Miracle-dz/UniFAR-FTUs huggingface dataset:
Lattice (List) Decoding Near Minkowski’s Inequality

Minkowski proved that any <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-dimensional lattice of unit determinant has a nonzero vector of Euclidean norm at most <inline-formula> <tex-math notation="LaTeX">$\sqrt {n}$ </tex-math></inline-formula>; in fact, there are <inline-formula> <tex-math notation="LaTeX">$2^{\Omega (n)}$ </tex-math></inline-formula> such lattice vectors. Lattices whose minimum distances come close to Minkowski’s bound provide excellent sphere packings and error-correcting codes in <inline-formula> <tex-math notation="LaTeX">${\mathbb {R}}^{n}$ </tex-math></inline-formula>. The focus of this work is a certain family of efficiently constructible <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-dimensional lattices due to Barnes and Sloane, whose minimum distances are within an <inline-formula> <tex-math notation="LaTeX">$O(\sqrt {\log n})$ </tex-math></inline-formula> factor of Minkowski’s bound. Our primary contribution is a polynomial-time algorithm that <italic>list decodes</italic> this family to distances approaching <inline-formula> <tex-math notation="LaTeX">$1/\sqrt {2}$ </tex-math></inline-formula> of the minimum distance. The main technique is to decode Reed-Solomon codes under error measured in the Euclidean norm, using the Koetter-Vardy “soft decision” variant of the Guruswami-Sudan list-decoding algorithm.
Better Together? Performance Dynamics in Retail Chain Expansion before and after Mergers (cid:3)

This paper evaluates how mergers a⁄ect the performance e¢ ciency of retail chains. We estimate a dynamic model of retail expansion using data on convenience-store chains in Japan before and after an actual merger event. Our estimation allows for the presence of performance e¢ ciency, in the form of serially correlated state variables that evolve both endogenously and stochastically. The estimates reveal that although the merged (cid:133)rm bene(cid:133)ted from lower expansion costs, underlying performance e¢ ciency for the merged entity did not improve following the merger, and such changes in performance varied across markets. Simulation analysis reveals the dampened performance is associated with the merged (cid:133)rm(cid:146)s diminished ability to retain e¢ ciency gains from one year to the next. However, these negative e⁄ects can be mitigated if the merged (cid:133)rm inherits the primitives behind the performance e¢ ciency of the more dominant merging party.
Evaluation of Hypoglycaemic and Anti Hyperglycaemic Activity of the Aqueous Extract of the Roots of Jatropha curcas L. (Euphorbiacées)

DOI: 10.21276/sjmps.2018. 4.12. 7 Abstract: The species Jatropha curcas of the family Euphorbiaceae, widespread in Central Africa, is a shrub 5 to 8 m high; widely used in traditional medicine. The interest of the present study is the evaluation of the hypoglycemic and antihyperglycemic activity of the aqueous extract of its roots and the formulation of capsules from the dry extract. After the harvest of Jatropha curcas in the region of the center Cameroon, roots underwent clearly a decoction in distilled water, maceration in ethanol 80 % and in pure methanol. Characterization of secondary metabolites families was performed by coloring and phytochemical precipitation tests. Subsequently, some of these metabolites were measured from standards. The animal material (male and female rats) was prepared for demonstration of hypoglycemic and antihyperglycaemic activity using as method the measurement of blood glucose every 30 minutes for 3 hours. The determination of the yields of the extracts showed a significant profitability, 8,14% for the aqueous extract. Phytochemical screening analysis and determination of phenolic compounds revealed that the aqueous extract had high levels of total phenols (24.037 mg Eq AG / g dried material), total flavonoids (9.493 mg Eq Q / g DM), flavanols (2,704 mg Eq R / g DM), flavones and flavonols (2,053 mg Eq Q / g DM), saponins (6,270%), tannins condensates (0,019 mg Eq C / g DM) and anthocyanins (2,625 mg / ml), relative to the methanolic extract and the ethanolic extract. In addition, the methanolic extract was found to be the most titrated in alkaloids (0.698%). Administration of the aqueous extract of root at a dosage of 500 mg/kg of body weight significantly decreased glycaemia (p < 0.05) compared to the group of rats which received distilled water at 10 ml/kg and showed a significant change in percentage of glucose reduction (p = 0.003), comparable to that of glibenclamide 10 mg / kg of body weight, the sulfonylurea hypoglycemic drug of reference. Our results show that the roots of J. curcas have an interesting hypoglycemic effect suggesting their use as extracts in hypoglycemic treatments.
The Plant Pathology Challenge 2020 data set to classify foliar disease of apples

Premise Apple orchards in the United States are under constant threat from a large number of pathogens and insects. Appropriate and timely deployment of disease management depends on early disease detection. Incorrect and delayed diagnosis can result in either excessive or inadequate use of chemicals, with increased production costs and increased environmental and health impacts. Methods and Results We have manually captured 3651 high‐quality, real‐life symptom images of multiple apple foliar diseases, with variable illumination, angles, surfaces, and noise. A subset of images, expert‐annotated to create a pilot data set for apple scab, cedar apple rust, and healthy leaves, was made available to the Kaggle community for the Plant Pathology Challenge as part of the Fine‐Grained Visual Categorization (FGVC) workshop at the 2020 Computer Vision and Pattern Recognition conference (CVPR 2020). Participants were asked to use the image data set to train a machine learning model to classify disease categories and develop an algorithm for disease severity quantification. The top three area under the ROC curve (AUC) values submitted to the private leaderboard were 0.98445, 0.98182, and 0.98089. We also trained an off‐the‐shelf convolutional neural network on this data for disease classification and achieved 97% accuracy on a held‐out test set. Discussion This data set will contribute toward development and deployment of machine learning–based automated plant disease classification algorithms to ultimately realize fast and accurate disease detection. We will continue to add images to the pilot data set for a larger, more comprehensive expert‐annotated data set for future Kaggle competitions and to explore more advanced methods for disease classification and quantification.
Effects of accelerated reading rate on memory for text

Based on the finding that increasing stimulus presentation rate can increase short-term memory (STM) span, reading rate was accelerated in a series of experiments that demonstrated substantial gains in reading accuracy and comprehension. The present investigation tested the hypothesis that comprehension gains in fast-paced reading are attributable primarily to changes in STM functioning. A series of standard STM-sensitive tasks were administered in both self-paced and fast-paced reading conditions. The magnitude and relative specificity of effects evident in fastpaced reading confirmed the hypothesis. Several alternative hypotheses were rejected
Study on the Development of Mozzarella Cheese Adding Lipase Enzyme and Its Quality Evaluation

The study was conducted for the development of mozzarella cheese by use of rennet and lipase enzyme. For the development of the mozzarella cheese, four types of treatments have been taken in which T0, as a control sample in which 2.5g of rennet enzyme used alone. Now this rennet enzyme would be replaced by lipase enzyme by 0.25 g for three sample e.g. T1(2.25g vs 0.25g), T2 (2.00g vs 0.50g), T3 (1.75g vs 0.75g). After the production of the mozzarella cheese, physio-chemical properties such as stretch ability, hardness, moisture, fat, protein, acidicity and total solid content of the mozzarella cheese were evaluated. Organoleptic properties such as flavor, tast, body and texure and colour appearances were also evaluated by sensory team for evaluation of the quality of the cheese. Microbiological analysis (SPC) was conducted for the fresh cheese and stored cheese (10 days) for evaluation of the shelf life of the cheese. After the evolution of the quality and shelf life, it was concluded that the stretch ability, fat, protein, total solid content and acidicity of produced Mozzarella cheese were increases and hardness and moisture of product decreases from sample T0 to T3. While sensory properties such as taste, appearance, and flavour of T3 was good in compression of T0 but colour of T0 is better than T3. The overall perception and acceptability by sensory members was positive. The shelf life of the developed cheese was found more than 10 days.
Chelid Turtles (Pleurodira, Chelidae) from the Miocene Camfield Beds, Northern Territory of Australia, with a Description of a New Genus and Species

The Bullock Creek Local Fauna (mid Miocene Camfield Beds, Northern Territory of Australia) contains five or possibly six species of chelid turtles (Pleurodira: Chelidae), though only one form is sufficiently represented to be named. Birlimarr gaffneyi gen. et sp. nov. is a small (carapace length: 160 mm), shortnecked species represented by a complete skeleton. A phylogenetic analysis of cranial and shell characters resolves the new genus as quite derived amongst Australian short-necked chelids, the plesiomorphic sister taxon to a clade composed of Emydura plus an Elseya dentata 'generic group'. Three other forms are diagnosed on shell material to genus or suprageneric level: a large (carapace length ca. 440 mm) shortnecked form is designated gen. aff. Emydura/Elseya sp. indet. and two long-necked forms are designated Chelodina sp. A and sp. B. Additional shell material indicates a fifth and possibly a sixth chelid species is present in the assemblage, but the lower level affinities of these forms cannot be determined. The Emydura/Elseya form is similar but not identical to previously described 'Emydura ' material from the Miocene of South Australia (Wipajiri and Namba Formations), while a previously described carapace from the Miocene of western Queensland (Carl Creek Limestone) may represent a species of Birlimarr gen. nov. The Carl Creek Limestone and Camfield Beds assemblages are similarly diverse, approximating the maximum sympatric diversity recorded in extant assemblages: palaeoenvironmental conditions at these two Miocene localities thus appear to have been optimal for chelid diversity. Many of the Camfield Beds specimens show evidence of predation by crocodiles.
Effects of staphylococcal enterotoxin B on rodent mast cells

Staphylococcal enterotoxin B (SEB) was tested in rodent mast cell cultures for the release of serotonin. Both rat RBL-2H3 mast cells and murine peritoneal cells released serotonin after SEB stimulation in culture. Release of serotonin in RBL-2H3 cells depended on the concentration of SEB; an appreciable release was seen at 50 micrograms/ml. The release of serotonin was not due to cell death. Serotonin release could be enhanced by bradykinin but not by vasoactive intestinal peptide, substance P, lipopolysaccharide from Salmonella typhimurium, the calcium ionophore A23187, acetylcholine, adenosine, 5-hydroxyeicosatetraenoic acid, indomethacin, or phorbol myristate acetate. SEB bound directly to the membrane of RBL-2H3 mast cells, and the SEB-binding site, the presumptive receptor, appeared to be a protein. The SEB receptor could not be capped under membrane-capping conditions, and serotonin release could not be enhanced by attempts to cross-link the receptor. These results suggest that mast cells may be an important cell type involved in SEB toxicosis and that release of serotonin may be enhanced by activation of the kinin-kallikrein system.
Asymmetric Adjustment Toward Optimal Capital Structure: Evidence From a Crisis∗

We employ dynamic threshold partial adjustment models to study the asymmetries in firms’ adjustments toward their target leverage. Using a sample of US firms during 2002–2012, we document a negative impact of the Global Financial Crisis on the speed of leverage adjustment. In our subperiod analysis, we find moderate evidence of cross-sectional heterogeneity in this speed, which seems more pronounced pre-crisis and provides little support for the financial constraint view. For the pre-crisis period, more constrained firms, such as those with high growth, with large investment, of small size, and with volatile earnings, adjust their capital structures more quickly than their less constrained counterparts. These firms rely heavily on external funds to offset their large financing deficits, suggesting that their higher adjustment speeds may be driven by lower adjustment costs that are shared with the transaction costs of accessing external capital markets. For the crisis period, the speed of adjustment only varies with the deviation from target leverage, with only firms having sufficiently large deviations attempting to revert to the target, albeit slowly. Overall, our results provide new evidence of both cross-sectional and time-varying asymmetries in capital structure adjustments, which is consistent with the trade-off theory. JEL Classification: G30; G32; C33
MITIGATION OF HEAT STRESS EFFECTS ON WASHINGTON NAVEL ORANGE BY USING MELATONIN, GIBBERELLIN AND SALICYLIC TREATMENTS

One Farm strategy for adaptation to high temperatures and increasing the production of ‘Washington navel’ orange on sandy soil was achieved by spray some compounds to avoid the stress. Using hormonal substances and antioxidants (melatonin “Mel”, gibberellic acid “GA3”, salicylic acid “SA”) as a foliar spray was carried out in mid-April at two concentrations (25 and 50 mg/L) to reduce the impact of heat stress on the growth, productivity and quality of ‘Washington navel’ orange trees grown in the National Research Centre farm for research and production in Al-Nubaria region, Al-Behira Governorate, Egypt. This study was carried out during two successive growing seasons (2017 and 2018). Such, the low concentration of each of GA3 and Mel show a positive effect on most of vegetative growth characteristics followed by SA at the high concentration. Also, spraying with Mel or GA3 at low concentration had high values of many elements such as N, P, K and Mg followed by SA at high concentration which had high values of many elements such as Fe, Mn and Cu. Fruit set percentage of Washington navel orange was improved using all spraying materials especially GA3 and Mel, and this resulted in improving number of fruits, fruit weight and tree yield in the two experimental seasons. So, it can be observed that the high yield which resulted from GA3 spray at low concentration was due to the high fruits number while those resulted from Mel spray at low concentration was due to the high fruit weight, this was true in the first season whatever, in the second season, high yield resulted from GA3 spray at low concentration or Mel spray at two concentrations was due to the high fruits number and weight, as high yield resulted from SA spray at low concentration was in second order comparing with unsprayed trees. Also, all spraying materials maintained good fruit quality.
SynSetExpan: An Iterative Framework for Joint Entity Set Expansion and Synonym Discovery

Entity set expansion and synonym discovery are two critical NLP tasks. Previous studies accomplish them separately, without exploring their interdependencies. In this work, we hypothesize that these two tasks are tightly coupled because two synonymous entities tend to have similar likelihoods of belonging to various semantic classes. This motivates us to design SynSetExpan, a novel framework that enables two tasks to mutually enhance each other. SynSetExpan uses a synonym discovery model to include popular entities' infrequent synonyms into the set, which boosts the set expansion recall. Meanwhile, the set expansion model, being able to determine whether an entity belongs to a semantic class, can generate pseudo training data to fine-tune the synonym discovery model towards better accuracy. To facilitate the research on studying the interplays of these two tasks, we create the first large-scale Synonym-Enhanced Set Expansion (SE2) dataset via crowdsourcing. Extensive experiments on the SE2 dataset and previous benchmarks demonstrate the effectiveness of SynSetExpan for both entity set expansion and synonym discovery tasks.
Longest k-monotone chains.

We study higher order convexity properties of random point sets in the unit square. Given $n$ uniform i.i. d random points, we derive asymptotic estimates for the maximal number of them which are in $k$-monotone position, subject to mild boundary conditions. Besides determining the order of magnitude of the expectation, we also prove strong concentration estimates. We provide a general framework that includes the previously studied cases of $k=1$ (longest increasing sequences) and $k=2$ (longest convex chains).
NanoGrav 12.5-yr data and different stochastic Gravitational wave background sources

The NANOGrav Collaboration recently observed a strong evidence for a stochastic common-spectrum process in the pulsar timing data. In this work, we evaluate the possibility to interpret this process as stochastic gravitational wave backgrounds produced from first-order phase transitions, cosmic strings, domain walls, and large amplitude curvature perturbations.
Scarification of seeds of Acacia angustissima (Mill.) Kuntze and its effect on germination

Summary Field germination of Acacia angustissima (Mill.) Kuntze, a N2-fixing tree whose bark is traditionally extracted for tannins used in the leather industry, takes up to 55 days and the percentage of germination is generally less than 20%. Abrasion, dry or wet heat, or immersing the seeds in different concentrations of sulphuric acid, chloric acid or hydrogen peroxide for different lengths of time, were tested as germination enhancers. The application of dry heat resulted in < 37% germination, less than the 44% found for untreated control seeds. The application of wet heat for ≤ 30 s and abrasion for 1200 s resulted in 69% germination. Immersing seeds in concentrated H2SO4 for 900 s resulted in 77% germination while diluting H2SO4 or longer exposure to concentrated H2SO4 decreased it. Immersing the seeds in concentrated or diluted H2O2 also increased germination, but to a lesser extent than when immersed in H2SO4. We found that the exposure of seeds of A. angustissima to concentrated H2SO4 for 900 s appeared to be the best technique to stimulate their germination; the treatment gave the largest percentage of germination in the shortest time, the technique is easy to apply and many seeds can be treated at once.
Task-Based Analog-to-Digital Converters

Obtaining digital representations of multivariate continuous-time (CT) signals is a challenge encountered in many signal processing systems. In practice, these signals are often acquired to extract some underlying information, i.e., for a specific task. Employing conventional task-agnostic analog-to-digital converters (ADCs), typically designed to minimize the mean squared error (MSE) in reconstructing the CT input signal, can be costly and energy-inefficient in such cases. In this work, we study task-based ADCs, which are designed to obtain a digital representation of a multivariate CT input process with the goal of recovering an underlying statistically related parameter vector, referred to as the task. The proposed system employs analog filtering, uniform sampling, and scalar uniform quantization of the input process before recovering the task vector using a linear digital recovery filter. We optimize the analog and digital filters and derive closed-form expressions for the achievable MSE in recovering the task vector from a set of analog signals when utilizing ADCs with a fixed sampling rate and amplitude resolution. Based on our derivation, we provide guidelines for designing practical acquisition systems subject to a constraint on the bit rate. Our analysis proves that the intuitive approaches of either recovering the task vector solely in digital or designing the analog filter to estimate the task vector are inferior to the proposed joint design. We then consider the recovery of a set of matched filter outputs under a rate budget. We numerically verify our theoretical observations and demonstrate that task-based ADCs substantially outperform analog matched filtering as well as applying the matched filter solely in the digital domain. When acquiring signals for a task under tight bit budgets, we also show that it is often preferable to sample below the Nyquist rate instead of reducing the quantization resolution.
The stellar metallicity distribution function of galaxies in the CALIFA survey

We present a novel method to retrieve the chemical structure of galaxies using integral field spectroscopy data through the stellar Metallicity Distribution Function (MDF). This is the probability distribution of observing stellar populations having a metallicity $Z$. We apply this method to a set of $550$ galaxies from the CALIFA survey. We present the behaviour of the MDF as a function of the morphology, the stellar mass and the radial distance. We use the stellar metallicity radial profiles retrieved as the first moment of the MDF, as an internal test for our method. The gradients in these radial profiles are consistent with the known trends: they are negative in massive early-type galaxies and tend to positive values in less massive late-type ones. We find that these radial profiles may not convey the complex chemical structure of some galaxy types. Overall, low mass galaxies ($\log{M_\star/\mathrm{M}_{\odot}}\leq10$) have broad MDFs ($\sigma_Z\sim1.0\,$dex), with unclear dependence on their morphology. However this result is likely affected by under-represented bins in our sample. On the other hand, massive galaxies ($\log{M_\star/\mathrm{M}_{\odot}}\geq11$) have systematically narrower MDFs ($\sigma_Z\leq0.2\,$dex). We find a clear trend whereby the MDFs at $r_k/R_e>1.5$ have large variance. This result is consistent with sparse SFHs in medium/low stellar density regions. We further find there are multi-modal MDFs in the outskirts ($\sim18\,$per cent) and the central regions ($\sim40\,$per cent) of galaxies. This behaviour is linked to a fast chemical enrichment during early stages of the SFH, along with the posterior formation of a metal-poor stellar population.
Message Passing Neural Processes

Neural Processes (NPs) are powerful and flexible models able to incorporate uncertainty when representing stochastic processes, while maintaining a linear time complexity. However, NPs produce a latent description by aggregating independent representations of context points and lack the ability to exploit relational information present in many datasets. This renders NPs ineffective in settings where the stochastic process is primarily governed by neighbourhood rules, such as cellular automata (CA), and limits performance for any task where relational information remains unused. We address this shortcoming by introducing Message Passing Neural Processes (MPNPs), the first class of NPs that explicitly makes use of relational structure within the model. Our evaluation shows that MPNPs thrive at lower sampling rates, on existing benchmarks and newly-proposed CA and Cora-Branched tasks. We further report strong generalisation over density-based CA rule-sets and significant gains in challenging arbitrary-labelling and few-shot learning setups.