Miracle-dz/UniFAR-FTUs huggingface dataset:
2 Exact linear aggregation : a nonparametric characterisation

In the tradition of Afriat (1967), Diewert (1973) and Varian (1982), we provide a revealed preference characterisation of exact linear aggregation. This guarantees that aggregate demand can be written as a function of prices and aggregate income alone, while abstracting from income-distributional aspects. We also establish nonparametric conditions for individual consumption to be representable in terms of Gorman Polar Form preferences. Our results are simple and complement those of Gorman (1953, 1961). We illustrate the practical usefulness of our results by means of an empirical application to a Spanish balanced microdata panel. Acknowledgments: Some of our results in the current paper already appeared in an earlier manuscript entitled “Aggregation without the aggravation? Nonparametric analysis of the representative consumer”, which should now be considered superseded. We are very grateful to Richard Blundell, Don Brown, Martin Browning, Erwin Diewert, Federico Echenique, Arthur Lewbel, Peter Neary, Krishna Pendakur and seminar participants at Bergen, CIREQ Montreal, DIW Berlin, Keele, Malaga, Oxford, Paris Dauphine and Tilburg for helpful comments and suggestions.
Attention Is All You Need for Chinese Word Segmentation

Taking greedy decoding algorithm as it should be, this work focuses on further strengthening the model itself for Chinese word segmentation (CWS), which results in an even more fast and more accurate CWS model. Our model consists of an attention only stacked encoder and a light enough decoder for the greedy segmentation plus two highway connections for smoother training, in which the encoder is composed of a newly proposed Transformer variant, Gaussian-masked Directional (GD) Transformer, and a biaffine attention scorer. With the effective encoder design, our model only needs to take unigram features for scoring. Our model is evaluated on SIGHAN Bakeoff benchmark datasets. The experimental results show that with the highest segmentation speed, the proposed model achieves new state-of-the-art or comparable performance against strong baselines in terms of strict closed test setting.
Composting oil palm wastes and sewage sludge for use in potting media of ornamental plants.

The use of oil palm wastes, particularly the empty fruit bunch (EFB), frond and trunk as compost are now receiving greater attention by researchers. Currently, these organic waste materials have not been fully utilized on a large scale, either agriculturally or industrially, for manufacture of useful by-products. Another organic waste that needs to be appropriately disposed of in Malaysia is the sewage sludge. Co-composting these waste materials could potentially convert these wastes into value added product. The objective of this study was to determine the best formulation using oil palm wastes and sewage sludge in producing a composted material to be used as a potting media in horticulture. Composting different oil palm wastes with sewage sludge was carried out in the glasshouse using a polystyrene box. Shredded oil palm wastes (EFB, frond and trunk) were mixed with sewage sludge in 3 different ratios (1:0, 3:1 and 4:1 ratio) and adjusted to 60% moisture content. Based on the temperature, C/N, NH4+ -N and NO3- - N + NO2- -N patterns of the oil palm wastes added with sludge during composting, the EFB, frond and trunk added with sludge composts seemed to perform similarly.However, due to the small volume of compost, the temperature did not sustain > 45oC because of dissipation of the heat. Oil palm trunk with sewage sludge at 4:1 ratio was found to be the most optimum compost as potting media for ornamental plants because of its texture suitable for potting media, not stringent or stiff, had high nutrient contents (2.05 % N, 0.640 % P, 1.39 % K, 0.705 % Ca, 0.229% Mg), pH 6.2 and low C/N ratio, 19.
Path Loss Exponent Prediction for Outdoor Millimeter Wave Channels through Deep Learning

In this paper, we propose a new algorithm for predicting the path loss exponent of outdoor millimeter-wave band channels through deep learning method. The proposed algorithm has the advantage of requiring less inference time compared to existing deterministic channel models while concretely considering the topographical characteristics. We used three-dimensional ray tracing to generate the outdoor millimeterwave band channel and path loss exponent. We trained a neural network with generated path loss exponent. To evaluate the performance of the proposed method, we analyzed the influence of the hyperparameters and environmental features, for example, building density and average distance from the transmitter.
Fiscal policy and economic growth in Kenya

The question of whether or not fiscal policy stimulates growth has dominated theoretical and empirical debate for a long time. One viewpoint believes that government involvement in economic activity is vital for growth, but an opposing view holds that government operations are inherently bureaucratic and inefficient and therefore stifles rather than promotes growth. In the empirical literature, results are equally mixed. The aim of this paper is not to resolve the raging debate but to add to the fiscal policy-growth literature by examining the case of a small open developing country, Kenya. We used time series techniques to investigate the relationship between various measures of fiscal policy on growth on annual data for the period 1964 – 2002. Categorising government expenditure into productive and unproductive and tax revenue into distortionary and non-distortionary, we found unproductive expenditure and nondistortionary tax revenue to be neutral to growth as predicted by economic theory. However, contrary to expectations, productive expenditure has strong adverse effect on growth whilst there was no evidence of distortionary effects on growth of distortionary taxes. On the other hand, government investment was found to be beneficial to growth in the long run. These results should prove useful to policy makers in Kenya in formulating expenditure and tax policies to ensure unproductive expenditures are curtailed while at the same time boosting public investment. Outline
Task Assignment Algorithms in Data Shared Mobile Edge Computing Systems

The appearance of the Mobile Edge Computing (MEC) successfully solves the bottlenecks of traditional Cloud based networks as computation ability of each mobile edge is sufficiently utilized. Since the mobile edges, including mobile devices and base stations, have certain data processing abilities, it is not necessary to offload all the computation tasks to the remote could for handling. Therefore, it is quite important to decide the optimal task assignment in a MEC system, and a series of algorithms have been proposed to solve it. However, the existing algorithms ignored the data distribution during task assignment, so that the applied ranges of these algorithms are quite limit. Considering the data sharing is very important and common in a MEC system, this paper studies the task assignment algorithm in Data Shared Mobile Edge Computing Systems, and three algorithms are proposed to deal with holistic tasks and divisible tasks, respectively. The theoretical analysis on the hardness of the problem, the correctness, complexities and ratio bounds of the algorithms are also provided. Finally, the extensive experiment results were carried out. Both of the theoretical analysis and experiment results show that our algorithms have high performance in terms of latency and energy consumption.
Yield components and reproductive, physiological, and root traits used in early selection for nitrogen use efficiency in corn

The objective of this work was to examine the possibility of using yield components and reproductive, physiological, and root traits in early selection for nitrogen use efficiency (NUE) in corn. Sixty-four inbred lines were evaluated under two nitrogen fertilization levels: ideal and low. The evaluations were performed at three phenological stages: eight fully-expanded leaves, tasseling stage, and physiological maturity. It is possible to select superior lines for NUE, but the yield components did not show differential behavior under the different nitrogen levels evaluated. Root, reproductive, and physiological traits are not promising for early selection of corn lines with high NUE. Likewise, the eight-leaves and tasseling stages were not promising for this purpose, since NUE should be estimated taking grain yield into account. However, indirect selection for NUE can be performed via number of ears or using the selection index considering number and weight of ears.
The Immunogenicity and Anti-tumor Efficacy of a Rationally Designed Neoantigen Vaccine for B16F10 Mouse Melanoma

Tumor neoantigens are ideal targets for cancer immunotherapy as they are recognized by host immune system as foreigners and can elicit tumor-specific immune responses. However, existing strategies utilizing RNA or long peptides for the neoantigen vaccines render limited immune responses since only 20–30% of neoantigens predicted in silico to bind MHC I molecules are capable of eliciting immune responses with the majority of responding T cells are CD4+. Therefore, it warrants further exploration to enhance neoantigen-specific CD8+ T cells responses. Since neoantigens are naturally weak antigens, we asked whether foreign T help epitopes could enhance their immunogenicity. In present study, we chose 4 weak B16F10 neoantigens as vaccine targets, and fused them to the transmembrane domain of diphtheria toxin, namely DTT-neoAg. Strikingly, the vaccine elicited anti-tumor CD8+ T cells responses and enhanced tumor infiltration of both T cells and NK cells. Impressively, DTT-neoAg vaccine significantly deterred tumor growth with the inhibition rate reached 88% in the preventive model and 100% in the therapeutic model at low dose of tumor challenge. Furthermore, after second challenge with higher dose of tumor cells, 33.3% of the immunized mice remained tumor-free for 6 months in the therapeutic model. Because DTT is a non-toxic domain of diphtheria toxin, it may be not of great concern in terms of safety as a Th epitope provider. Thus, the fusion strategy employed by this study may become a feasible and powerful approach for development of personalized cancer vaccines.
A seed chalcid ( Eurytoma squamosa Bugbee) parasitizes buckbrush ( Ceanothus fendleri Gray) seeds in a ponderosa pine forest of Arizona

Predispersal seed parasitism rates were quantified for buckbrush ( Ceanothus fendleri Gray) in 3 Arizona ponderosa pine ( Pinus ponderosa Laws.) forest units that had been thinned for ecological restoration objectives. The chalcidoid wasp Eurytoma squamosa Bugbee (Eurytomidae) was responsible for 35% of total seed loss in a single year. These findings represent an expansion of the known range and host list for E. squamosa and increase our understanding of factors that may constrain regeneration of C. fendleri in Southwest ponderosa pine forests.
Plant Leaves Classification: A Few-Shot Learning Method Based on Siamese Network

In recent years, the method of plant leaf classification by deep learning has gradually become mature. However, training a leaf classifier based on deep learning requires a large number of samples for supervised training. In this paper, a few-shot learning method based on the Siamese network framework is proposed to solve a leaf classification problem with a small sample size. First, the features of two different images are extracted by a parallel two-way convolutional neural network with weight sharing. Then, the network uses a loss function to learn the metric space, in which similar leaf samples are close to each other and different leaf samples are far away from each other. In addition, a spatial structure optimizer (SSO) method for constructing the metric space is proposed, which will help to improve the accuracy of leaf classification. Finally, a k-nearest neighbor (kNN) classifier is used to classify leaves in the learned metric space. The average classification accuracy is used as a performance measure. The open access Flavia, Swedish and Leafsnap datasets are used to evaluate the performance of the method. The experimental results show that the proposed method can achieve a high classification accuracy with a small size of supervised samples.
PODIUM: Probabilistic Datalog Analysis via Contribution Maximization

The use of probabilistic datalog programs has been advocated for applications that involve recursive computation and uncertainty. While using such programs allows for a flexible knowledge derivation, it makes the analysis of query results a challenging task. Particularly, given a set O of output tuples and a number k, one would like to understand which k-size subset of the input tuples has affected the most the derivation of O. This is useful for multiple tasks, such as identifying critical sources of errors and understanding surprising results. To this end, we formalize the Contribution Maximization problem and present an efficient algorithm to solve it. Our algorithm injects a refined variant of the classic Magic Sets technique, integrated with a sampling method, into top-performing algorithms for the well-studied Influence Maximization problem. We propose to demonstrate our solution in a system called PODIUM. We will demonstrate the usefulness of PODIUM using real-life data and programs, and illustrate the effectiveness of our algorithm.
Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks

Predicting a user's preference in a short anonymous interaction session instead of long-term history is a challenging problem in the real-life session-based recommendation, e.g., e-commerce and media stream. Recent research of the session-based recommender system mainly focuses on sequential patterns by utilizing the attention mechanism, which is straightforward for the session's natural sequence sorted by time. However, the user's preference is much more complicated than a solely consecutive time pattern in the transition of item choices. In this paper, therefore, we study the item transition pattern by constructing a session graph and propose a novel model which collaboratively considers the sequence order and the latent order in the session graph for a session-based recommender system. We formulate the next item recommendation within the session as a graph classification problem. Specifically, we propose a weighted attention graph layer and a Readout function to learn embeddings of items and sessions for the next item recommendation. Extensive experiments have been conducted on two benchmark E-commerce datasets, Yoochoose and Diginetica, and the experimental results show that our model outperforms other state-of-the-art methods.
Internal Rationality, Learning and Imperfect Information

We construct, estimate and explore the monetary policy consequences of a New Keynesian (NK) behavioural model with bounded-rationality and heterogeneous agents. We radically depart from most existing models of this genre in our treatment of bounded rationality and learning. Instead of the usual Euler learning approach, we assume that agents are internally rational (IR) given their beliefs of aggregate states and prices. The model is inhabited by fully rational (RE) and IR agents where the latter use simple heuristic rules to forecast aggregate variables exogenous to their micro-environment. We find that IR results in an NK model with more persistence and a smaller policy space for rule parameters that induce stability and determinacy. In the most general form of the model, agents learn from their forecasting errors by observing and comparing them with those under RE making the composition of the two types endogenous. In a Bayesian estimation with fixed proportions of RE and IR agents and a general heuristic forecasting rule we find that a pure IR model fits the data better than the pure RE case. However, the latter with imperfect rather than the standard perfect information assumption outperforms IR (easily) and RE-IR composites (slightly), but second moment comparisons suggest that the RE-IR composite can match data better. Our findings suggest that Kalman-filtering learning with RE can match bounded-rationality in matching persistence seen in the data.
The Effect of Gut Microbiome Composition on Human Immune Responses: An Exploration of Interference by Helminth Infections

Background: Soil-transmitted helminths have been shown to have the immune regulatory capacity, which they use to enhance their long term survival within their host. As these parasites reside in the gastrointestinal tract, they might modulate the immune system through altering the gut bacterial composition. Although the relationships between helminth infections or the microbiome with the immune system have been studied separately, their combined interactions are largely unknown. In this study we aim to analyze the relationship between bacterial communities with cytokine response in the presence or absence of helminth infections. Results: For 66 subjects from a randomized placebo-controlled trial, stool and blood samples were available at both baseline and 21 months after starting three-monthly albendazole treatment. The stool samples were used to identify the helminth infection status and fecal microbiota composition, while whole blood samples were cultured to obtain cytokine responses to innate and adaptive stimuli. When subjects were free of helminth infection (helminth-negative), increasing proportions of Bacteroidetes was associated with lower levels of IL-10 response to LPS {estimate [95% confidence interval (CI)] −1.96 (−3.05, −0.87)}. This association was significantly diminished when subjects were helminth-infected (helminth positive) (p-value for the difference between helminth-negative versus helminth-positive was 0.002). Higher diversity was associated with greater IFN-γ responses to PHA in helminth-negative (0.95 (0.15, 1.75); versus helminth-positive [−0.07 (−0.88, 0.73), p-value = 0.056] subjects. Albendazole treatment showed no direct effect in the association between bacterial proportion and cytokine responses, although the Bacteroidetes’ effect on IL-10 responses to LPS tended downward in the albendazole-treated group [−1.74 (−4.08, 0.59)] versus placebo [−0.11 (−0.84, 0.62); p-value = 0.193]. Conclusion: We observed differences in the relationship between gut microbiome composition and immune responses, when comparing individuals infected or uninfected with geohelminths. Although these findings are part of a preliminary exploration, the data support the hypothesis that intestinal helminths may modulate immune responses, in unison with the gut microbiota. Trial Registration: ISRCTN, ISRCTN83830814. Registered 27 February 2008 — Retrospectively registered, http://www.isrctn. com/ISRCTN83830814.
Insight into mechanism underlying the medicinal use of Cydonia oblonga in gut and airways disorders.

This studywasintendedto providethepharmacological rationalization for themedicinaluse ofCydonia oblongaingut and airwaysdiseases.Results showed thatthecrude extract of Cydonia oblongaseeds (Co.Cr)producedatropine- sensitivespasmodiceffectsinisolatedileum of guinea -pigandrabbit jejunumpreparations.In rabbit jejunum, Co.Cr alsoshowed relaxantactivityatslightlyhigher concentrations (0.110 mg/mL). W hen analyzed onrabbit jejunum precontracted withK + (80 mM),theplant extract(0.003-10 mg/mL) produced relaxation . A rightwardshifting of Ca ++ dose-response curvesalong withdecline inthe maximum responsewas observed afterpretreatmentwithCo.Cr(0.003- 0.01 mg/mL),which wassimilar tothe effect of verapamil.The plant extract (0.01-10mg/mL )relaxedCCh (1 µM) and K + (80mM)-induced contractionsof isolated rabbit tracheal preparations ,similar tothe effect produced by verapamil. Data showthatCydonia oblongapossessesspasmogeniceffectmediatedthroughcholinergic pathway , whileCa ++ antagonist like mechanism is found responsible for itsspasmolyticactivity, thus provides justification for the medicinal ++ antagonist; Gut; Trachea.
Improved Bounds for Track Numbers of Planar Graphs

A track layout of a graph consists of a vertex coloring and a total order of each color class, such that no two edges cross between any two color classes. The track number of a graph is the minimum number of colors required by a track layout of the graph. This paper improves lower and upper bounds on the track number of several families of planar graphs. We prove that every planar graph has track number at most $225$ and every planar $3$-tree has track number at most $25$. Then we show that there exist outerplanar graphs whose track number is $5$, which leads to the best known lower bound of $8$ for planar graphs. Finally, we investigate leveled planar graphs and tighten bounds on the track number of weakly leveled graphs, Halin graphs, and X-trees.
Ventricular Arrhythmias in First Acute Myocardial Infarction: Epidemiology, Mechanisms, and Interventions in Large Animal Models

Ventricular arrhythmia and subsequent sudden cardiac death (SCD) due to acute myocardial infarction (AMI) is one of the most frequent causes of death in humans. Lethal ventricular arrhythmias like ventricular fibrillation (VF) prior to hospitalization have been reported to occur in more than 10% of all AMI cases and survival in these patients is poor. Identification of risk factors and mechanisms for VF following AMI as well as implementing new risk stratification models and therapeutic approaches is therefore an important step to reduce mortality in people with high cardiovascular risk. Studying spontaneous VF following AMI in humans is challenging as it often occurs unexpectedly in a low risk subgroup. Large animal models of AMI can help to bridge this knowledge gap and are utilized to investigate occurrence of arrhythmias, involved mechanisms and therapeutic options. Comparable anatomy and physiology allow for this translational approach. Through experimental focus, using state-of-the-art technologies, including refined electrical mapping equipment and novel pharmacological investigations, valuable insights into arrhythmia mechanisms and possible interventions for arrhythmia-induced SCD during the early phase of AMI are now beginning to emerge. This review describes large experimental animal models of AMI with focus on first AMI-associated ventricular arrhythmias. In this context, epidemiology of first AMI, arrhythmogenic mechanisms and various potential therapeutic pharmacological targets will be discussed.
of Doxorubicin-induced Cardiotoxicity Comparison of Cardiac Troponin I and T , Including the Evaluation of an Ultrasensitive Assay , as Indicators

Cardiac troponin (cTn) has been utilized to assess acute myocardial injury, but the cTn response in active/ongoing chronic injury is not well documented. The purpose of this study was to characterize the cardiac troponin I (cTnI), cardiac troponin T (cTnT), high-sensitivity cTnI, hematology, and clinical chemistry responses in rats treated with doxorubicin. Rats treated with 1, 2, or 3 mg/kg/week (wk) of doxorubicin for 2, 4, or 6 wks were sacrificed after 0, 2, or 4 wks of recovery and compared to untreated controls and animals treated with doxorubicin/dexrazoxane (50 mg/kg/wk) or etoposide (1 and 3 mg/kg/wk). The incidence and mean magnitude of cTn response increased with increasing dose and/or duration of doxorubicin treatment. Conversely, dexrazoxane/doxorubicin was partially protective for cardiotoxicity, and minimal cardiotoxicity occurred with etoposide treatment. Both cTnI and cTnT effectively identified doxorubicin-induced injury as indicated by vacuolation of cardiomyocytes of the atria/ventricles. The association between the cTn responses and histological changes was greater at the higher total exposures, but the magnitude of cTn response did not match closely with histologic grade. The high-sensitivity cTnI assay was also effective in identifying cardiac injury. Alterations occurred in the hematology and clinical chemistry parameters and reflected both dose and duration of doxorubicin treatment.