Miracle-dz/UniFAR-FTUs huggingface dataset:
Cell and Langerhans Cell Development Human CD34-Derived Interstitial Dendritic Tight Control of STAT5 Activity Determines

Despite the crucial function of dendritic cells (DC) in immunity, the molecular mechanisms regulating human DC development remain poorly deﬁned. STAT5 regulates various hematopoietic lineages and is activated by GM-CSF, a critical cytokine in DC development. In this study, we investigated the role of STAT5 during differentiation of human CD34 + hematopoietic progenitors into precursor DC (pre-DC) and their subsequent differentiation toward interstitial DC and Langerhans cells. Inhibiting STAT5 activity by dominant-negative STAT5 promoted Langerhans cell commitment of hematopoietic progenitors but resulted in loss of pre-interstitial DC development, showing subset-speciﬁc regulation. Increasing the low endogenous STAT5 activity by ectopic STAT5 activation downregulated expression of the critical DC transcription factor PU.1 and abrogated commitment to either DC lineage. In contrast, high STAT5 activity was beneﬁcial in already committed pre-DC: terminal DC differentiation was associated with increased endogenous STAT5 phosphorylation levels, JAK2–STAT5 inhibition reduced terminal DC differentiation, and conditional STAT5 activation in pre-DC improved development of BDCA-1 + , DC-SIGN + , and Langerin + DC with normal maturation and T cell stimulation. These data show that STAT5 critically regulates human DC development, with speciﬁc requirements for the level of STAT5 activation at distinct differentiation stages. By regulating STAT5 activity, cytokines present at speciﬁc locations and under different pathophysiological conditions can determine the fate of DC precursors. The Journal of Immunology , 2011, 186: 7016–7024.
Upper Lithospheric Structure of the Middle Benue Trough, Nigeria, Derived from Analysis of Satellite Gravity Data

The middle Benue Trough is the central segment of the Benue Trough, an elongate northeast orientated intracratonic rift basin in Nigeria. This study provides new information on the morphology/geometry of the upper lithospheric structures (Moho, crystalline crust thickness and crustal stretching factor) and tectonics in the middle Benue Trough and adjacent basement complex regions by analysis and three-dimensional (3D) inverse modelling of satellite Bouguer gravity anomaly data. Results from 3D inverse modelling reveal Moho depths varying from c. 24 ± 2 to 32 ± 3 km and crystalline crustal thickness values ranging between c. 20 ± 2 and 32 ± 3 km. Shallow Moho and thin crust occur beneath the Trough axial region, whereas deep Moho and thick crust underlay the basement complex regions and the Trough northwestern region. It also shows the presence of a prominent northeast-southwest (NE-SW) orientated, shallow Moho structures (c. 24 ± 2 to 27 ± 3 km deep) and thin crust (c. 20 ± 2 to 26 ± 3 km thick) within the Trough axial region. Comparisons of Moho depths with topography and Bouguer gravity anomaly values using profiles highlight that the observed broad, longer-wavelength near-positive Bouguer gravity anomalies over the Trough axial region are strongly associated with elevated (shallow) Moho and isostatic effect of lower topography. Crustal stretching factor (â) values obtained across the area range between c. 1.03 and 1.59 with moderately high values (c. 1.25 – 1.59) occurring in the Trough axial region. The tectonic implication of the results is that the crust/lithosphere beneath middle Benue Trough has been strongly affected/modified by intense regional, extensional and/or wrench (strike-slip) tectonics associated with past rifting events. The imaged Moho and crystalline crust structures beneath the Trough axial region exhibit a major NE-SW orientation which is strikingly similar to the orientation of the Benue Trough, structures and regional Bouguer gravity anomalies. The study reveals that the shallow faulted crustal basements, shallow Moho and thin crystalline crust that characterised the Trough axial region were mainly formed through (regional) extensional tectonics which affected the underlying crust/lithosphere during rifting events in the Early to Late Cretaceous.
Incidence of sucking insect-pests of okra and their correlation with abiotic factors

Investigation on incidence of sucking insect-pests of okra and its relationship with weather parameters was carried out during Kharif , 2019 at experimental area, Department of Entomology, CCSHAU, Hisar. Observations for sucking pests were recorded at weekly interval from three leaves per plant, starting from 27 th Standard Meteorological Week (SMW). Maximum population of leafhopper (8.8 nymphs/ leaf) and whitefly (3.3 adults/ leaf) was observed during 31 st and 32 nd SMW, respectively. While the minimum population of leafhopper (0.2 nymphs/ leaf) and whitefly (0.1 adults/ leaf) was recorded during 39 th and 27 th SMW, respectively. Leafhopper population was significantly and negatively correlated with maximum temperature, while significant and positively correlated with evening relative humidity and rainfall. Correlation of leafhopper with all other weather parameters was negative and non-significant. While considering the whitefly population, it was found significant and positively correlated with rainfall, morning and evening relative humidity; however, a negative and significant correlation existed with maximum temperature. It was concluded that maximum population of sucking insect-pests was recorded during August month and it increases with rainfall.
A QUANTITATIVE STUDY ON THE FACTORS INFLUENCING HIGHER EDUCATION INSTITUTIONS STUDENTS’ ACCEPTANCE OF MOBILE PAYMENTS IN KLANG VALLEY, MALAYSIA

The adoption of mobile payment is still considered new for an evolving economy like Malaysia whereby mobile payment has yet to be fully recognized and accepted by a majority of Malaysians. There are several factors that could influence the acceptance of mobile payment. While numerous studies on factors affecting the intention of adopting mobile payment services had been conducted in different age groups and in different countries, there is still a lack of studies conducted in this particular field, especially in Malaysia. Thus, the purpose of this study is to examine how factors such as personal innovativeness, perceived compatibility and facilitating condition could affect the intention to accept mobile payment among higher education institution students in Klang Valley, Malaysia, using the Technology Acceptance Model (TAM). A total of 104 valid responses were collected and analyzed using the SPSS software to carry out descriptive and frequency analysis of the variables, as well as conducting tests such as reliability, normality, and hypotheses testing. The results achieved in this study demonstrate that perceived compatibility have a positive significance on acceptance of mobile payment, while personal innovativeness and facilitating condition do not have any significant influence on the intention to accept mobile payment among the higher education institution students in Klang Valley, Malaysia. Subsequently, this study highlights the discussion of the findings, together with the conclusion, limitations of this research, and recommendations for future research.
Correlations of linear sizes of molars with cephalometric indicators of practically healthy men of the southern region of Ukraine BIOMEDICAL AND BIOSOCIAL

The high prevalence of pathologies of the dental and tooth-jaw system both in Ukraine and abroad requires scientists to search for new, fundamental discoveries, in particular, to identify patterns of the relationship between cephalometric and odontometric indicators within certain ethnic populations. The purpose of the study is to establish the features of the correlations of computed tomography sizes of molars with cephalometric parameters of practically healthy men in the southern region of Ukraine. 33 practically healthy men of the first mature age, inhabitants of the southern region of Ukraine (from Mykolayiv, Kherson, Odesa, Zaporizhia regions and the Autonomous Republic of Crimea) underwent cephalometry and cone-beam computed tomography followed by odontometry of molars. The correlations between odonto- and cephalometric parameters were assessed in the license package "Statistica 6.1" using non-parametric Spearman statistics. In almost healthy men of the southern region of Ukraine in the qualitative analysis of multiple nature, mostly direct reliable and medium unreliable correlations, found only between most sizes of molars on the lower jaw and the length and height of the nose. Quantitative analysis revealed the most significant correlations between computed tomography sizes of molars and face sizes (7.4 % on the upper jaw, most of which are inverse, and 13.2 % on the lower jaw, almost evenly straight and inverse). With the indicators of the skull, the relative majority of reliable, mostly inverse correlations are observed with the height of the teeth, their crowns and the length of the roots (on the upper jaw 11.4 %, on the lower jaw 5.4 %); and with indicators of the facial skull - almost evenly with the height of the teeth, their crowns and the length of the roots (on the upper jaw 6.4 %, on the lower jaw 14.6 %, in both cases mostly reverse) and with vestibular-lingual and mesio-distal dimensions (on the upper jaw 8.7 %, mostly reverse, on the lower jaw 11.8 %, mostly straight). The obtained results of correlation analysis of computed tomography sizes of molars with cephalometric indicators and indices confirm the population specificity of the features of the dental system and are necessary for the correct construction of regression models.
Towards Self-Powered Wearables via Wrist-Worn Energy Harvesters

Wearable electronics are demonstrating great potential in many areas, but finite capacity of chemical batteries become a bottleneck of their applications. We develop three kinds of wrist-worn energy harvesters that have different power enhancement mechanisms to address this issue. A compact energy harvester with magnetic frequency-up converter is developed to increase energy conversion capacity while a tiny repulsive magnetic spring is introduced to enhance response motion. Besides, a proof-massless energy harvester is investigated to improve motion capture capacity of wrist-worn energy harvester. A general model is constructed to predict the performances of these energy harvesters. The experimental results show that these energy harvesters achieve significant power improvement and their power output and power density can reach ten times that of the counterparts.
Rethinking Saliency Map: A Context-Aware Perturbation Method to Explain EEG-Based Deep Learning Model

Deep learning is widely used to decode the electroencephalogram (EEG) signal. However, there are few attempts to specifically study how to explain EEG-based deep learning models. In this paper, we review the related works that attempt to explain EEG-based models. And we find that the existing methods are not perfect enough to explain the EEG-based model due to the non-stationary nature, high inter-subject variability and dependency of EEG data. The characteristics of the EEG data require the explanation to incorporate the instance-level saliency identification and the context information of EEG data. Recently, mask perturbation is proposed to explain deep learning model. Inspired by the mask perturbation, we propose a new context-aware perturbation method to address these concerns. Our method not only extends the scope to the instance level but can capture the representative context information when estimating the saliency map. In addition, we point out another role of context information in explaining the EEG-based model. The context information can also help suppress the artifacts in the EEG-based deep learning model. In practice, some users might want a simple version of the explanation, which only indicates a few features as salient points. To further improve the usability of our method, we propose an optional area limitation strategy to restrict the highlighted region. In the experiment section, we select three representative EEG-based models and implement them on the emotional EEG dataset DEAP. The results of the experiments support the advantages of our method.
Efficient Policy Iteration for Robust Markov Decision Processes via Regularization

Robust Markov decision processes (MDPs) provide a general framework to model decision problems where the system dynamics are changing or only partially known. Efficient methods for some \texttt{sa}-rectangular robust MDPs exist, using its equivalence with reward regularized MDPs, generalizable to online settings. In comparison to \texttt{sa}-rectangular robust MDPs, \texttt{s}-rectangular robust MDPs are less restrictive but much more difficult to deal with. Interestingly, recent works have established the equivalence between \texttt{s}-rectangular robust MDPs and policy regularized MDPs. But we don't have a clear understanding to exploit this equivalence, to do policy improvement steps to get the optimal value function or policy. We don't have a clear understanding of greedy/optimal policy except it can be stochastic. There exist no methods that can naturally be generalized to model-free settings. We show a clear and explicit equivalence between \texttt{s}-rectangular $L_p$ robust MDPs and policy regularized MDPs that resemble very much policy entropy regularized MDPs widely used in practice. Further, we dig into the policy improvement step and concretely derive optimal robust Bellman operators for \texttt{s}-rectangular $L_p$ robust MDPs. We find that the greedy/optimal policies in \texttt{s}-rectangular $L_p$ robust MDPs are threshold policies that play top $k$ actions whose $Q$ value is greater than some threshold (value), proportional to the $(p-1)$th power of its advantage. In addition, we show time complexity of (\texttt{sa} and \texttt{s}-rectangular) $L_p$ robust MDPs is the same as non-robust MDPs up to some log factors. Our work greatly extends the existing understanding of \texttt{s}-rectangular robust MDPs and naturally generalizable to online settings.
Variational Transformer: A Framework Beyond the Trade-off between Accuracy and Diversity for Image Captioning

Accuracy and Diversity are two essential metrizable manifestations in generating natural and semantically correct captions. Many efforts have been made to enhance one of them with another decayed due to the trade-off gap. In this work, we will show that the inferior standard of accuracy draws from human annotations (leave-one-out) are not appropriate for machine-generated captions. To improve diversity with a solid accuracy performance, we exploited a novel Variational Transformer framework. By introducing the"Invisible Information Prior"and the"Auto-selectable GMM", we instruct the encoder to learn the precise language information and object relation in different scenes for accuracy assurance. By introducing the"Range-Median Reward"baseline, we retain more diverse candidates with higher rewards during the RL-based training process for diversity assurance. Experiments show that our method achieves the simultaneous promotion of accuracy (CIDEr) and diversity (self-CIDEr), up to 1.1 and 4.8 percent. Also, our method got the most similar performance of the semantic retrieval compared to human annotations, with 50.3 (50.6 of human) for R@1(i2t).
Diesel Combustion Analysis Based on Two-Zone Model. (Comparison between Model Analysis and Experiment).

The proposed two-zone model of diesel combustion consists of a burned zone and an unburned zone, and the thermodynamic process is independent and adiabatic in each zone except that the air is entrained from the unburned zone to the burned zone under a specified condition of the excess air ratio during the combustion period. The excess air ratio in the diffusion combustion period is assumed to be constant in the present model. The calculated time histories of the burned-zone gas temperature and the cumulative soot formation were compared with the measured time histories of the flame temperature and the KL value based on the infrared two-color method. The calculated results agree qualitatively and partly quantitatively with the experimental results except for the swirl effect. As a result, it is shown that the present two-zone model analysis is very useful and effective in evaluating the combustion process in a DI diesel engine.
Representation Gap in Deep Reinforcement Learning

Deep reinforcement learning gives the promise that an agent learns good policy 1 from high-dimensional information. Whereas representation learning removes ir-2 relevant and redundant information and retains pertinent information. We consider 3 the representation capacity of action value function and theoretically reveal its 4 inherent property, representation gap with its target action value function. This 5 representation gap is favorable. However, through illustrative experiments, we 6 show that the representation of action value function grows similarly compared 7 with its target value function, i.e. the undesirable inactivity of the representation 8 gap ( representation overlap ). Representation overlap results in a loss of repre-9 sentation capacity, which further leads to sub-optimal learning performance. To 10 activate the representation gap, we propose a simple but effective framework Policy 11 Optimization from Preventing Representation Overlaps (POPRO), which regular-12 izes the policy evaluation phase through differing the representation of action value 13 function from its target. We also provide the convergence rate guarantee of POPRO. 14 We evaluate POPRO on gym continuous control suites. The empirical results show 15 that POPRO using pixel inputs outperforms or parallels the sample-efficiency of 16 methods that use state-based features. 17
Universality in Anderson localization on random graphs with varying connectivity

We perform a thorough and complete analysis of the Anderson localization transition on several models of random graphs with regular and random connectivity. The unprecedented precision and abundance of our exact diagonalization data (both spectra and eigenstates), together with new finite size scaling and statistical analysis of the graph ensembles, unveils a universal behavior which is described by two simple, integer, scaling exponents. A by-product of such analysis is a reconciliation of the tension between the results of perturbation theory coming from strong disorder and earlier numerical works, which seemed to suggest that there should be a non-ergodic region above a given value of disorder W_{E}WE which is strictly less than the Anderson localization critical disorder W_CWC, and that of other works which suggest that there is no such region. We find that, although no separate W_{E}WE exists from W_CWC, the length scale at which fully developed ergodicity is found diverges like |W-W_C|^{-1}|W−WC|−1, while the critical length over which delocalization develops is \sim |W-W_C|^{-1/2}∼|W−WC|−1/2. The separation of these two scales at the critical point allows for a true non-ergodic, delocalized region. In addition, by looking at eigenstates and studying leading and sub-leading terms in system size-dependence of participation entropies, we show that the former contain information about the non-ergodicity volume which becomes non-trivial already deep in the delocalized regime. We also discuss the quantitative similarities between the Anderson transition on random graphs and many-body localization transition.
Domain Constraints in Feature Space: Strengthening Robustness of Android Malware Detection against Realizable Adversarial Examples

—Strengthening the robustness of machine learning-based Android malware detectors in the real world requires incorporating realizable adversarial examples (RealAEs), i.e., AEs that satisfy the domain constraints of Android malware. However, existing work focuses on generating RealAEs in the problem space, which is known to be time-consuming and impractical for adversarial training. In this paper, we propose to generate RealAEs in the feature space, leading to a simpler and more efﬁcient solution. Our approach is driven by a novel interpretation of Android malware properties in the feature space. More concretely, we extract feature-space domain constraints by learning meaningful feature dependencies from data and applying them by constructing a robust feature space. Our experiments on DREBIN, a well-known Android malware detector, demonstrate that our approach outperforms the state-of-the-art defense, Sec-SVM, against realistic gradient- and query-based attacks. Additionally, we demonstrate that generating feature-space RealAEs is faster than generating problem-space RealAEs, indicating its high applicability in adversarial training. We further validate the ability of our learned feature-space domain constraints in representing the Android malware properties by showing that (i) re-training detectors with our feature-space RealAEs largely improves model performance on similar problem-space RealAEs and (ii) using our feature-space domain constraints can help distinguish RealAEs from unrealizable AEs (unRealAEs).
MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers

Mixed-integer programming (MIP) technology offers a generic way of formulating and solving combinatorial optimization problems. While generally reliable, state-of-the-art MIP solvers base many crucial decisions on hand-crafted heuristics, largely ignoring common patterns within a given instance distribution of the problem of interest. Here, we propose MIP-GNN, a general framework for enhancing such solvers with data-driven insights. By encoding the variable-constraint interactions of a given mixed-integer linear program (MILP) as a bipartite graph, we leverage state-of-the-art graph neural network architectures to predict variable biases, i.e., component-wise averages of (near) optimal solutions, indicating how likely a variable will be set to 0 or 1 in (near) optimal solutions of binary MILPs. In turn, the predicted biases stemming from a single, once-trained model are used to guide the solver, replacing heuristic components. We integrate MIP-GNN into a state-of-the-art MIP solver, applying it to tasks such as node selection and warm-starting, showing significant improvements compared to the default setting of the solver on two classes of challenging binary MILPs. Our code and appendix are publicly available at https://github.com/lyeskhalil/mipGNN.
Fast Object Placement Assessment

Object placement assessment (OPA) aims to predict the rationality score of a composite image in terms of the placement ( e.g. , scale, location) of inserted foreground object. However, given a pair of scaled foreground and background, to enumerate all the reasonable locations, existing OPA model needs to place the foreground at each location on the background and pass the obtained composite image through the model one at a time, which is very time-consuming. In this work, we investigate a new task named as fast OPA. Speciﬁcally, provided with a scaled foreground and a background, we only pass them through the model once and predict the rationality scores for all locations. To accomplish this task, we propose a pioneering fast OPA model with several innovations ( i.e. , foreground dynamic ﬁlter, background prior transfer, and composite feature mimicking) to bridge the performance gap between slow OPA model and fast OPA model. Extensive experiments on OPA dataset show that our proposed fast OPA model performs on par with slow OPA model but runs signiﬁcantly faster.