Miracle-dz/UniFAR-FTUs huggingface dataset:
A Landmark-Based Image Registration Model using a Nonlinear Elasticity Smoother for Mapping Mouse Atlas to Gene Expression Data

This paper is devoted to the registration of gene expression data to a neuroanatomical mouse atlas in two dimensions. We use a nonlinear elasticity regularization allowing large and smooth deformations, and overcome the diﬃculty of minimizing the nonlinear elasticity functional by introducing an additional variable v (cid:39) ∇ u , where u is the displacement. Thus, the nonlinearity in the derivatives of the unknown u no longer exists in the obtained Euler-Lagrange equation. Experimental results show that gene expression data are mapped to their corresponding mouse atlas by minimizing a standard L 2 dissimilarity measure along with landmark constraints. We also present comparisons with biharmonic regularization to show that the proposed nonlinear elasticity model needs fewer numerical correction such as regridding, renders smaller dissimilarity in landmark distance and intensity maps, and produces larger mutual information
SMART LOAD - MANAGEMENT FOR EV CHARGING INFRASTRUCTURE IN A RESIDENTIAL COMPLEX

: Interest in and demand for electric vehicles (EVs) is growing strongly due to the increasing awareness of climate change and the respective long-term decarbonisation goals. One of the biggest challenges remains the provision of large-scale, efficient charging infrastructure (IS) in urban areas with intelligent load-management and appropriate pricing schemes. Our results show an increase of yearly load in the building due to E-Mobility by 39%. Nevertheless, given the appropriate pricing incentives, the existing household load maximum is not exceeded and also steering towards electricity consumption at times of high renewable shares is possible. This represents a promising outlook to avoid additional pressure to the distribution grid and promote the implementation of such IS for the further diffusion of EVs.
The Cosmic Microwave Background and Observational Convergence in the m

I use the most recent cosmic microwave background (CMB) anisotropy measurements to constrain the leading cold dark matter models in the Ω m − Ω Λ plane. A narrow triangular region is preferred. This triangle is elongated in such a way that its intersection with even conservative versions of recent supernovae, cluster mass-to-light ratios and double radio source constraints, is small and provides the current best limits in the Ω m − Ω Λ plane: Ω Λ = 0 . 62 ± 0 . 16 and Ω m = 0 . 24 ± 0 . 10. This complementarity between CMB and other observations rules out Ω Λ = 0 models at more than the 99% conﬁdence level.
Dialogue in the EAP classroom: practitioners' and learners' collaborative knowledge-Building

The complex ways in which English for Academic Purposes (EAP) practitioners articulate their professional identity have recently been called into question (Ding & Bruce, 2017; Campion, 2016). In the absence of an extensive range of teacher preparation options specifically tailored to those seeking to enter the profession, EAP instructors often begin teaching on academic literacy or university preparation programmes such as pre-sessionals with a general language teaching qualification such as a DELTA (Martin, 2014). This sometimes presents undue challenges for those entering classrooms targeting English language use in specific academic disciplines where the focus is on English for Specific Academic Purposes (ESAP) in areas such as business, humanities and sciences. Here, the basis upon which language pedagogy is applied in such circumstances would be conditioned by the transfer of knowledge about teaching and learning in general ELT and general academic English (EGAP) classrooms to an ESAP context. However, with prevailing research in EAP still focuses predominantly on academic discourse and the linguistic dimensions of academic publications or the “what” of EAP, far less is known about the “how” of EAP in relation to learners and teaching in the classroom (Todd, 2003).
High Gain DC/DC Converter for Photovoltaic Applications

As far renewable energy is concerned solar photovoltaic means of energy production plays a major role. Since sun’s energy is available in abundant not completely its source is utilized by the photovoltaic cells. So, efficiency of the energy conversion scheme should be high. Hence a high-efficiency dc–dc converter is proposed for low-voltage photovoltaic sources. The proposed converter boosts up the DC voltage with high voltage gain and high efficiency. The proposed converter uses dual active clamping circuit and resonant voltage doubler rectifier. At low voltage side, the voltage stress of power switches is reduced. At high-voltage side, zero-current turn-off of output diodes is achieved.
Risk factors of cerebral microbleeds in young and middle-aged patients with hypertension

Background & Objectives: This study aimed to explore the incidence and potential risk factors of cerebral microbleeds (CMBs) in young and middle-aged patients with hypertension. Methods: We retrospectively analyzed the clinical data of young and middle-aged patients with hypertension in the Department of Neurology, General Hospital of Western Theater Command, Chengdu, China between August 2018 and December 2020. The demographic baseline, laboratory parameters and clinical imaging data were collected. Microbleed anatomical rating scale (MARS) was applied to evaluate the presence, amount, and topographical distributions of CMBs. Results: Among 196 young and middle-aged patients with hypertension, 84 (42.9%) patients had CMBs. CMBs were more likely to occur in the deep brain tissue regions (41.8%), followed by lobar or infratentorial region. White matter hyperintensity (OR, 5.262; 95%CI, 1.314-21.075; P=0.019), abnormal lipid metabolism (OR, 3.832; 95%CI, 1.578-9.306; P=0.003), usage of anti-platelet aggregation drugs (OR, 2.947; 95%CI, 1.138-7.632; P=0.026), smoking history (OR, 3.218; 95%CI, 1.073-9.651; P=0.037), and hyperhomocysteinemia (OR, 1.415; 95%CI, 1.018-1.967; P=0.039) were independently associated with deep or infratentorial CMBs in young and middle-aged patients with hypertension. However, the occurrence of strictly lobar CMBs was only independently associated with abnormal lipid metabolism (OR, 4.162; 95%CI, 1.685-10.282; P=0.002). Conclusions: The rate of CMBs was high in young and middle-aged patients with hypertension, most commonly occurring in the deep brain tissue region. While multiple risk factors were identified to be associated with deep or infratentorial CMBs, the occurrence of strictly lobar CMBs was only associated with abnormal lipid metabolism.
Development of Solar Powered Electric Wheelchair for Physically Challenged Persons

:- Persons with disability face many physical challenges, including nerve and muscular degeneration, reduced motor function and balance, impaired mobility, and so on. This research work proposes a solar power-assisted wheelchair for the differently disabled low income persons living in developing countries like Nigeria. This paper envisages an electric wheelchair that is cost effective, sustainable and efficient in terms of energy consumption. This wheelchair is designed especially for the paraplegics and aged people with an aim to make them more independent. The major components of the model are: the mechanical structure, the solar panels, the DC motor, and the control circuitry. The solar panels serve the dual purposes of providing shelter for the user and also providing the electrical energy that powers the electric motor. A prototype of the proposed model was produced and tested for the design goals
Efﬁcient Streaming Inference for Inﬁnite Latent Feature Models

Bayesian nonparametrics are a classic Bayesian approach to continual learning, in which the non-parametric model grows in representational capacity as more data are observed. One Bayesian nonparametric model, the Indian Buffet Process (IBP), is used when the stream of data is assumed to be generated by an unknown number of binary latent features. However, use of the IBP has been limited by the lack of inference algorithms for the online setting. Here we propose a Bayesian recursion called the Recursive IBP (R-IBP) that efﬁciently ﬁlters a posterior distribution over latent features from a sequence of observations in a streaming manner. R-IBP can be combined with a variety of other models whenever the number of components is unknown, including independent component analysis (ICA), non-negative matrix factorization (NMF), and factor analysis (FA). Our recursive ﬁlter has quasilinear average time complexity and logarithmic average space complexity in the number of observations.
DEPARTMENT OF ECONOMICS DISCUSSION PAPER

Disputes about econometric methodology partly reﬂect a lack of evidence on alternative approaches. We reconsider econometric model selection from a computer-automation perspective, focusing on general-to-speciﬁc reductions, embodied in PcGets . Starting from a general congruent model, standard testing procedures eliminate statistically-insigniﬁcant variables, with diagnostic tests checking the validity of reductions, ensuring a congruent ﬁnal selection. Since jointly selecting and diagnostic testing has eluded theoretical analysis, we study modelling strategies by simulation. The Monte Carlo experiments show that PcGets recovers the DGP speciﬁcation from a general model with size and power close to commencing from the DGP itself.
Model of Optimal Centroids Approach for Multivariate Data Classiﬁcation

—Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and ﬁsh in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classiﬁcation. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classiﬁcation. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes.
KNOWLEDGE SPILLOVERS ACROSS REGIONS

This study leverages a unique research design, a proximate industry peer’s outbound relocation, to examine how knowledge spillover drives the positive relationship between agglomeration and firm innovation. We expect a firm’s innovation performance to decrease when a proximate industry peer moves to another region, because the change in proximity disrupts knowledge spillovers. To evaluate this, we utilize novel data on relocations of nanotechnology R&D firms over 24 years. Contrary to our expectation, we find that a proximate industry peer’s outbound relocation increases a focal firm’s innovation performance. Taking an abductive approach, we investigate the nature of the impact and offer a theoretical explanation for this finding. We argue that a proximate industry peer’s outbound relocation creates an inter-region knowledge channel, which creates opportunities for firms to absorb distant knowledge from the relocated peer’s new region. By showing that exit from a cluster can enhance cluster innovation – under certain circumstances, we provide novel and nuanced insights into cluster dynamics.
The Kaleckian model in the long run: Economies of Scale as a source for Hysteresis

This paper examines the controversy around the Kaleckian model of growth and distribution. We argue that the proposed long-run dynamic adjustment, proposed by Kaleckian scholars, lacks a coherent economic rationale. We provide economic justiﬁcation for the adjustment of the desired rate of utilization towards the actual rate on behalf of a cost-minimizing ﬁrm, after examining the factors that determine the utilization of resources. The cost minimizing ﬁrm has an incentive to increase the utilization of its capital if the rate of the returns to scale decreases as its production increases. We show that there are evidence in the theory and the empirical research that justify this behavior of returns to scale. In that way the desired rate of utilization becomes endogenous.
Improving Graph Clustering with Multi-Granularity Debiased Contrastive Learning

Recently, deep graph clustering achieves sig-001 nificant success by utilizing both the node at-002 tribute features and the graph structure infor-003 mation. However, the existing methods still 004 have some limitations: (1) lack of a flexible 005 mechanism to fuse multi-granularity informa-006 tion learned from different views. (2) introduce 007 the noise positive-negative sample pairs lead 008 to reduced the model performance. To tackle 009 these problems, we propose a debiased con-010 trastive learning framework DCL-MGI, which 011 integrates the multi-granularity information of 012 graph data. Specifically, two contrastive learn-013 ing modules are constructed to capture multi-014 granularity feature information from node-level 015 and graph-level, respectively. Meanwhile, an 016 adaptive strategy of fusing stable graph struc-017 ture information and node representations is 018 proposed to select unbiased contrastive sample 019 pairs, which reduces the false-negative samples. 020 Furthermore, we utilize the temporal entropy 021 metric to evaluate the sample quality under 022 each view and communicate the two indepen-023 dent contrastive learning modules in a collab-024 orative training manner. Experimental results 025 on six real-world datasets demonstrate that our 026 proposed framework enhances state-of-the-art 027 methods on the graph clustering task. 028
A FORMALIZATION OF DARCS PATCH THEORY USING INVERSE SEMIGROUPS

Darcs is a distributed version control system with a ﬂexible repository model. An abstract “theory of patches” determines how changes from multiple sources are combined and transformed while maintaining a consistent history. Previous formalizations of the semantics of Darcs have generally relied on deﬁnitions and assumptions chosen speciﬁcally to model that system. We present a formalization of patch theory which builds upon established mathematical concepts. In particular, we model patch eﬀects as elements of inverse semigroups , which generalize the properties of partial injective functions. We demonstrate that our approach provides a useful framework for discussing and proving results about the operations performed by Darcs. We also give new insight into conﬂictors , which are used to merge incompatible changes and can aﬀect reliability and performance of the software.
What Do Worker Flows Say about the Wage Gains from Unemployment Insurance?

How large are the eﬀects of unemployment insurance on re-employment wages? Search theory holds that UI increases accepted wages by making workers more selective about the jobs they accept. We show that the standard search model puts strong testable restrictions on the magnitude of this selectivity eﬀect, given observed worker ﬂows. A simple formula links the eﬀect of UI on wages to its eﬀect on job-ﬁnding hazard and to the size of frictional wage dispersion. Given the model-implied magnitude of the latter, the implied wage gain from UI cannot be very large. Our own empirical analysis using SIPP shows that, for high-wealth workers, the eﬀects of UI on both duration and wages are close to zero, consistent with the model’s predictions. However, for liquidity-constrained workers, the estimated wage eﬀect of UI is substantially larger than what a standard search model implies given its estimated eﬀect on the job-ﬁnding hazard. We conclude that large estimated wage gains from UI are likely not due to selectivity alone.
Surveillance of Communicable Diseases in Era of Emerging Viral Zoonotic Infections: lessons from H1N1 and MERS-CoV

This escalating emerging disease problem illustrates the unpredictable nature of pathogens that require dynamic and evolving public health strategies for surveillance, disease management and mitigation [16]. This sharp rise of emerging diseases also tests our ability to respond appropriately [8]. Hence, the first step in controlling this growing emerging diseases problem is through prompt recognition and identification by effective and efficient surveillance system [1,17]. Reporting of communicable diseases allows public health officials to describe new diseases and the mode of transmission, so preventive measures can be developed and implemented [17]. Developing effective and efficient surveillance and response/control systems is important for national, the regional and global health security. Furthermore, functioning surveillance systems are necessary for the success of global health initiatives [1].
Promoting the Development of Core Competencies during Early Medical Education: Implementation of a Teaching Assistant Program in Anatomy Course

Competency-based curricula that emphasize personal and professional development have been widely implemented in medical schools. To strengthen competency training during the early years of medical education, we established an anatomy Teaching Assistant (TA) program for raising second-year medical students. The medical student TAs assumed a leadership role in a dissection-based anatomy course for Physician Assistant (PA) students. They worked in the dissection laboratory demonstrating dissection techniques, confirming anatomic structures, and engaging PA students in dialog to facilitate a comprehensive understanding of anatomy. Two questionnaires were conducted at the end of the course: a “TA Self-Reflection Survey” completed by the medical students and a “TA Evaluation Survey” completed by the PA students. Both questionnaires addressed the development of TA core competencies during the course. The survey data indicated that medical student TAs acquired a greater base of knowledge for practice and enhanced interpersonal communication and life-long learning skills, such as the confidence to admit and correct mistakes. The data also indicated that the medical student TAs learned and demonstrated many attributes of professionalism, including commitment to excellence, respect and compassion for learners, timeliness, and inter-professional collaboration. The TAs agreed that they learned essential skills and behaviors that would benefit future patient care. Our findings indicate that anatomy TA programs provide a valuable opportunity for medical students to share basic science knowledge with allied health professionals and acquire a working knowledge of core competencies that are expected of practicing physicians. Therefore, in response to the compelling need for improving physician’s nontechnical skills, postgraduate residency programs in various disciplines have developed courses with an emphasis on nontechnical competency domains [12-16]. One approach, “multidisciplinary team simulation”, has been described as a successful program for teaching both technical and nontechnical skills [17,18]. Unfortunately, literature reviews indicate that the level of evidence supporting the value of this approach is low and the impact of this type of training on patient care has not yet been objectively assessed [19]. Literature review also indicates that nontechnical skill training in surgical residency is marginally effective. The overall strength of the evidence concerning training outcome measures was graded as “moderate” in teamwork, “low” in patient-centered communication, decision making and coping with stress, and “very low” in patient safety and error management [20]. These findings emphasize the crucial importance of early nontechnical core competency training during medical education.
DDDT_A_320119 2843..2855

*These authors contributed equally to this work Purpose: A novel folate receptor-targeted β-cyclodextrin (β-CD) drug delivery vehicle was constructed to improve the bioavailability, biosafety, and drug loading capacity of curcumin. Controlled release and targeted delivery was achieved by modifying the nanoparticles with folic acid (FA). Methods: Folate-conjugated β-CD-polycaprolactone block copolymers were synthesized and characterized. Curcumin-loaded nanoparticles (FA-Cur-NPs) were structured by selfassembly. The physicochemical properties, stability, release behavior and tumor-targeting ability of the fabricated nanoparticles were studied. Results: The average particle size and drug loading of FA-Cur-NPs was 151.8 nm and 20.27%, respectively. Moreover, the FA-Cur-NPs exhibited good stability in vitro for 72 h. The drug release profiles showed that curcumin from FA-Cur-NPs was released significantly faster in a pH 6.4 phosphate buffered solution (PBS) than in pH 7.4, indicating that curcumin can be enriched around the tumor site compared with normal cells. Additionally, the internalization of FA-Cur-NPs was aided by FA receptor-mediated endocytosis, and its cytotoxicity was proportional to the cellular uptake efficiency. Furthermore, in vivo studies confirmed that FA-Cur-NPs exhibited marked accumulation in the tumor site and excellent antitumor activity. Conclusion: These findings suggest that FA-Cur-NPs are a promising approach for improving cancer therapy through active targeting and controllable release.
On the FPGA Implementation of the VR-RLS Algorithms

—This paper presents the main elements proposed for an efficient implementation on Field Programmable Gate Array (FPGA) of our novel Variable-Regularized Recursive Least Squares (VR-RLS) algorithm. The followed performance axes are the overall processing speed and the amount of used hardware resources. We also focus on this adaptive algorithm performance in the scenario of acoustic echo cancellation (AEC), from the finite precision implementation degradation point of view.
Neural Architectures Towards Invariant Representation Learning

The world is complex, ever changing yet structured. Given this complexity, how can an organism living in such a world or even an artificial system distill this information to perceive what is important towards its intention and what is not? What theoretical principles underline such an ability? More interestingly, how do these principles operate in the chaotic randomness and complexities of neural circuits in mammalian brains? These questions form the very heart of the study of representation learning, and also point to perhaps the most interesting directions the field must explore. Indeed, one of the fundamental pursuits of machine learning and artificial intelligence is learning to be invariant to nuisance transformations in the data. Most prior work has focused on addressing these challenges through different aspects of the deep learning pipeline such as loss functions, data augmentation and more recently self-supervision techniques. However, the core architecture or structure of these networks has yet to be adapted to these challenges. In this thesis, we explore how to learn and encode invariance towards nuisance transformations by redesigning the convolution architecture itself leading to more powerful and efficient neural networks. We present two fundamental improvements to neural architecture design through NPTNs (Non-Parametric Transformation Networks) and PRCNs (Permanent Random Connectome Networks). These are designed to be drop-in