Miracle-dz/UniFAR-FTUs huggingface dataset:
Distinguishing neural correlates of context-dependent advantageous- and disadvantageous-inequity aversion

Significance Despite extensive research on disadvantageous inequity, little is known about advantageous inequity and whether these two types of inequity involve differential neurocognitive mechanisms. We address these questions from the perspective of context dependency and suggest that these two types of inequity are associated with differential neurocognitive substrates, subserved by different brain regions and in particular by the spatial gradient in insular activity. Our findings shed light on how social contexts (i.e., interpersonal guilt) are integrated into social decision making and suggest that the resistance to unequal situations when individuals are in disadvantageous status may primarily stem from their emotional responses, whereas the resistance to unequal situations when individuals are in advantageous status may involve advanced cognitive functions such as mentalizing. Humans can integrate social contextual information into decision-making processes to adjust their responses toward inequity. This context dependency emerges when individuals receive more (i.e., advantageous inequity) or less (i.e., disadvantageous inequity) than others. However, it is not clear whether context-dependent processing of advantageous and disadvantageous inequity involves differential neurocognitive mechanisms. Here, we used fMRI to address this question by combining an interactive game that modulates social contexts (e.g., interpersonal guilt) with computational models that enable us to characterize individual weights on inequity aversion. In each round, the participant played a dot estimation task with an anonymous coplayer. The coplayer would receive pain stimulation with 50% probability when either of them responded incorrectly. At the end of each round, the participant completed a variant of dictator game, which determined payoffs for him/herself and the coplayer. Computational modeling demonstrated the context dependency of inequity aversion: when causing pain to the coplayer (i.e., guilt context), participants cared more about the advantageous inequity and became more tolerant of the disadvantageous inequity, compared with other conditions. Consistently, neuroimaging results suggested the two types of inequity were associated with differential neurocognitive substrates. While the context-dependent processing of advantageous inequity was associated with social- and mentalizing-related processes, involving left anterior insula, right dorsolateral prefrontal cortex, and dorsomedial prefrontal cortex, the context-dependent processing of disadvantageous inequity was primarily associated with emotion- and conflict-related processes, involving left posterior insula, right amygdala, and dorsal anterior cingulate cortex. These results extend our understanding of decision-making processes related to inequity aversion.
Fairness-Aware Relational Learning and Inference

AI and machine learning have become essential tools in automatic decision-making. When used in processes such as employment, education, advertising, loan approval, criminal risk assessment, and policing, these tools can have significant influence on the lives of individuals. This potential influence has raised concerns about algorithmic discrimination and bias. Recently, a number of methods to address these concerns have been proposed. These methods deal with bias through unawareness/blindness, awareness/Lipschitz property (Dwork et al. 2012), demographic parity/disparate impact (Feldman et al. 2015), preference-based (Zafar et al. 2017), and equality of opportunity (Hardt, Price, and Srebro 2016).
Collaborative Privacy Management in Online Social Networks

Online Social Networks (OSNs) are widely used media for users to share various types of contents with their connections. These contents tend to contain private information of users. In most cases, the extent of privacy might not just include the publisher of the content, but also other OSN users that are related to it. Satisfying all privacy requirements of the related users for a content is an important but difficult task, since these requirements might be in conflict. Hence, resolution of the privacy disputes in such cases is essential. Accordingly, we propose a collaborative privacy management system with an agent-based model, where agents represent users and manage their privacy requirements with a modified version of Clarke-Tax mechanism that achieves fair handling of privacy settings and taxes the agents whose privacy settings are chosen.
The Role of Inflammation and Inflammatory Mediators in the Development, Progression, Metastasis, and Chemoresistance of Epithelial Ovarian Cancer

Inflammation plays a role in the initiation and development of many types of cancers, including epithelial ovarian cancer (EOC) and high grade serous ovarian cancer (HGSC), a type of EOC. There are connections between EOC and both peritoneal and ovulation-induced inflammation. Additionally, EOCs have an inflammatory component that contributes to their progression. At sites of inflammation, epithelial cells are exposed to increased levels of inflammatory mediators such as reactive oxygen species, cytokines, prostaglandins, and growth factors that contribute to increased cell division, and genetic and epigenetic changes. These exposure-induced changes promote excessive cell proliferation, increased survival, malignant transformation, and cancer development. Furthermore, the pro-inflammatory tumor microenvironment environment (TME) contributes to EOC metastasis and chemoresistance. In this review we will discuss the roles inflammation and inflammatory mediators play in the development, progression, metastasis, and chemoresistance of EOC.
Outage Analysis of Heterogeneous mmWave Cellular Systems Employing JSDM

In this paper, the outage performance of a single-cell two-tier mmWave cellular system employing joint spatial division and multiplexing (JSDM) are investigated. It is assumed that the macro base station (BS) equipped with a large number of antennas serve the single antenna pico BS (as a relay) and users simultaneously, and that the pico BS is located at the edge of the macro cell. Theoretical analysis of the signal-to- interference-plus-noise ratio (SINR) outage probability of each user is first obtained. The cell SINR outage probability is then derived. Simulation results demonstrating the performance improvement due to the relay introduced by the pico BS are provided. Overall, the impact of deploying pico BS in the single-cell mmWave cellular networks is characterized.
POMDP: State-Based Decision Making for Personalized Assistive Robots

Social and domestic robots are aimed at providing assistance and companionship in the daily life of elderly users. In this context, the robot’s ability to automatically adapt to the user, user-adaptiveness, can be a deciding factor in the system’s success. This work presents αPOMDP, a POMDPbased decision-making mechanism able to learn and adapt to a user, applied to a realistic scenario. Specifically, this work introduces the following key innovative factors: • A novel state-based reward formulation; • A novel learning mechanism and execution loop.
On the Uplink of Large-Scale MIMO Systems with Correlated Ricean Fading Channels

In this work, we focus on the uplink (UL) of a single-cell large-scale multi-user MIMO system where $K$ mono-antenna users communicate with a BS equipped with 𝒩 antennas. This latter estimates the Ricean correlated channels based on training symbols and uses maximum-ratio- combining or linear-minimum-mean-square-error receivers for signal processing. Assuming a fixed number of users with an asymptotic antenna regime, we derive closed-form approximations of the achievable UL rates as a function of the system's parameters, the Ricean factors and the training sequence's length. Accordingly, these expressions are instructive in how the aforementioned parameters impact the performances. Plus, a different processing approach using only the statistics of the channels is investigated and found to be a judicious choice when the Ricean factor is high enough. Although our analytical results are based on a large antenna-limit, we show by simulations that they provide very accurate approximations even for finite system dimensions.
The Opportunities and Challenges of Peroxisome Proliferator-Activated Receptors Ligands in Clinical Drug Discovery and Development

Peroxisome proliferator-activated receptors (PPARs) are a well-known pharmacological target for the treatment of multiple diseases, including diabetes mellitus, dyslipidemia, cardiovascular diseases and even primary biliary cholangitis, gout, cancer, Alzheimer’s disease and ulcerative colitis. The three PPAR isoforms (α, β/δ and γ) have emerged as integrators of glucose and lipid metabolic signaling networks. Typically, PPARα is activated by fibrates, which are commonly used therapeutic agents in the treatment of dyslipidemia. The pharmacological activators of PPARγ include thiazolidinediones (TZDs), which are insulin sensitizers used in the treatment of type 2 diabetes mellitus (T2DM), despite some drawbacks. In this review, we summarize 84 types of PPAR synthetic ligands introduced to date for the treatment of metabolic and other diseases and provide a comprehensive analysis of the current applications and problems of these ligands in clinical drug discovery and development.
Maximum Volume Inscribed Ellipsoid: A New Simplex-Structured Matrix Factorization Framework via Facet Enumeration and Convex Optimization

Consider a structured matrix factorization model where one factor is restricted to have its columns lying in the unit simplex. This simplex-structured matrix factorization (SSMF) model and the associated factorization techniques have spurred much interest in research topics over different areas, such as hyperspectral unmixing in remote sensing, topic discovery in machine learning, to name a few. In this paper we develop a new theoretical SSMF framework whose idea is to study a maximum volume ellipsoid inscribed in the convex hull of the data points. This maximum volume inscribed ellipsoid (MVIE) idea has not been attempted in prior literature, and we show a sufficient condition under which the MVIE framework guarantees exact recovery of the factors. The sufficient recovery condition we show for MVIE is much more relaxed than that of separable non-negative matrix factorization (or pure-pixel search); coincidentally it is also identical to that of minimum volume enclosing simplex, which is known to be a powerful SSMF framework for non-separable problem instances. We also show that MVIE can be practically implemented by performing facet enumeration and then by solving a convex optimization problem. The potential of the MVIE framework is illustrated by numerical results.
Ambient Occlusion Baking via a Feed-Forward Neural Network

We present a feed-forward neural network approach for ambient occlusion baking in real-time rendering. The idea is based on implementing a multi-layer perceptron that allows a general encoding via regression and an efﬁcient decoding via a simple GPU fragment shader. The non-linear nature of multi-layer perceptrons makes them suitable and effective for capturing non-linearities described by ambient occlusion values. A multi-layer perceptron is also random-accessible, has a compact size, and can be evaluated efﬁciently on the GPU. We illustrate our approach of screen-space ambient occlusion based on neural network including its quality, size, and run-time speed.
Point Spread Function Estimation in X-Ray Imaging with Partially Collapsed Gibbs Sampling

The point spread function (PSF) of a translation invariant imaging system is its impulse response, which cannot always be measured directly. This is the case in high energy X-ray radiography, and it must be estimated from images of calibration objects indirectly related to the impulse response. When the PSF is assumed to have radial symmetry, it can be estimated from an image of an opaque straight edge. We use a non-parametric Bayesian approach, where the prior probability density for the PSF is modeled as a Gaussian Markov random field and radial symmetry is incorporated in a novel way. Markov Chain Monte Carlo posterior estimation is carried out by adapting a recently developed improvement to the Gibbs sampling algorithm, referred to as partially collapsed Gibbs sampling. Moreover, the algorithm we present is proven to satisfy invariance with respect to the target density. Finally, we demonstrate the efficacy of these methods on radiographic data obtained from a high-energy X-ray diagnostic system at the U.S. Department of Energy's Nevada National Security Site.
CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning

Extreme Multi-label Learning (XML) considers large sets of items described by a number of labels that can exceed one million. Tree-based methods, which hierarchically partition the problem into small scale sub-problems, are particularly promising in this context to reduce the learn-ing/prediction complexity and to open the way to parallelization. However, the current best approaches do not exploit tree randomization which has shown its efficiency in random forests and they resort to complex partitioning strategies. To overcome these limits, we here introduce a new random forest based algorithm with a very fast partitioning approach called CRAFTML. Experimental comparisons on nine datasets from the XML literature show that it outperforms the other tree-based approaches. Moreover with a paral-lelized implementation reduced to five cores, it is competitive with the best state-of-the-art methods which run on one hundred-core machines.
Cerebellar implementation of movement sequences through feedback

Most movements are not unitary, but are comprised of sequences. Although patients with cerebellar pathology display severe deficits in the execution and learning of sequences (Doyon et al., 1997; Shin and Ivry, 2003), most of our understanding of cerebellar mechanisms has come from analyses of single component movements. Eyelid conditioning is a cerebellar-mediated behavior that provides the ability to control and restrict inputs to the cerebellum through stimulation of mossy fibers. We utilized this advantage to test directly how the cerebellum can learn a sequence of inter-connected movement components in rabbits. We show that the feedback signals from one component are sufficient to serve as a cue for the next component in the sequence. In vivo recordings from Purkinje cells demonstrated that all components of the sequence were encoded similarly by cerebellar cortex. These results provide a simple yet general framework for how the cerebellum can use simple associate learning processes to chain together a sequence of appropriately timed responses.
Transportation Modes Classification Using Feature Engineering

Predicting transportation modes from GPS (Global Positioning System) records is a hot topic in the trajectory mining domain. Each GPS record is called a trajectory point and a trajectory is a sequence of these points. Trajectory mining has applications including but not limited to transportation mode detection, tourism, traffic congestion, smart cities management, animal behaviour analysis, environmental preservation, and traffic dynamics are some of the trajectory mining applications. Transportation modes prediction as one of the tasks in human mobility and vehicle mobility applications plays an important role in resource allocation, traffic management systems, tourism planning and accident detection. In this work, the proposed framework in Etemad et al. is extended to consider other aspects in the task of transportation modes prediction. Wrapper search and information retrieval methods were investigated to find the best subset of trajectory features. Finding the best classifier and the best feature subset, the framework is compared against two related papers that applied deep learning methods. The results show that our framework achieved better performance. Moreover, the ground truth noise removal improved accuracy of transportation modes prediction task; however, the assumption of having access to test set labels in pre-processing task is invalid. Furthermore, the cross validation approaches were investigated and the performance results show that the random cross validation method provides optimistic results.
Quadrangulation through morse-parameterization hybridization

We introduce an approach to quadrilateral meshing of arbitrary triangulated surfaces that combines the theoretical guarantees of Morse-based approaches with the practical advantages of parameterization methods. We first construct, through an eigensolver followed by a few Gauss-Newton iterations, a periodic four-dimensional vector field that aligns with a user-provided frame field and/or a set of features over the input mesh. A field-aligned parameterization is then greedily computed along a spanning tree based on the Dirichlet energy of the optimal periodic vector field, from which quad elements are efficiently extracted over most of the surface. The few regions not yet covered by elements are then upsampled and the first component of the periodic vector field is used as a Morse function to extract the remaining quadrangles. This hybrid parameterization- and Morse-based quad meshing method is not only fast (the parameterization is greedily constructed, and the Morse function only needs to be upsampled in the few uncovered patches), but is guaranteed to provide a feature-aligned quad mesh with non-degenerate cells that closely matches the input frame field over an arbitrary surface. We show that our approach is much faster than Morse-based techniques since it does not require a densely tessellated input mesh, and is significantly more robust than parameterization-based techniques on models with complex features.
Using Morphemes from Agglutinative Languages like Quechua and Finnish to Aid in Low-Resource Translation

Quechua is a low-resource language spoken by nearly 9 million persons in South America (Hintz and Hintz, 2017). Yet, in recent times there are few published accounts of successful adaptations of machine translation systems for low-resource languages like Quechua. In some cases, machine translations from Quechua to Spanish are inadequate due to error in alignment. We attempt to improve previous alignment techniques by aligning two languages that are similar due to agglutination: Quechua and Finnish. Our novel technique allows us to add rules that improve alignment for the prediction algorithm used in common machine translation systems.
Acceptable System Latency for Gaze-Dependent Level of Detail Rendering

Human visual system is unable to perceive all details in the entire field of view. High frequency features are noticeable only at a small angle of 1--2 degrees around the viewing direction. Therefore, it is a reasonable idea to render the coarser object representations for the parafoveal and peripheral visions. A core problem of this gaze-dependent level-of-detail rendering is minimisation of the system latency. In this work we measure how fast should be the whole process of rendering and visualisation to prevent that level-of-detail change will be visible for human observers. We measured in the pilot experiment that even for distant periphery, the change from coarser to fine object representation should take less than 28 ms.
Analysis of Negative Capacitance UTB SOI MOSFETs considering Line-Edge Roughness and Work Function Variation

We analyze the threshold voltage (Vt) and switching time (ST) variations of negative capacitance UTB SOI MOSFETs (NC-SOI) and UTB SOI MOSFETs considering line-edge roughness (LER) and work function variation (WFV). Compared to SOI, NC-SOI exhibits smaller LER induced Vt variations (σVt}) and comparable WFV induced Vt variations. LER induced σVt} can be suppressed by negative capacitance, while WFV induced σVt} cannot be suppressed by negative capacitance. For considering LER, NC-SOI with larger subthreshold swing (SS) and worse short channel effect exhibits better capacitance matching, larger voltage gain (Av), and larger threshold voltage difference (VtNC-SOI-VtSOI), which mitigates the σVt}. For NC-SOI MOSFETs, WFV induced σVt} (=16.2mV) is larger than LER induced σVt} (=3.8mV). For SOI MOSFETs, WFV and LER show comparable σVt}. However, for both NC-SOI and SOI MOSFETs, LER induced ST variations are larger than the WFV induced ST variations. This is because transition charge (Δ Q}) and effective drive current (Ieff) are positively correlated for considering WFV and negatively correlated for considering LER. Compared with SOI, NC-SOI considering LER and WFV exhibits smaller ST variations due to larger Ieff.
Optimal Mobile Association and Power Allocation in Device-to-Device-Enable Heterogeneous Networks with Non-Orthogonal Multiple Access Protocol

In this paper, we investigate mobile association and power allocation in device-to-device (D2D)- enabled heterogeneous networks with non-orthogonal multiple access (NOMA) protocol. We formulate two optimization problems to maximize the minimum rate and the sum rate of the network, respectively. Each problem includes power allocation, access point selection and transmission mode switching. To solve the problems, we develop a two-step method, which can always reach the closed-form solution. Simulation results show that the proposed method can significantly improve the overall throughput of the system, compared with the traditional solutions without D2D communications. In addition, we also investigate the tradeoff between overall throughput and fairness.
miR-145-5p Increases Osteoclast Numbers In Vitro and Aggravates Bone Erosion in Collagen-Induced Arthritis by Targeting Osteoprotegerin

Background Osteoprotegerin (OPG) inhibits bone resorption and binds with strong affinity to receptor activator of NF κB ligand (RANKL), thereby preventing RANKL from binding to its receptor RANK. Osteoclasts have documented effects on bone erosion of rheumatoid arthritis (RA). The aim of this study was to examine the role of miR-145-5p in the regulation of RA osteoclast differentiation and bone erosion. Material/Methods Expression of microRNA-145-5p in human peripheral blood mononuclear cells (PBMC) and synovial tissue was assayed by real-time polymerase chain reaction (RT-PCR). OPG, RANK, and RANKL expression in RAW-264.7 cells was examined by RT-PCR and Western blot analysis. Osteoclast formation was detected by tartrate-resistant acid phosphatase (TRAP) staining. The effect of miR-145-5p on predicted target mRNAs was examined by luciferase reporter assays. Collagen-induced arthritis (CIA) was induced by injecting DBA/1 mice with bovine type II collagen (CII), and miR-145-5p agomir was administered by intravenous injection. Morphological changes in the CIA joint were assessed by micro-computed tomography (CT) and histopathology. Results miR-145-5p levels significantly increased in RA PBMC and synovial tissue compared with normal PBMC and osteoarthritis (OA) tissue. After transfection of RAW-264.7 cells with miR-145-5p, RANK and RANKL expression increased significantly, while OPG expression decreased significantly. TRAP staining results showed osteoclast numbers increased. Micro-CT analysis of the arthritic joints showed that the miR-145-5p agomir caused bone erosion in mice, and histopathological analysis revealed that miR-145-5p agomir aggravates cartilage erosion. Conclusions Our findings indicate that administration of miR-145-5p aggravates joint erosion in CIA mice. This suggests that miR-145-5p is a potential target for the treatment of RA.