Miracle-dz/UniFAR-FTUs huggingface dataset:
Single cell RNA sequencing analysis of mouse cochlear supporting cell transcriptomes with activated ERBB2 receptor indicates a cell-specific response that promotes CD44 activation

Hearing loss caused by the death of cochlear hair cells (HCs) might be restored through regeneration from supporting cells (SCs) via dedifferentiation and proliferation, as observed in birds. In a previous report, ERBB2 activation in a subset of cochlear SCs promoted widespread down-regulation of SOX2 in neighboring cells, proliferation, and the differentiation of HC-like cells. Here we analyze single cell transcriptomes from neonatal mouse cochlear SCs with activated ERBB2, with the goal of identifying potential secreted effectors. ERBB2 induction in vivo generated a new population of cells with de novo expression of a gene network. Called small integrin-binding ligand n-linked glycoproteins (SIBLINGs), these ligands and their regulators can alter NOTCH signaling and promote cell survival, proliferation, and differentiation in other systems. We validated mRNA expression of network members, and then extended our analysis to older stages. ERBB2 signaling in young adult SCs also promoted protein expression of gene network members. Furthermore, we found proliferating cochlear cell aggregates in the organ of Corti. Our results suggest that ectopic activation of ERBB2 signaling in cochlear SCs can alter the microenvironment, promoting proliferation and cell rearrangements. Together these results suggest a novel mechanism for inducing stem cell-like activity in the adult mammalian cochlea.
Reframing Bike Challenge Problem using Model Selection

Bike challenge is an important real-life transportation problem where we need to predict the future availability of bikes based on the currently available bikes. Here the given problem does not provide enough training data for a particular test station. Therefore, the challenge becomes to use the given models of similar stations to perform the prediction. In this paper, we propose an eﬃcient model selection approach to solve this problem.
Expertise, gender, and equilibrium play

Mixed‐strategy Nash equilibrium is the cornerstone of our understanding of strategic situations that require decision makers to be unpredictable. Using data from nearly half a million serves over 3000 tennis matches, and data on player rankings from the ATP and WTA, we examine whether the behavior of professional tennis players is consistent with equilibrium. We find that win rates conform remarkably closely to the theory for men, but conform somewhat less neatly for women. We show that the behavior in the field of more highly ranked (i.e., better) players conforms more closely to theory. We show that the statistical tests used in the prior related literature are not valid for large samples like ours; we develop a novel statistical test that is valid and show, via Monte Carlo simulations, that it is more powerful against the alternative that receivers follows a nonequilibrium mixture.
Master Thesis Fast, scalable worst-case optimal joins for graph-pattern matching on in-memory graphs in Spark

Graph pattern matching is a challenge for data processing systems like Spark because the size of the intermediary result required by finding the matches with standard binary join operators grow over linear with regards to the inputs. Recently popularized worst-case optimal join algorithms, WCOJs, seem a better match to tackle this challenge because they do not materialize the aforementioned large intermediary results. We investigate two major open questions regarding WCOJs in graph use cases. First, we develop a WCOJ specialized to graph-pattern matching, where the joins are self-joins on an edge table and compare its performance with a generic WCOJ. Second, we propose a novel method for distributed execution of such WCOJs. We show that the previously proposed methods to distribute WCOJs that would fit Spark, do not scale well to large graph patterns (five vertices and more). Based on this result, we propose to keep the edge relationship cached in compressed form on all workers but distribute the computation using a logical partitioning. Our approach trades off memory usage for better scalability. This is reasonable as most graphs today fit in main memory [37]. In all, our work provides the first scalable Spark implementation of a WCOJ, very suited for graph use.
MetaLight: Value-Based Meta-Reinforcement Learning for Traffic Signal Control

Using reinforcement learning for traffic signal control has attracted increasing interests recently. Various value-based reinforcement learning methods have been proposed to deal with this classical transportation problem and achieved better performances compared with traditional transportation methods. However, current reinforcement learning models rely on tremendous training data and computational resources, which may have bad consequences (e.g., traffic jams or accidents) in the real world. In traffic signal control, some algorithms have been proposed to empower quick learning from scratch, but little attention is paid to learning by transferring and reusing learned experience. In this paper, we propose a novel framework, named as MetaLight, to speed up the learning process in new scenarios by leveraging the knowledge learned from existing scenarios. MetaLight is a value-based meta-reinforcement learning workflow based on the representative gradient-based meta-learning algorithm (MAML), which includes periodically alternate individual-level adaptation and global-level adaptation. Moreover, MetaLight improves the-state-of-the-art reinforcement learning model FRAP in traffic signal control by optimizing its model structure and updating paradigm. The experiments on four real-world datasets show that our proposed MetaLight not only adapts more quickly and stably in new traffic scenarios, but also achieves better performance.
Estimating Participation and Spill-over Effects in Conditional Cash Transfer Programs

In this paper we propose a method to estimate externality effects in cash transfer programmes, even in cases when the benefit is not randomly assigned. Externality is assessed through the decomposition of the average treatment effect on the treated (ATT) into participation (direct) effect and externality (indirect) effect. We also suggest a nonparametric decomposition to investigate whether changes in household outcomes are caused by the income transfer itself or by the other non-monetary components of the programme, such as conditionalities and family support services. We apply all these decompositions on the effect of a conditional cash transfer (CCT) programme on household consumption and savings in Paraguay. This was possible because of the presence of two distinct comparison groups, one within the village and potentially exposed to the externality, and another in a different village not affected by the programme. Furthermore, the evaluation survey collected information on both income and consumption. The results indicate that the programme has a small impact on consumption and a considerable impact on savings. In the absence of externality, however, the programme would have a higher effect on consumption, mostly associated with the cash transfer, and a lower effect on savings. Moreover, the impact on the pattern of consumption is significantly related to a substitution effect and is not related to the increase in income.
Introducing a Strategic Information Systems Planning Meta-Introducing a Strategic Information Systems Planning Meta-Method for Cooperative Inter-Organization Relationships Method for Cooperative Inter-Organization Relationships

— Strategic Information Systems Planning (SISP) and aligning IT with business has been in a key focus of IS managers for decades already. Constant changes in business environment and developments in technologies are hardly making the effort any easier. Characteristic for available SISP methods is their focus on a single organization. However, in current network economy the role of inter organizational systems is increasing creating a need for Inter Organizational Strategic Information Systems Planning (IOSISP). In this paper a general requirements for such are sought and presented in a form of meta method.
Limb contraction drives fear perception

Humans and other primate species are experts at recognizing affective information from body movements but the underlying brain mechanisms are still largely unknown. Previous research focusing on the brain representation of symbolic emotion categories has led to mixed results. This study used representational similarity and multi-voxel pattern analysis techniques to investigate how postural and kinematic features computed from affective whole-body movement videos are related to brain processes. We show that body posture and kinematics differentially activated brain regions indicating that this information might be selectively encoded in these regions. Most specifically, the feature limb contraction seemed to be particularly relevant for distinguishing fear and it was represented in several regions spanning affective, action observation and motor preparation networks. Our approach goes beyond traditional methods of mapping symbolic emotion categories to brain activation/deactivation by discovering which specific movement features are encoded in the brain, and possibly drive automatic emotion perception.
The Effect of Population Distribution on Government Expenditure in West Africa

This study examined the relationship between government expenditure and population distribution in West African countries between 1980 and 2013. In this study, panel estimation method was used to examine the effect of demographic variables, proxied by aged dependent population, young dependent population and urbanization rate, on government expenditure. Other regressors used in this study are domestic income and inflation rate. The result of the panel estimation shows that the determinants of public expenditure growth in West Africa are domestic income, inflation rate, urbanization rate and aged dependency population. Therefore, population control policies which target growing aged dependency rate and urbanization should be considered in the determination and management of public expenditure.
New York State P-12 Science Learning Standards

*The performance expectations marked with an asterisk integrate traditional science content with engineering through a Practice or Disciplinary Core Idea. The text in the “Disciplinary Core Ideas” section is reproduced verbatim from A Framework for K-12 Science Education: Practices, Cross-Cutting Concepts, and Core Ideas unless it is preceded by (NYSED). P ag e6 4 HS. Structure and Function Students who demonstrate understanding can: HS-LS1-1. Construct an explanation based on evidence for how the structure of DNA determines the structure of proteins which carry out the essential functions of life through systems of specialized cells. [Clarification Statement: Emphasis should be on how the DNA code is transcribed and translated in the synthesis of proteins. Types of proteins involved in performing life functions include enzymes, structural proteins, cell receptors, hormones, and antibodies. ] [Assessment Boundary: Assessment does not include identification of specific cell or tissue types, whole body systems, specific protein structures and functions, or the detailed biochemistry of protein synthesis. ] HS-LS1-2. Develop and use a model to illustrate the hierarchical organization of interacting systems that provide specific functions within multicellular organisms. [Clarification Statement: Emphasis is on functions at the organism’s system level such as nutrient uptake, water delivery, immune response, and organism response to stimuli. An example of an interacting system could be an artery depending on the proper function of elastic tissue and smooth muscle to regulate and deliver the proper amount of blood within the circulatory system. ] [Assessment Boundary: Assessment does not include interactions and functions at the molecular or chemical reaction level. ] HS-LS1-3. Plan and conduct an investigation to provide evidence that feedback mechanisms maintain homeostasis [Clarification Statement: Examples of investigations could include heart rate response to exercise, stomate response to moisture and temperature, and root development in response to water levels. ] [Assessment Boundary: Assessment does not include the cellular processes involved in the feedback mechanism. ] The performance expectations above were developed using the following elements from the NRC document A Framework for K-12 Science Education:
Using lattice packings to create extended codebooks

In fields such as communications, the mathematical theory of codes is useful to quickly and accurately transmit information. One continuing goal of coding theory is to create ”optimal” codes. Specifically, optimizing codes entails improving such properties of the codes as rate of transmission and error-correcting ability, as well as minimizing the average power required to send messages. One method of finding improved codes is that of extending existing codes. Extending a code can often provide a new codebook which improves upon the properties of the old code. Gilbert and Tropp [1] mention a particularly interesting method of extending existing codebooks. Let Λ be the coordinates of the points in a m-dimensional sphere-packing; that is, if λ ∈ Λ, then λ = (a1, ., am). For the purposes of this paper, we are mostly interested in the case in which Λ is the set of points of a lattice, but this is not necessary. Let CB be a d-dimensional pre-existing code (which will now be referred to as the ”base codebook”) consisting of at least m codewords, CB = {b1,b2, . ,bs : s ≥ m}. Our extended codebook, C, is created by using the coordinates of each lattice point as coefficients of all possible m-term subsets of CB , forming all possible linear combinations. In other words, create a m x n matrix M which has exactly m distinct words from CB as its rows. The corresponding new codeword, c, in our extended codebook is the product of a lattice point λ with M , so
Hierarchical Classiﬁcation of Diatom Images using Predictive Clustering Trees

This paper presents a hierarchical multi-label classiﬁcation (HMC) system for diatom image classiﬁcation. HMC is a variant of classiﬁcation where an instance may belong to multiple classes at the same time and these classes/labels are organized in a hierarchy. Our approach to HMC exploits the classiﬁcation hierarchy by building a single predictive clustering tree (PCT) that can simultaneously predict all diﬀerent levels in the hierarchy of taxonomic ranks: genus, species, variety, and form. Hence, PCTs are very eﬃcient: a single classiﬁer is valid for the hierarchical classiﬁcation scheme as a whole. To improve the predictive performance of the PCTs, we construct ensembles of PCTs. We evaluate our system on the ADIAC database with diatom images. We apply several feature extraction techniques that can be used in the context of diatom images. Moreover, we investigate whether combination of these techniques increase the predictive performance. The experiments show that our system outperforms the most widely used approaches for image annotation.
Molecular Dynamics Simulation of Human Pancreatic Lipase and Lipase-colipase Complex: Insight into the Structural Fluctuations and Conformational Changes

Although the structure of Human Pancreatic Lipase has been documented through the X-ray crystallography, the knowledge about the molecular rearrangement and dynamic equilibrium in the structure (particularly in the catalytic triad and lid domains) is very scanty. The structural fluctuations and conformational changes undergo by Human Pancreatic Lipase (HPL) with and without colipase were computationally investigated through molecular dynamics simulation technique using GROMACS 2018.4, MOE 2016.0801 and VMD softwares in order to gain insight into the complex transitions at different domains. The structural stability was revealed vis-a-vis Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuations (RMSF) plots. The levels of compactness/folding and conformational changes of the protein were determined using Radius of gyration and secondary analysis respectively. Salt bridge analysis gives more ionic pairs interactions than experimentally determined results. Results show that though both proteins are stable, lipase-colipase complex is more deviated and flexible than lipase. Also, additional information regarding the conformational transitions, interactions and dynamics that govern stability of lipase-colipase complex which were ‘hidden’ to experimental techniques were revealed.
Energy artificial intelligence for effective energy imbalance cost reduction in the steel industry Authors names and affiliations

Renewable energy sources play a key role to achieve global CO2 emission targets. However, the increasing penetration of solar and wind generation impacts short-term volatility and grid stability, ultimately resulting in higher imbalance penalties for large industrials. Such uncontrolled price fluctuations impose risks for steel plants with an electric arc furnace as the imbalance cost is a significant cost driver. Alpiq and Swiss Steel established a convenient solution to reduce imbalance volume using artificial intelligence and real-time internet-of-things (IoT). The core of the method is a self-learning algorithm predicting the plant’s power consumption for the next few hours based on real-time metering, day ahead forecast and further data. The forecast continuously predicts a 15-min profile which reflects latest available data, hence considering any deviations from the planned production schedule. The prediction is then applied as correction schedule and the differential volume placed as intraday re-nomination, unless overridden by Swiss Steel. We obtain a fully automated information flow with industrial IoT-gateways collecting data, a cloud-algorithm and automated communication between energy data management (EDM) systems. With the new practice an imbalance volume reduction of up to 27% is achieved, effectively reducing cost and protecting against unpredictable imbalance prices. State-of-the-art technology enabled an attractive payback time. On top of the immediate benefit, the setup paves the way for future improvements, e.g. forecast horizon and performance. Furthermore, valuable process and performance insights beyond energy may be obtained in the future thanks to systematic real-time data availability and automated analytics. Besides the cost saving advantage for the steel plant the concept also leads to better absorption of renewable energy in the market. This demonstrates how digital energy technology creates tangible value for power consumers while catalyzing the clean-energy transition. The best practice emphasizes the innovation spirit of the industry and increases its competitiveness.
PointPoseNet: Point Pose Network for Robust 6D Object Pose Estimation

In this paper, we propose a novel pipeline to estimate 6D object pose from RGB-D images of known objects present in complex scenes. The pipeline directly operates on raw point clouds extracted from RGB-D scans. Specifically, our method takes the point cloud as input and regresses the point-wise unit vectors pointing to the 3D keypoints. We then use these vectors to generate keypoint hypotheses from which the 6D object pose hypotheses are computed. Finally, we select the best 6D object pose from the hypotheses based on a proposed scoring mechanism with geometry constraints. Extensive experiments show that the proposed method is robust against the variety in object shape and appearance as well as occlusions between objects, and that our method outperforms the state-of-the-art methods on the LINEMOD and Occlusion LINEMOD datasets.
Remembering that “Everybody Hurts”: The Role of Self-Compassion in Responses to Rejection

Abstract Self-compassion involves treating oneself kindly, acknowledging that all humans experience suffering, and maintaining a balanced awareness of negative thoughts and feelings. Three studies (N = 614) examined the potential role of self-compassion in response to interpersonal rejection. Study 1 recruited a large, diverse internet sample and explored relationships between general perceived acceptance and several outcome variables (affect, depression, self-esteem), testing whether self-compassion moderates these relationships. Similarly, Study 2 tested whether self-compassion moderates the relationships between daily acceptance/rejection and outcome variables. Finally, Study 3 tested whether a self-compassion manipulation effectively promotes coping with rejection. Taken together, results reveal that self-compassion both predicts (Studies 1 and 2) and promotes (Study 3) relatively adaptive responses to rejection. These results suggest that a self-compassionate mindset may lessen the sting of rejection.
Cognitive Functions at Various Types of Neuropsychological Profile Typical for Encephalopathy Patients

Рассмотрены особенности когнитивных функций у больных дисциркуляторной энцефалопатией. Показана взаимосвязь типа нейропсихологического профиля и протекания когнитивных функций, выявлены нейропсихологические факторы и зоны (блоки) головного мозга, которые вовлекаются в патологический процесс при данном заболевании. Ключевые слова: нейропсихологический профиль, когнитивные функции, нейропсихологический фактор. The article considers features of cognitive functions typical for encephalopathy patients. It is shown, how the type of neuropsychological profile influences on cognitive functions and how neuropsychological factors and zones (blocks) of a brain are involved in pathological process at this disorder.
ON THE EVOLUTIONARY DYNAMICS OF VIRULENCE

The aim of these notes is to demonstrate, by way of examples, how the techniques of Adaptive Dynamics can be used to study the evolutionary dynamics of infectious diseases. We focus on evolution of a single phenotypic trait, namely the disease induced death rate, or virulence. In a series of worked-out examples, we introduce the basic notions of Adaptive Dynamics and follow (some of) the development of evolutionary epidemiology through the years. We begin with the so called single infection model, discuss the conventional evolutionary wisdom and the trade-off hypothesis. Later on, we focus on the role of multiple infections in the evolution of infectious diseases. We investigate in more detail a superinfection model and discuss how the details of the superinfection process shape the course of evolution. In the last part of these notes, we introduce an example of a nested model that explicitly links the epidemiological dynamics at the host population level to the dynamics of infection in a single infected host. Such a nested model allows us to derive the precise form of the superinfection probability from the underlying mechanistic submodel of within-host dynamics.