Miracle-dz/UniFAR-FTUs huggingface dataset:
The Bramson correction for Fisher--KPP equations with nonlocal diffusion

We establish the logarithmic Bramson correction to the position of solutions to the Fisher--KPP equation with nonlocal diffusion. Solutions with step-like initial data typically resemble a front at position $c_{*} t - \frac{3}{2 \lambda_{*}} \log t + \mathcal{O}(1)$ for explicit constants $c_{*}$ and $\lambda_{*}$. However, certain singular diffusions exhibit more exotic behavior.
Improving the Performance of Deep Quantum Optimization Algorithms with Continuous Gate Sets

Variational quantum algorithms are believed to be promising for solving computationally hard problems and are often comprised of repeated layers of quantum gates. An example thereof is the quantum approximate optimization algorithm (QAOA), an approach to solve combinatorial optimization problems on noisy intermediate-scale quantum (NISQ) systems. Gaining computational power from QAOA critically relies on the mitigation of errors during the execution of the algorithm, which for coherence-limited operations is achievable by reducing the gate count. Here, we demonstrate an improvement of up to a factor of 3 in algorithmic performance as measured by the success probability, by implementing a continuous hardware-efficient gate set using superconducting quantum circuits. This gate set allows us to perform the phase separation step in QAOA with a single physical gate for each pair of qubits instead of decomposing it into two C$Z$-gates and single-qubit gates. With this reduced number of physical gates, which scales with the number of layers employed in the algorithm, we experimentally investigate the circuit-depth-dependent performance of QAOA applied to exact-cover problem instances mapped onto three and seven qubits, using up to a total of 399 operations and up to 9 layers. Our results demonstrate that the use of continuous gate sets may be a key component in extending the impact of near-term quantum computers.
UvA-DARE (Digital Academic Repository) Rethinking Item Importance in Session-based Recommendation

Session-based recommendation aims to predict a user’s actions at the next timestamp based on anonymous sessions. Previous work mainly focuses on the transition relationship between items that the user interacted with during an ongoing session. They generally fail to pay enough attention to the importance of the items involved in these interactions in terms of their relevance to user’s main in-tent. In this paper, we propose a Session-based Recommendation approach with an Importance Extraction Module, i.e., SR-IEM, that considers both a user’s long-term and recent behavior in an ongoing session. We employ a modified self-attention mechanism to estimate item importance in a session, which is then used to predict user’s long-term preference. Item recommendations are produced by combining the user’s long-term preference and their current interest as conveyed by the last item they interacted with. Comprehensive experiments are conducted on two publicly available benchmark datasets. The proposed SR-IEM model outperforms start-of-the-art baselines in terms of Recall and MRR for the task of session-based recommendation. In addition, compared to state-of-the-art models, SR-IEM has a reduced computational complexity.
Non-interferometric and non-iterative complex wave-field reconstruction based on Kramers-Kronig relations

We reported a novel non-interferometric and non-iterative computational imaging method, synthetic aperture imaging based on Kramers-Kronig relations (KKSAI), to reconstruct complex wave-field. By collecting images through a modified microscope system with pupil modulation capability, we show that the phase and amplitude profile of the sample at pupil limited resolution can be extracted from as few as two intensity images by exploiting Kramers-Kronig relations. KKSAI reconstruction is non-iterative, free of parameter tuning and applicable to a wider range of samples. Simulation and experiment results have proved that it has much lower computational burden and achieves the best reconstruction quality when compared with two existing phase imaging methods.
The Structured Weighted Violations MIRA

We present the Structured Weighted Violation MIRA (SWVM), a new structured prediction algorithm that is based on an hybridization between MIRA (Crammer and Singer, 2003) and the structured weighted violations perceptron (SWVP) (Dror and Reichart, 2016). We demonstrate that the concepts developed in (Dror and Reichart, 2016) combined with a powerful structured prediction algorithm can improve performance on sequence labeling tasks. In experiments with syntactic chunking and named entity recognition (NER), the new algorithm substantially outperforms the original MIRA as well as the original structured perceptron and SWVP. Our code is available at this https URL.
A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

Deep learning has recently been used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and it has achieved significant performance improvements over traditional machine learning in computer-aided diagnosis of brain disorders. This paper reviews the applications of deep learning methods for neuroimaging-based brain disorder analysis. We first provide a comprehensive overview of deep learning techniques and popular network architectures by introducing various types of deep neural networks and recent developments. We then review deep learning methods for computer-aided analysis of four typical brain disorders, including Alzheimer's disease, Parkinson's disease, Autism spectrum disorder, and Schizophrenia, where the first two diseases are neurodegenerative disorders and the last two are neurodevelopmental and psychiatric disorders, respectively. More importantly, we discuss the limitations of existing studies and present possible future directions.
Playing odds and evens with finite automata

This paper is concerned with asymptotic behaviour of a repeated game of "odds and evens", with strategies of both players represented by finite automata. It is proved that, for every $n$, there is an automaton with $2^n \cdot \mathrm{poly}(n)$ states which defeats every $n$-state automaton, in the sense that it wins all rounds except for finitely many. Moreover, every such automaton has at least $2^n \cdot (1 - o(1))$ states, meaning that the upper bound is tight up to polynomial factors. This is a significant improvement over a classic result of Ben-Porath in the special case of "odds and evens". Moreover, I conjecture that the approach can be generalised to arbitrary zero-sum games.
Winter philopatry in migratory waterfowl

Philopatry in migratory species can apply to any location used during the annual cycle. The degree of philopatry influences the genetic structure of populations, but only at the stage of the annual cycle when pair formation and gene exchange occur. Because pair formation in birds typically occurs during the breeding season, most studies have fo- cused on breeding-site philopatry. Waterfowl (Anseriformes) are an important exception to this pattern because pair formation often occurs during the winter months. Yet, surprisingly few studies have examined winter philopatry in waterfowl. To serve as an impetus for future research, we summarize published information on winter philopatry in waterfowl and ex- amine these patterns in light of current hypotheses proposed to explain philopatric behavior. Our analyses indicate that geese, swans, and sea ducks show high levels of winter philopatry, with homing rates varying between 49 and 98% to small study areas. In contrast, return rates (0 to 20%) and homing rates (35 to 85%) to large study areas probably are comparatively lower for dabbling ducks and pochards. Unfortunately, detailed comparisons among groups are hindered by variation in the scale at which philopatric behavior is evaluated (ranging from <1 km2 to 105 kM2), and by confounding of return rates with homing rates. Future studies of winter philopatry would benefit by the adoption of a more standardized meth- odology. Many of the hypotheses proposed to explain breeding philopatry apply equally well to winter philopatry. In particular, both genetic and ecological mechanisms may play a role in the evolution of philopatry to the wintering ground. Additional field studies are needed to test these hypotheses, and we suggest future directions for a more detailed ex- amination of this neglected area of research. Received 9 September 1996, accepted 28 April 1998.
Sensitivity to high salinity in tetraploid citrus seedlings increases with water availability and correlates with expression of candidate genes

We investigated tolerance to high salinity in well-irrigated diploid and tetraploid citrus. Comparisons were made between two diploids (2×) of trifoliate orange (Poncirus trifoliata (L.) Raf. ) and willow leaf mandarin (Citrus deliciosa Ten), their respective doubled diploids (4×) and the allotetraploid (FLHORAG1) obtained from the protoplast fusion of trifoliate orange and Willow leaf mandarin. Salinity stress was applied by progressively increasing the concentration of NaCl from 50 mM to 400 mM for 8 weeks. Two-year-old plants were watered daily. Maximum quantum yield of PSII, and leaf and root chloride and sodium content were monitored. We previously reported that under moderate saline stress, citrus 4× genotypes were more tolerant that the 2×, but under these experimental conditions, 4× seedlings were certainly more sensitive to salt stress than 2×, as they accumulated more toxic ions and were more affected than 2×. Chloride accumulation in 4× leaves was greater and the maximum quantum yield of PSII was more reduced in 4× than in 2×. The expression of several candidate genes involved in signal transduction, sodium and chloride transport, osmotic adjustment, regulation of the stomata opening and detoxification processes were also investigated by quantitative real-time reverse transcription-PCR. A high correlation was observed between phenotype of sensitivity to stress and gene expression changes.
Minimum degree conditions for tight Hamilton cycles

We develop a new framework to study minimum d$d$ ‐degree conditions in k$k$ ‐uniform hypergraphs, which guarantee the existence of a tight Hamilton cycle. Our main theoretical result deals with the typical absorption, path cover and connecting arguments for all k$k$ and d$d$ at once, and thus sheds light on the underlying structural problems. Building on this, we show that one can study minimum d$d$ ‐degree conditions of k$k$ ‐uniform tight Hamilton cycles by focusing on the inner structure of the neighbourhoods. This reduces the matter to an Erdös–Gallai‐type question for (k−d)$(k-d)$ ‐uniform hypergraphs, which is of independent interest. Once this framework is established, we can easily derive two new bounds. Firstly, we extend a classic result of Rödl, Ruciński and Szemerédi for d=k−1$d=k-1$ by determining asymptotically best possible degree conditions for d=k−2$d = k-2$ and all k⩾3$k \geqslant 3$ . This was proved independently by Polcyn, Reiher, Rödl and Schülke. Secondly, we provide a general upper bound of 1−1/(2(k−d))$1-1/(2(k-d))$ for the tight Hamilton cycle d$d$ ‐degree threshold in k$k$ ‐uniform hypergraphs, thus narrowing the gap to the lower bound of 1−1/k−d$1-1/\sqrt {k-d}$ due to Han and Zhao.
Building a Manga Dataset “Manga109” With Annotations for Multimedia Applications

Manga, or comics, which are a type of multimodal artwork, have been left behind in the recent trend of deep learning applications because of the lack of a proper dataset. Hence, we built Manga109, a dataset consisting of a variety of 109 Japanese comic books (94 authors and 21 142 pages) and made it publicly available by obtaining author permissions for academic use. We carefully annotated the frames, speech texts, character faces, and character bodies; the total number of annotations exceeds 500 k. This dataset provides numerous manga images and annotations, which will be beneficial for use in machine learning algorithms and their evaluation. In addition to academic use, we obtained further permission for a subset of the dataset for industrial use. In this article, we describe the details of the dataset and present a few examples of multimedia processing applications (detection, retrieval, and generation) that apply existing deep learning methods and are made possible by the dataset.
Interpretable random forest models through forward variable selection

Random forest is a popular prediction approach for handling high dimensional covariates. However, it often becomes infeasible to interpret the obtained high dimensional and non-parametric model. Aiming for obtaining an interpretable predictive model, we develop a forward variable selection method using the continuous ranked probability score (CRPS) as the loss function. Our stepwise procedure leads to a smallest set of variables that optimizes the CRPS risk by performing at each step a hypothesis test on a significant decrease in CRPS risk. We provide mathematical motivation for our method by proving that in population sense the method attains the optimal set. Additionally, we show that the test is consistent provided that the random forest estimator of a quantile function is consistent. In a simulation study, we compare the performance of our method with an existing variable selection method, for different sample sizes and different correlation strength of covariates. Our method is observed to have a much lower false positive rate. We also demonstrate an application of our method to statistical post-processing of daily maximum temperature forecasts in the Netherlands. Our method selects about 10% covariates while retaining the same predictive power.
Delightful Companions: Supporting Well-Being Through Design Delight

This paper presents three design products referred to as delightful companions that are intended to help people engage in well-being practices. It also introduces the approach utilized to guide the design decisions during their creation. Design delight is the name of this approach, which comprises six experiential qualities that are regarded as antecedents of delight. The objective of this paper is to introduce the approach and the companions and state the two paths that have defined the future steps of this research.
Response of an aphid parasitoid, Aphelinus asychis to its host, plant, host‐plant complex, and to malathion

Aphelinus asychis (Walker) can be valuable as a biocontrol agent of the aphid, Schizaphis graminum (Rondani), a major pest on grain crops in the United States. This study reports the wind tunnel, and olfactometric responses of this parasitoid to the host (aphid), plant (sorghum), and host‐plant complex (aphid‐infested sorghum). In addition, the parasitoids' response to malathion‐treated plants is also presented. The goal of the present study was to test the hypothesis that volatiles associated with the host attract natural enemies, as reported in cases of many hymenopterous parasitoids, and also to learn about the effects of insecticides on these parasitoids. In wind tunnel studies, these parasitoids moved upwind in the direction of the host‐infested plant. There was no direct flight observed, however, these parasitoids were observed to hop and jump, and sometimes walk to their host. In the olfactometer experiments, we found that A. asychis is attracted to host‐plant complex. The parasitoids' response to malathion in the olfactometer suggested that a malathion formulation when applied to plants can lure these beneficials, thus providing new insight into the ongoing task of integrating chemical and biological control of insect pests.
Induction of IκBα Expression as a Mechanism Contributing to the Anti-inflammatory Activities of Peroxisome Proliferator-activated Receptor-α Activators*

Chronic inflammation is a hallmark of degenerative diseases such as atherosclerosis. Peroxisome proliferator-activated receptors (PPARs) are transcription factors belonging to the nuclear receptor superfamily, which are expressed in the cells of the atherosclerosic lesion. PPARα ligands have been reported to exert anti-inflammatory activities in different cell types by antagonizing the transcriptional activity of NF-κB. In the present study, the influence of PPARα activators on the NF-κB signaling pathway was investigated. Our results show that fibrates, synthetic PPARα activators, induced the expression of the inhibitory protein IκBα in human aortic smooth muscle cells as well as in primary human hepatocytes, whereas neither IκB-kinase activity nor the degradation rate of IκBα were affected. Using PPARα-null mice, we demonstrated that fibrates induced IκBα in liver in vivo and that this action required PPARα. Furthermore, fibrate treatment induced IκBα protein expression in the cytoplasm and also enhanced IL-1β-induced accumulation of IκBα protein in the nucleus. These actions of fibrates on IκBα expression were accompanied by a decrease in NF-κB DNA binding activity as demonstrated by electrophoretic mobility shift assays. Taken together, these data provide an additional molecular mechanism for the anti-inflammatory activity of PPARα agonists and reinforce their potential use in the treatment of inflammatory diseases.
The Genus Callinectes

The genus CaUinectes was formed by Stimpsou in 1860 ^ for the reception of the species of Portniiiche haviug a narrow or ±-shaped abdomen in the male, and the merus of the outer maxillipeds short, sharply prominent, and curved outward at its antero-external angle. In this genus he places "the common American Lupa diacanfha^^ (Latreille), and for want of sufficient material is unable to find constant differences between the northern and southern varieties of this species, or even to separate Pacific Coast specimens, regarding as doubtfully distinct L. beJlicosa, which he had recently described from Guaymas. In 1S63 Lieut. Albert Ordway ^ j)ublished comparative descriptions of nine different species of CaUinectes. ^ Say's name hastatus was given to the common sjiecies of eastern North America, the name diacanthus was restricted to a Brazilian form described by Dana in 1852, and six new species were added. Mr. Ordwaj^ claimed that there were wellmarked characters separating the species, the variations in the abdomi-
Probing Linguistic Systematicity

Recently, there has been much interest in the question of whether deep natural language understanding (NLU) models exhibit systematicity, generalizing such that units like words make consistent contributions to the meaning of the sentences in which they appear. There is accumulating evidence that neural models do not learn systematically. We examine the notion of systematicity from a linguistic perspective, defining a set of probing tasks and a set of metrics to measure systematic behaviour. We also identify ways in which network architectures can generalize non-systematically, and discuss why such forms of generalization may be unsatisfying. As a case study, we perform a series of experiments in the setting of natural language inference (NLI). We provide evidence that current state-of-the-art NLU systems do not generalize systematically, despite overall high performance.
Salt-induced protein synthesis in tomato roots: the role of ABA

The role played by abscisic acid (ABA) in regulating salt-induced protein synthesis was investigated in roots of tomato (Lycopersicon esculentum Mill, cv. Ailsa Craig). Roots of 9-d-old Ailsa Craig (AC) seedlings and the near-isogenic ABA-deficient mutant, flacca (flc), were exposed to salt which elicited the appearance of novel polypeptides and both repressed and enhanced the synthesis of others. The polypeptide profiles of salt-treated AC and flc roots were similar suggesting that the synthesis of most novel polypeptides in salt-treated roots is not dependent on an elevated level of endogenous ABA. Exogenous ABA and a combined ABA/salt treatment were applied to the roots of AC and flc. Exogenous ABA, in the absence of salt, induced the accumulation of several polypeptides that were unique to this treatment as well as a subset of those synthesized in salt-treated roots. Interestingly, in roots exposed to the combined ABA/salt treatment, only those polypeptides that accumulated in both ABA or salt-treated roots were synthesized. Endogenous ABA levels increased 2-fold in salt-treated AC roots and 14-fold in salt-treated flc roots. Although the absolute level of ABA was lower in salt-treated flc than in AC, this demonstrates that flc has the capacity to accumulate some ABA in its roots following a salt treatment. Since it is possible that this level of ABA was sufficient to induce the changes in polypeptide synthesis observed in salt-treated roots of flc, the salt-induced accumulation of endogenous ABA was prevented by treating AC roots with fluridone. In these roots, the set of salt-induced polypeptides was similar to that observed in salt-treated roots indicating that an elevated level of endogenous ABA may not play a major role in regulating the accumulation of most salt-induced proteins in tomato roots.
Informativity for data-driven model reduction through interpolation

A method for data-driven interpolatory model reduction is presented in this extended abstract. This framework enables the computation of the transfer function values at given interpolation points based on time-domain input-output data only, without explicitly identifying the high-order system. Instead, by characterizing the set of all systems explaining the data, necessary and sufficient conditions are given under which all systems in this set share the same transfer function value at a given interpolation point. After following this so-called data informativity perspective, reduced-order models can be obtained by classical interpolation techniques. An example of an electrical circuit illustrates this framework.
CYCLOTELLA ALCHICHICANA SP. NOV. FROM A SALINE MEXICAN LAKE

Cyclotella alchichicana sp. nov. is described from Lake Alchichica, Puebla, Mexico, a saline and alkaline crater-lake in Central Mexico. The new species is similar to C. quillensis from which it distinguished by the arrangement of the marginal fultoportulae and by the external opening of the rimoportula that is elongated slit-like and lacking the external projection on the mantle. Some habitat and ecological data where C. alchichicana has been observed are provided.
Hierarchical Attention Transformer Architecture For Syntactic Spell Correction

The attention mechanisms are playing a boosting role in advancements in sequence-to-sequence problems. Transformer architecture achieved new state of the art results in machine translation, and it's variants are since being introduced in several other sequence-to-sequence problems. Problems which involve a shared vocabulary, can benefit from the similar semantic and syntactic structure in the source and target sentences. With the motivation of building a reliable and fast post-processing textual module to assist all the text-related use cases in mobile phones, we take on the popular spell correction problem. In this paper, we propose multi encoder-single decoder variation of conventional transformer. Outputs from the three encoders with character level 1-gram, 2-grams and 3-grams inputs are attended in hierarchical fashion in the decoder. The context vectors from the encoders clubbed with self-attention amplify the n-gram properties at the character level and helps in accurate decoding. We demonstrate our model on spell correction dataset from Samsung Research, and report significant improvement of 0.11\%, 0.32\% and 0.69\% in character (CER), word (WER) and sentence (SER) error rates from existing state-of-the-art machine-translation architectures. Our architecture is also trains ~7.8 times faster, and is only about 1/3 in size from the next most accurate model.