Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Interactive Collaborative Filtering

In this paper, we study collaborative filtering in an interactive setting, in which the recommender agents iterate between making recommendations and updating the user profile based on the interactive feedback. The most challenging problem in this scenario is how to suggest items when the user profile has not been well established, \ie recommend for cold-start users or warm-start users with taste drifting. Existing approaches either rely on overly pessimistic linear exploration strategy or adopt meta-learning based algorithms in a full exploitation way. In this work, to quickly catch up with the user's interests, we proposed to represent the exploration policy with a neural network and directly learn it from the feedback data. Specifically, the exploration policy is encoded in the weights of multi-channel stacked self-attention neural networks and trained with efficient Q-learning by maximizing users' overall satisfaction in the recommender systems. The key insight is that the satisfied recommendations triggered by the exploration recommendation can be viewed as the exploration bonus (delayed reward) for its contribution on improving the quality of the user profile. Therefore, the proposed exploration policy, to balance between learning the user profile and making accurate recommendations, can be directly optimized by maximizing users' long-term satisfaction with reinforcement learning. Extensive experiments and analysis conducted on three benchmark collaborative filtering datasets have demonstrated the advantage of our method over state-of-the-art methods.
2 Gene expression in mouse models for multiple sclerosis, Alzheimer's disease and stroke

article i nfo 10 11 Available online xxxx 12 15 Keywords: 16 cDNA microarrays 17 Brain inflammation 18 Cerebral stroke 19 Alzheimer's disease 20 Gene expression profiling 21 The brain responds to injury and infection by activating innate defense and tissue repair mechanisms. Working 22 upon the hypothesis that the brain defense response involves common genes and pathways across diverse pa- 23 thologies, we analysed globalgene expression inbrainfrommousemodels representing threemajor centralner- 24 vous system disorders, cerebral stroke, multiple sclerosis and Alzheimer's disease compared to normal brain
Phylogeography of two sympatric giant flying squirrel subspecies, Petaurista alborufus lena and P. philippensis grandis (Rodentia: Sciuridae), in Taiwan

To test the association between Pleistocene forest dynamics relative to elevation and the population dynamics of arboreal small mammals, we examined the phylogeographical predictions for the genetic structure of the red and white giant flying squirrel (Petaurista alborufus lena) and the Indian giant flying squirrel (P. philippensis grandis) using complete mitochondrial control region sequences. Both giant flying squirrels are endemic subspecies to Taiwan and are sympatric in much of their range. In the phylogenetic analyses, we included 35 specimens of P. alborufus lena collected from 20 localities and five specimens with unknown sampling localities. Also, we phylogenetically examined 32 specimens of P. philippensis grandis from 18 localities and three specimens with unknown sampling localities. We identified 36 haplotypes of P. alborufus lena and 33 haplotypes of P. philippensis grandis. Although we did not recognize major phylogroups, we found several minor phylogroups in both subspecies, suggesting similar evolutionary histories. Phylogeographical and demographic tests showed distributions of these two subspecies expanded into coniferous and mixed forests that developed during glaciation in Taiwan's lowlands and middle lands. This suggests that these two Petaurista subspecies shifted elevation from mountainous areas to lowlands during glaciation. © 2011 The Linnean Society of London, Biological Journal of the Linnean Society, 2011, 102, 404–419.
Morphological and molecular characterization, enzyme production and pathogenesis of Fusarium temperatum on corn in Mexico

Abstract A Fusarium sp. from a corn field in Chapingo, Estado de Mexico, Mexico was identified and characterized by morphological and molecular approaches. Colony growth on Czapek's medium and potato dextrose agar and pigment formation was assessed. Microscopically, the isolate displayed typical hyaline septate mycelium with long-beaked macroconidia with one to six septa, with a curved apical cell and a foot-like basal cell. Molecular characterization was performed by PCR of the ITS1-5.8S rDNA-ITS2 region and a portion of the elongation factor 1 alpha (EF-1α) followed by sequencing. The isolate was identified as Fusarium temperatum, an important corn pathogen in Europe, Asia and South America. A phylogenetic analysis employing the EF-1α sequence showed the isolate grouped with several species of the Gibberella fujikuroi species complex. Additionally, the isolate produced pectinase, xylanase, cellulase and lipase in culture and at 15 days post-inoculation, was able to infect the roots of corn. To our knowledge, this is the first confirmed identification and characterization of a phytopathogenic F. temperatum isolate from Mexico.
Seasonal Occurrence of Life Stages of Grasshoppers (Orthoptera: Acridoidea) in the Southern Pampas, Argentina

The seasonal occurrence of life stages of grasshopper species of the Southern Pampas was studied through sampling of representative habitats for 5 seasons (Nov. 2005 through Jan. 2010) in Laprida County, Buenos Aires Province. Twenty-two species were collected, 11 of which were very abundant. Ten species had 1 generation per year (univoltine), of which 9 (Aleuas lineatus, Borellia bruneri, B. pallida, Covasacris pallidinota, Dichroplus conspersus, D. elongatus, D. maculipennis, D. pratensis, and Scotussa lemniscata) spent the winter in the egg stage, with younger nymphs (instars I, II, and III) occurring from mid-spring (Oct.-Nov.). The period of maximum abundance of nymphs was from Nov. to mid-Dec. From Jan., most species had a higher proportion of adults, which was the dominant state by mid-autumn (Mar.-Apr.). Only 1 species (Sinipta dalmani) spent the winter as a nymph. Baeacris pseudopunctulactus was the only species with more than 1 generation per year.
On the $h$-principle for Horizontal Immersions in Certain Corank $2$ Fat Distributions

In this article we consider a class of fat corank $2$ distribution on a manifold, which includes the holomorphic contact structures. We prove the h-principle for regular horizontal immersion $\Sigma\to (M,\mathcal{D})$ for such a distribution $\mathcal{D}$ on $M$ if $\dim M \ge 4\dim\Sigma + 6$. In particular, we show that $\mathcal{D}$-horizontal maps always exist provided $\dim M \ge \max \{4\dim\Sigma + 6, 5\dim\Sigma-1\}$.
Examining Redundancy in the Context of Safe Machine Learning

This paper describes a set of experiments with neural network classifiers on the MNIST database of digits. The purpose is to investigate naive implementations of redundant architectures as a first step towards safe and dependable machine learning. We report on a set of measurements using the MNIST database which ultimately serve to underline the expected difficulties in using NN classifiers in safe and dependable systems.
Traffic Agent Trajectory Prediction Using Social Convolution and Attention Mechanism

The trajectory prediction is significant for the decision-making of autonomous driving vehicles. In this paper, we propose a model to predict the trajectories of target agents around an autonomous vehicle. The main idea of our method is considering the history trajectories of the target agent and the influence of surrounding agents on the target agent. To this end, we encode the target agent history trajectories as an attention mask and construct a social map to encode the interactive relationship between the target agent and its surrounding agents. Given a trajectory sequence, the LSTM networks are firstly utilized to extract the features for all agents, based on which the attention mask and social map are formed. Then, the attention mask and social map are fused to get the fusion feature map, which is processed by the social convolution to obtain a fusion feature representation. Finally, this fusion feature is taken as the input of a variable-length LSTM to predict the trajectory of the target agent. We note that the variable-length LSTM enables our model to handle the case that the number of agents in the sensing scope is highly dynamic in traffic scenes. To verify the effectiveness of our method, we widely compare with several methods on a public dataset, achieving a 20% error decrease. In addition, the model satisfies the real-time requirement with the 32 fps.
MCMI: Multi-Cycle Image Translation with Mutual Information Constraints

We present a mutual information-based framework for unsupervised image-to-image translation. Our MCMI approach treats single-cycle image translation models as modules that can be used recurrently in a multi-cycle translation setting where the translation process is bounded by mutual information constraints between the input and output images. The proposed mutual information constraints can improve cross-domain mappings by optimizing out translation functions that fail to satisfy the Markov property during image translations. We show that models trained with MCMI produce higher quality images and learn more semantically-relevant mappings compared to state-of-the-art image translation methods. The MCMI framework can be applied to existing unpaired image-to-image translation models with minimum modifications. Qualitative experiments and a perceptual study demonstrate the image quality improvements and generality of our approach using several backbone models and a variety of image datasets.
A Survey on Sensor Technologies for Unmanned Ground Vehicles

Unmanned ground vehicles (UGVs) have a huge development potential in both civilian and military fields, and have become the focus of research in various countries. In addition, high-precision, high-reliability sensors are significant for UGVs’ efficient operation. This paper proposes a brief review on sensor technologies for UGVs. Firstly, characteristics of various sensors are introduced. Then the strengths and weaknesses of different sensors as well as their application scenarios are compared. Furthermore, sensor applications in some existing UGVs are summarized. Finally, the hotspots of sensor technologies are forecasted to point the development direction.
Hybrid RF/VLC Systems: A Comprehensive Survey on Network Topologies, Performance Analyses, Applications, and Future Directions

Wireless communications refer to data transmissions in unguided propagation media through the use of wireless carriers such as radio frequency (RF) and visible light (VL) waves. The rising demand for high data rates, especially, in indoor scenarios, overloads conventional RF technologies. Therefore, technologies such as millimeter waves (mmWave) and cognitive radios have been adopted as possible solutions to overcome the spectrum scarcity and capacity limitations of the conventional RF systems. In parallel, visible light communication (VLC) has been proposed as an alternative solution, where a light source is used for both illumination and data transmission. In comparison to RF links, VLC links present a very high bandwidth that allows much higher data rates. VLC exhibits also immunity to interference from electromagnetic sources, has unlicensed channels, is a very low power consumption system, and has no health hazard. VLC is appealing for a wide range of applications including reliable communications with low latency such as vehicle safety communication. Despite the major advantages of VLC technology and a variety of its applications, its use has been hampered by its cons such as its dependence on a line of sight connectivity. Recently, hybrid RF/VLC systems were proposed to take advantage of the high capacity of VLC links and better connectivity of RF links. Thus, hybrid RF/VLC systems are envisioned as a key enabler to improve the user rates and mobility on the one hand and to optimize the capacity, interference and power consumption of the overall network on the other hand. This paper seeks to provide a detailed survey of hybrid RF/VLC systems. This paper represents an overview of the current developments in the hybrid RF/VLC systems, their benefits and limitations for both newcomers and expert researchers.
Information geometric aspects of probability paths with minimum entropy production for quantum state evolution

We present an information geometric analysis of both entropic speeds and entropy production rates arising from geodesic evolution on manifolds parametrized by pure quantum states. In particular, we employ pure states that emerge as outputs of suitably chosen [Formula: see text] time-dependent Hamiltonian operators that characterize analog quantum search algorithms of specific types. The [Formula: see text] Hamiltonian models under consideration are specified by external time-dependent magnetic fields within which spin-[Formula: see text] test particles are immersed. The positive definite Riemannian metrization of the parameter manifold is furnished by the Fisher information function. The Fisher information function is evaluated along parametrized squared probability amplitudes obtained from the temporal evolution of these spin-[Formula: see text] test particles. A minimum action approach is then utilized to induce the transfer of the quantum system from its initial state to its final state on the parameter manifold over a finite temporal interval. We demonstrate in an explicit manner that the minimal (that is, optimum) path corresponds to the shortest (that is, geodesic) path between the initial and final states. Furthermore, we show that the minimal path serves also to minimize the total entropy production occurring during the transfer of states. Finally, upon evaluating the entropic speed as well as the total entropy production along optimal transfer paths within several scenarios of physical interest in analog quantum searching algorithms, we demonstrate in a transparent quantitative manner a correspondence between a faster transfer and a higher rate of entropy production. We therefore conclude that higher entropic speed is associated with lower entropic efficiency within the context of quantum state transfer.
Probabilistic Multi-modal Trajectory Prediction with Lane Attention for Autonomous Vehicles

Trajectory prediction is crucial for autonomous vehicles. The planning system not only needs to know the current state of the surrounding objects but also their possible states in the future. As for vehicles, their trajectories are significantly influenced by the lane geometry and how to effectively use the lane information is of active interest. Most of the existing works use rasterized maps to explore road information, which does not distinguish different lanes. In this paper, we propose a novel instance-aware representation for lane representation. By integrating the lane features and trajectory features, a goal-oriented lane attention module is proposed to predict the future locations of the vehicle. We show that the proposed lane representation together with the lane attention module can be integrated into the widely used encoder-decoder framework to generate diverse predictions. Most importantly, each generated trajectory is associated with a probability to handle the uncertainty. Our method does not suffer from collapsing to one behavior modal and can cover diverse possibilities. Extensive experiments and ablation studies on the benchmark datasets corroborate the effectiveness of our proposed method. Notably, our proposed method ranks third place in the Argoverse motion forecasting competition at NeurIPS 2019 1.
GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation

Streaming session-based recommendation (SSR) is a challenging task that requires the recommender system to do the session-based recommendation (SR) in the streaming scenario. In the real-world applications of e-commerce and social media, a sequence of user-item interactions generated within a certain period are grouped as a session, and these sessions consecutively arrive in the form of streams. Most of the recent SR research has focused on the static setting where the training data is first acquired and then used to train a session-based recommender model. They need several epochs of training over the whole dataset, which is infeasible in the streaming setting. Besides, they can hardly well capture long-term user interests because of the neglect or the simple usage of the user information. Although some streaming recommendation strategies have been proposed recently, they are designed for streams of individual interactions rather than streams of sessions. In this paper, we propose a G lobal A ttributed G raph (GAG) neural network model with a Wasserstein reservoir for the SSR problem. On one hand, when a new session arrives, a session graph with a global attribute is constructed based on the current session and its associate user. Thus, the GAG can take both the global attribute and the current session into consideration to learn more comprehensive representations of the session and the user, yielding a better performance in the recommendation. On the other hand, for the adaptation to the streaming session scenario, a Wasserstein reservoir is proposed to help preserve a representative sketch of the historical data. Extensive experiments on two real-world datasets have been conducted to verify the superiority of the GAG model compared with the state-of-the-art methods.
Growth-inhibiting Activity of Transcription Factor C/EBPα, Its Role in Haematopoiesis and Its Tumour Suppressor or Oncogenic Properties in Leukaemias (C/EBPα / cell cycle / proliferation / differentiation / haematopoiesis / leukaemia / tumour suppressor)

The CCAAT/enhancer binding protein alpha (C/EBPα or CEBPA) is the founding member of a fam- ily of related leucine zipper transcription factors that play important roles in myeloid differentiation. Target- ed inactivation of C/EBPα in mice demonstrates its im- portance in the proper development and function of liver, adipose tissue, lung and haematopoietic tissues. C/EBPα is highly expressed in these differentiated tis- sues where it controls differentiation-dependent gene expression and inhibits cell proliferation. Learning more about the precise molecular functions of the C/EBPα protein and how these are affected by leu- kaemogenic mutations should lead to an improved un- derstanding of the cellular functions that are disrupted in patients with AML. Decreased expression of C/EBPα but not C/EBPα mutation has been shown in patients with granulocytic leukaemias that are associated with translocations t(8;21), inv (16) or t(15;17). Derived fu- sion proteins repress C/EBPα expression. Differentia- tion therapy of some AML types is based on restoring C/EBPα function. However, apparently normal C/EBPα
Site Selection for Bud and Germ Tube Emergence in Candida albicans

SUMMARY: The site selection for bud and germ tube emergence has been studied in the dimorphic yeast Candida albicans. The fluorescent dye Calcofluor was used to stain bud scars. Two different modes of site selection were observed in yeasts. On cells cultivated at 23-28 °C, buds emerged primarily at one end of the cell at adjacent sites suggesting a type of persistent memory. Sites selected on yeasts growing at 37 °C were non-adjacent and scattered over the cell. The pattern of bud emergence during resumption of budding from stationary phase, or after temperature shift between conditions, was not a simple transition between modes. During these transitions the budding pattern sequentially passed through non-adjacent and adjacent budding modes before establishing the pattern characteristic of the growth temperature. Germ tubes emerged in a non-adjacent pattern from cells grown to stationary phase at 28 °C. The higher frequency of non-adjacent emergence and the absence of sequential pattern changing in site selection for germ tubes suggested that non-adjacent site selection for germ tubes and buds might not be the same.
IN VITRO ANTIMICROBIAL EFFICACY OF DATURA INNOXIA LEAF EXTRACT AGAINST GRAM POSITIVE BACTERIA

Datura innoxia leaves were examined for potential antibacterial activity by preparing their crude aqueous and Ethanol extracts against Gram-positive bacteria (Bacillu subtilis and Staphylococcus aureus). The results of Disc diffusion method indicated that the pattern of inhibition depends upon the plants part, solvent used for extraction and the organism tested. Extracts prepared from leaves were shown to have better efficacy. Ethanol extracts provided potent antibacterial activity as compared to aqueous extracts. Ethanolic extract showed the antibacterial activity against gram positive bacteria. Gram-positive bacteria Bacillus subtilis were found most sensitive as compared to Staphylococcus aureus. This study concluded that Datura innoxia leaves may present a potentially active antibacterial agent in near future.
Pathological and clinical findings and tissue distribution of Salmonella Gallinarum infection in turkey poults

This study aimed to experimentally induce Salmonella enterica subsp. enterica serovar Enteritidis (Salmonella Gallinarum) infection in turkey poults in order to detect S. Gallinarum using immunohistochemical, bacteriological, and histological procedures. The study included 90 white turkey poults that were divided into 3 groups (1-day-old, 3- week-old, and 2-month-old), each of which was orally inoculated with an inoculum of 0.3 mL of S. Gallinarum strain 9 broth culture that contained approximately 2 × 10 4 , 1 × 10 6 , and 1 × 10 9 cfu/mL, respectively. The duration of the experiment was 18 days. Clinical, pathological, and bacteriological findings were evaluated daily until 18 days post inoculation (pi). The most evident clinical symptoms in all 3 groups were diarrhea and somnolence, especially in 1-day- old group. S. Gallinarum strain 9 was isolated from organs and cloacal swabs up to 16 days pi. Inflammation and typical granulomatous nodules in the internal organs were observed in all 3 groups. Strong immunoreactivity was determined in the lungs, bursa of Fabricius, caecum, ileum, and cloaca of all infected poults up to 18 days pi. In conclusion, S. Gallinarum infection caused heterophilic granulomas, especially in the duodenum of turkey poults, and immunohistochemical analysis can be considered as an adjunct to bacteriological methods in the diagnosis of S. Gallinarum infection.
The 4.5 AStructure of Human AQP2

Located in the principal cells of the collecting duct, aquaporin-2 (AQP2) is responsible for the regulated water reabsorbtion in the kidney and is indispensable for the maintenance of body water balance. Disregulation or malfunctioning of AQP2 can lead to severe diseases such as nephrogenic diabetes insipidus, congestive heart failure, liver cirrhosis and preeclampsia. Here we present the crystallization of recombinantly expressed human AQP2 into two-dimensional protein-lipid arrays and their structural characterization by atomic force microscopy and electron crystallography. These crystals are double-layered sheets that have a