Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital capture of the histological microarchitecture in the myometrium and its implications for the propagation of electrophysiological excitation

Coordination of uterine contractions during labour is critical for successful delivery. The mechanisms underlying this coordination are not fully understood. Propagation of contraction signals has previously been observed to occur through transmission of electrical excitation waves. This thesis aims to examine the histological microarchitecture of the muscular layer of the uterus (myometrium) and determine how this structure affects the propagation of excitation by means of in silico three-dimensional reconstruction of the myometrium and numerical simulations of a spatially structured excitation-relaxation model. A key aim of the in silico reconstruction of the smooth muscle architecture of the myometrium is to identify structural features that correspond to the control of excitation behaviour in the myometrium. This examination is aided by analysis of excitation patterns observed in multi-electrode array recordings. The reconstruction is subsequently used as a basis for simulating electrical activity in the myometrium. Novel structural features are identified here that are located at the initiation points of electrical activity and are proposed to be the pacemaker sites in rat myometrium. Furthermore, boundary of low connectivity across the mesometrial border was observed in the rat, which corresponds to the termination of excitation waves observed in multielectrode array recordings. In addition, bridges of smooth muscle cells connecting the inner and outer layers of the myometrium were observed in both rat and human myometrium. Taken together these three features suggest a novel mechanism for control of contraction in the rat myometrium; an analogous mechanism is proposed for the human myometrium. The results presented in this thesis could provide an explanation for the patterns of excitation propagation observed in human and rat uteri. Further refinements of the methods used here are outlined and expected to generate a more detailed visualisation of the structures underpinning these mechanisms.
Flower Recognition using Deep Convolutional Neural Networks

This study investigates the suitable model for flower recognition based on deep Convolutional Neural Networks (CNN) with transfer learning approach. The dataset used in the study is a benchmark dataset from Kaggle. The performance of CNN for plant identification using images of flower are investigated using two popular image classification models: AlexNet and VGG16. Results show that CNN is proven to produce outstanding results for object recognition, but its achievement can still be influenced by the type of images and the number of layers of the CNN architecture. The models produced adequate performance rates, with the VGG16 model achieving the best results. AlexNet and VGG16 models achieved the accuracy of 85.69% and 95.02% respectively. This model can be replicated for flower recognition in other areas, especially in our national heritage, Taman Negara which is among the richest flora ecosystem in the world. The significant feature extraction processes were discussed as well, and this is useful for other types of flowers than the trained dataset.
The Mediating Role of University Industry Collaboration on the Relationship between Human Resource Development, Infrastructure and Performance of Universities in Kenya

There have been calls to universities in developing countries to strengthen their linkages with the industry. These calls have based their premise on enhancing the performance of the universities. Empirical evidence to support the calls has been minimal given that little research has been done in Africa on this phenomenon. This paper adopted a human resource development perspective to explain the role of the phenomenon of University-Industry – Collaboration (U-I-C) in the performance of universities. The study was done in Kenya drawing its respondents from 16 universities both government and private sponsored. Data was obtained from 130 respondents from a broad range of managers in various units of the universities. The results indicate that the Human Resource Development (HRD) Infrastructure for the universities in Kenya is at the level of importance (Mean=3.7;s.d=0.99), U-I-C is at a high level (Mean=3.7; s.d=1.04) and performance is rated at a moderate level (Mean=3.5;s.d=0.99). The results on the test of the two hypotheses of the study indicate that HRD Infrastructure is positively related with university performance and that it explains % of the variation in the university performance. Further, U-I-C partially mediates the relationship between University HRD Infrastructure and Performance. The study concludes that U-I-C is relevant to universities in Kenya since the strength of the relationship between University HRD Infrastructure and performance depends on the phenomenon of U-I-C. While the findings are considered to provide the needed empirical support for the adoption of U-I-C among universities, they raise implications both to the management of universities in Kenya and the multidisciplinary theory supporting HRD practices in organizations.
Promoting Sustainable Cities and Communities for Older Persons through Understanding their Preferable Social Activities

Malaysia is on track to become an aged nation by 2030, with older people accounting for 15% of the total population. Changes in a country’s demographic composition of older people have a significant socioeconomic impact, given that medical and long-term care services account for the majority of government spending. Promoting an active and healthy lifestyle among older people has become critical to their ability to live independently. Social participation among older persons is one way to encourage active ageing. The purpose of this study was to learn more about older people’s preferred social activities to better understand their preferences to promote sustainable cities and communities (SDG 11). A qualitative research approach was used in the study by using a semi-structured interview with a total of 41 older persons. Three states including Penang, Selangor and Kuala Lumpur were selected as the main location to reach the targeted participants in the study. All the older participants were selected based on the inclusion criteria including; (1) older persons aged 60 years and above; (2) older persons who participated in the social activities; (3) activities centres located at three urban areas including Penang, Kuala Lumpur and Selangor. The findings of the study found five (5) types of preferred activities among older persons: (1) Leisure learning activities; (2) Religious activities; (3) Community-related activities; (4) Home-based activities; and (5) Job-related activities. In conclusion, there is an apparent need to provide aged–friendly areas and create a good built environment to assist the older persons, in keeping with the national agenda to achieve aged nation status and achieve the SDGs.
Pengaruh Disiplin Kerja Pegawai Terhadap Pelayanan di Unit Pelaksana Teknis Dinas (UPTD) Pasar Bayongbong Kabupaten Garut

Rendahnya pelayanan di UPTD Pasar Bayongbong Kabupaten Garut di indikasikan karena disiplin kerja pegawai belum sepenuhnya optimal. Tujuan penelitian ini adalah menemukan data dan informasi mengenai besarnya pengaruh disiplin kerja pegawai terhadap pelayanan di UPTD Pasar Bayongbong Kabupaten Garut. Metode yang digunakan dalam penelitian ini adalah kausalitas dengan teknik pengumpulan data kuesioner yang disebarkan kepada 43 responden. Berdasarkan analisis statistik, terbukti adanya hubungan yang kuat antara disiplin kerja pegawai terhadap pelayanan di UPTD Pasar Bayongbong Kabupaten Garut. Hasil pengujian hipotesis menunjukkan bahwa terdapat pengaruh antara disiplin kerja pegawai terhadap pelayanan di UPTD Pasar Bayongbong Kabupaten Garut.
Greedy optimization of the geometry of Majorana Josephson junctions

Josephson junctions in a two-dimensional electron gas with spin-orbit coupling are a promising candidate to realize topological superconductivity. While it is known that the geometry of the junction strongly influences the size of the topological gap, the question of how to construct optimal geometries remains unexplored. We introduce a greedy numerical algorithm to optimize the shape of Majorana junctions. The core of the algorithm relies on perturbation theory and is embarrassingly parallel, which allows it to explore the design space efficiently. By introducing stochastic variations in the junction Hamiltonian, we avoid overfitting geometries to specific system parameters. Furthermore, we constrain the optimizer to produce smooth geometries by applying image filtering and fabrication resolution constraints. We run the algorithm in various setups and find that it reliably produces geometries with increased topological gaps over large parameter ranges. The results are robust to variations in the optimization starting point and the presence of disorder, which suggests the optimizer is capable of finding global maxima.
Towards Robust Unsupervised Disentanglement of Sequential Data - A Case Study Using Music Audio

Disentangled sequential autoencoders (DSAEs) represent a class of probabilistic graphical models that describes an observed sequence with dynamic latent variables and a static latent variable. The former encode information at a frame rate identical to the observation, while the latter globally governs the entire sequence. This introduces an inductive bias and facilitates unsupervised disentanglement of the underlying local and global factors. In this paper, we show that the vanilla DSAE suffers from being sensitive to the choice of model architecture and capacity of the dynamic latent variables, and is prone to collapse the static latent variable. As a countermeasure, we propose TS-DSAE, a two-stage training framework that first learns sequence-level prior distributions, which are subsequently employed to regularise the model and facilitate auxiliary objectives to promote disentanglement. The proposed framework is fully unsupervised and robust against the global factor collapse problem across a wide range of model configurations. It also avoids typical solutions such as adversarial training which usually involves laborious parameter tuning, and domain-specific data augmentation. We conduct quantitative and qualitative evaluations to demonstrate its robustness in terms of disentanglement on both artificial and real-world music audio datasets.
View Synthesis with Sculpted Neural Points

We address the task of view synthesis, generating novel views of a scene given a set of images as input. In many recent works such as NeRF (Mildenhall et al., 2020), the scene geometry is parameterized using neural implicit representations (i.e., MLPs). Implicit neural representations have achieved impressive visual quality but have drawbacks in computational efficiency. In this work, we propose a new approach that performs view synthesis using point clouds. It is the first point-based method that achieves better visual quality than NeRF while being 100x faster in rendering speed. Our approach builds on existing works on differentiable point-based rendering but introduces a novel technique we call"Sculpted Neural Points (SNP)", which significantly improves the robustness to errors and holes in the reconstructed point cloud. We further propose to use view-dependent point features based on spherical harmonics to capture non-Lambertian surfaces, and new designs in the point-based rendering pipeline that further boost the performance. Finally, we show that our system supports fine-grained scene editing. Code is available at https://github.com/princeton-vl/SNP.
Neutrino mass and kinetic gravity braiding degeneracies

Modified theories of gravity yield an effective dark energy in the background dynamics that achieves an accelerated expansion of the universe. In addition, they present a fifth force that induces gravitational signatures in structure formation, and therefore in the matter power spectrum and related statistics. On the other hand, massive neutrinos suppress the power spectrum at scales that also modified gravity enhances it, so a degeneration of these effects has been recognized for some gravity models. In the present work, we study both effects using kinetic gravity braiding (nKGB) models to find that in spite of some degeneracies, the role of the fifth force at very large scales imprints a bump in the matter power spectrum as a distinctive signature of this model and, therefore, acts as a smoking gun that seems difficult to match within the present knowledge of power spectra. These models result interesting, however, since the n = 1 presents no H 0 tension, and all nKGB studied here present no σ 8 tension and, in addition, a null neutrino mass is excluded.
Exploiting symmetry in variational quantum machine learning

Variational quantum machine learning is an extensively studied application of near-term quantum computers. The success of variational quantum learning models crucially depends on finding a suitable parametrization of the model that encodes an inductive bias relevant to the learning task. However, precious little is known about guiding principles for the construction of suitable parametrizations. In this work, we holistically explore when and how symmetries of the learning problem can be exploited to construct quantum learning models with outcomes invariant under the symmetry of the learning task. Building on tools from representation theory, we show how a standard gateset can be transformed into an equivariant gateset that respects the symmetries of the problem at hand through a process of gate symmetrization. We benchmark the proposed methods on two toy problems that feature a non-trivial symmetry and observe a substantial increase in generalization performance. As our tools can also be applied in a straightforward way to other variational problems with symmetric structure, we show how equivariant gatesets can be used in variational quantum eigensolvers.
Lifting the Curse of Multilinguality by Pre-training Modular Transformers

Multilingual pre-trained models are known to suffer from the curse of multilinguality, which causes per-language performance to drop as they cover more languages. We address this issue by introducing language-specific modules, which allows us to grow the total capacity of the model, while keeping the total number of trainable parameters per language constant. In contrast with prior work that learns language-specific components post-hoc, we pre-train the modules of our Cross-lingual Modular (X-Mod) models from the start. Our experiments on natural language inference, named entity recognition and question answering show that our approach not only mitigates the negative interference between languages, but also enables positive transfer, resulting in improved monolingual and cross-lingual performance. Furthermore, our approach enables adding languages post-hoc with no measurable drop in performance, no longer limiting the model usage to the set of pre-trained languages.
Stochastic first-order methods for average-reward Markov decision processes

We study the problem of average-reward Markov decision processes (AMDPs) and develop novel ﬁrst-order methods with strong theoretical guarantees for both policy evaluation and optimization. Existing on-policy evaluation methods suﬀer from sub-optimal convergence rates as well as failure in handling insuﬃciently random policies, e.g., deterministic policies, for lack of exploration. To remedy these issues, we develop a novel variance-reduced temporal diﬀerence (VRTD) method with linear function approximation for randomized policies along with optimal convergence guarantees, and an exploratory variance-reduced temporal diﬀerence (EVRTD) method for insuﬃciently random policies with comparable convergence guarantees. We further establish linear convergence rate on the bias of policy evaluation, which is essential for improving the overall sample complexity of policy optimization. On the other hand, compared with intensive research interest in ﬁnite sample analysis of policy gradient methods for discounted MDPs, existing studies on policy gradient methods for AMDPs mostly focus on regret bounds under restrictive assumptions on the underlying Markov processes (see, e.g., Abbasi-Yadkori et al., 2019), and they often lack guarantees on the overall sample complexities. Towards this end, we develop an average-reward variant of the stochastic policy mirror descent (SPMD) (Lan, 2022). We establish the ﬁrst (cid:101) O ( (cid:15) − 2 ) sample complexity for solving AMDPs with policy gradient method under both the generative model (with unichain assumption) and Markovian noise model (with ergodic assumption). This bound can be further improved to (cid:101) O ( (cid:15) − 1 ) for solving regularized AMDPs. Our theoretical advantages are corroborated by numerical experiments.
SKYSURF: Constraints on Zodiacal Light and Extragalactic Background Light through Panchromatic HST All-sky Surface-brightness Measurements. I. Survey Overview and Methods

We give an overview and describe the rationale, methods, and testing of the Hubble Space Telescope (HST) Archival Legacy project “SKYSURF. ” SKYSURF uses HST’s unique capability as an absolute photometer to measure the ∼0.2–1.7 μm sky-surface brightness (sky-SB) from 249,861 WFPC2, ACS, and WFC3 exposures in ∼1400 independent HST fields. SKYSURF’s panchromatic data set is designed to constrain the discrete and diffuse UV to near-IR sky components: Zodiacal Light (ZL), Kuiper Belt Objects (KBOs), Diffuse Galactic Light (DGL), and the discrete plus diffuse Extragalactic Background Light (EBL). We outline SKYSURF’s methods to: (1) measure sky-SB levels between detected objects; (2) measure the discrete EBL, most of which comes from AB≃17–22 mag galaxies; and (3) estimate how much truly diffuse light may exist. Simulations of HST WFC3/IR images with known sky values and gradients, realistic cosmic ray (CR) distributions, and star plus galaxy counts were processed with nine different algorithms to measure the “Lowest Estimated Sky-SB” (LES) in each image between the discrete objects. The best algorithms recover the LES values within 0.2% when there are no image gradients, and within 0.2%–0.4% when there are 5%–10% gradients. We provide a proof of concept of our methods from the WFC3/IR F125W images, where any residual diffuse light that HST sees in excess of zodiacal model predictions does not depend on the total object flux that each image contains. This enables us to present our first SKYSURF results on diffuse light in Carleton et al.
H1-stability of an L2-type method on general nonuniform meshes for subdiffusion equation

In this work the H 1 -stability of an L2 method on general nonuniform meshes is established for the subdiﬀusion equation. Under some mild con- straints on the time step ratio ρ k , for example 0 . 4573328 ≤ ρ k ≤ 3 . 5615528 for all k ≥ 2, a crucial bilinear form associated with the L2 fractional-derivative op- erator is proved to be positive semideﬁnite. As a consequence, the H 1 -stability of L2 schemes can be derived for the subdiﬀusion equation. In the special case of graded mesh, such positive semideﬁniteness holds when the grading parameter 1 < r ≤ 3 . 2016538 and therefore the H 1 -stability of L2 schemes holds. To the best of our knowledge, this is the ﬁrst work on the H 1 -stability of L2 method on general nonuniform meshes for subdiﬀusion equation.
Visualization Guidelines for Model Performance Communication Between Data Scientists and Subject Matter Experts

— Presenting the complexities of a model’s performance is a communication bottleneck that threatens collaborations between data scientists and subject matter experts. Accuracy and error metrics alone fail to tell the whole story of a model – its risks, strengths, and limitations – making it difﬁcult for subject matter experts to feel conﬁdent in deciding to use a model. As a result, models may fail innexpected ways if their weaknesses are not clearly understood. Alternatively, models may go unused, as subject matter experts disregard poorly presented models in favor of familiar, yet arguably substandard methods. In this paper, we propose effective use of visualization as a medium for communication between data scientists and subject matter experts. Our research addresses the gap between common practices in model performance communication and the understanding of subject matter experts and decision makers. We derive a set of communication guidelines and recommended visualizations for communicating model performance based on interviews of both data scientists and subject matter experts at the same organization. We conduct a follow-up study with subject matter experts to evaluate the efﬁcacy of our guidelines in presentations of model performance with and without our recommendations. We ﬁnd that our proposed guidelines made subject matter experts more aware of the tradeoffs of the presented model. Participants realized that current communication methods left them without a robust understanding of the model’s performance, potentially giving them misplaced conﬁdence in the use of the model.
Beyond Barker: Infant Mortality at Birth and Ischaemic Heart Disease in Older Age

Adverse conditions in early life can have consequential impacts on individuals' health in older age. In one of the first papers on this topic, Barker and Osmond 1986 show a strong positive relationship between infant mortality rates in the 1920s and ischaemic heart disease in the 1970s. We go 'beyond Barker', first by showing that this relationship is robust to the inclusion of local geographic area fixed effects, but not family fixed effects. Second, we explore whether the average effects conceal underlying heterogeneity: we examine if the infant mortality effect offsets or reinforces one's genetic predisposition for heart disease. We find considerable heterogeneity that is robust to within-area as well as within-family analyses. Our findings show that the effects of one's early life environments mainly affect individuals with the highest genetic risk for developing heart disease. Put differently, in areas with the lowest infant mortality rates, the effect of one's genetic predisposition effectively vanishes. These findings suggest that advantageous environments can cushion one's genetic risk of developing heart disease.
Symmetry of surfaces for linear fractional group

We will compute the stable upper genus for the family of finite non-abelian simple groups $PSL_2(\mathbb{F}_p)$ for $p \equiv 3~(mod~4)$. This classification is well-grounded in the other branches of Mathematics like topology, smooth, and conformal geometry, algebraic categories.