers can identify individuals from biological movements, such as walking or dancing. It remains to be investigated whether sign language motion, which obeys to other constraints than pure biomechanical ones, also allows for person identification. The present study is the first to investigate whether deaf perceivers recognize signers based on motion capture (mocap) data only. Point-light displays of 4 signers producing French Sign Language utterances were presented to a group of deaf participants. Results revealed that participants managed to identify familiar signers above chance level. Computational analysis of the mocap data provided further evidence that morphological cues were unlikely to be sufficient for signer identification. A machine learning approach aiming to evaluate the motion features that can account for human performance is currently being developed. First results of the model reveal high accuracy for signer identification based on the same stimulus material, even after having normalized for size and shape. The present behavioral and computational findings suggest that mocap data contain sufficient information to identify signers, and this beyond simple cues related to morphology.
Response of planting dates and genotypes on vegetative and floral characters in gladiolus

An experiment was conducted to study the effect of six dates of planting along with four genotypes on vegetative and floral characters of gladiolus. Planting of corms on 31 March resulted into the maximum plant height (119.12cm), spike length (98.06 cm), floret number/spike (13.20) and floret diameter (9.77 cm). Whereas, the minimum number of days for sprouting (10.88), days to slipping (74.27) and days to basal floret opening (86.15) were recorded for corms planted on 15 May. Among the four genotypes evaluated, Red Majesty was significantly superior to other genotypes in respect of plant height (117.71 cm) and spike length (92.47 cm). However, minimum number of days to spouting (20.88), slipping (76.85) and basal floret opening (89.42) and duration of spikes in field (8.65 day) were recorded for variety Suchitra, whereas the number of florets/spike (12.29) and floret diameter (9.93 cm) was significantly higher in Big Time Supreme, The interaction between different genotypes and planting dates for most of the traits was observed significant.