Miracle-dz/UniFAR-FTUs huggingface dataset:
Placing Caste: Spatialization, Urban Segregation, and Musical Boundary-Making

Abstract:In the face of India's enduring caste inequalities, their entrenchment or resistance is central to social and urban navigation. In this article, I locate music as an important site to investigate how caste comes to be spatialized, where place identity comes to symbolize group difference, with the effect of exacerbating social and spatial segregation. I use ethnographic field notes from attending musical events and 103 interviews with members of two live musical worlds situated in the southern city of Chennai: Carnatic music, which is seen as the preserve of Brahmin, or "upper" caste residents of the city; and Gaana music, which is associated with Dalit, or previously "untouchable" caste urban residents. I argue that for caste elites, symbolic power is constructed and maintained through norms and boundary work, thus producing hegemonic place identity and cultural power. For the caste oppressed, caste discrimination is experienced as stigmatized place identity, which is variously managed, reappropriated, or rejected through a range of strategies. The persistence of place-based stigma to devalue caste identity shapes the experience of caste-based urban inequalities that obstruct marginalized communities' right to the city. In recent years, Gaana musicians are using their music to protest caste-based urban segregation, indexing the rise of a cultural articulation of an anti-caste assertion that challenges the marginalization of urban Dalits. This article advances sociological understandings of the forms that urban segregation can take when layered with caste as an axis of social difference and conceptualizes spatialization of caste as a driver of urban segregation.
Control of Light-Matter Interactions in Classical and Quantum Optics

In this thesis, we examined a series of techniques for controlling the interaction of light with matter that could be employed to optimize or to control physical phenomena in various potential applications. Some of these schemes are described with classical electrodynamic theory while others require a semi-classical or full quantum framework. In Ch.(2), we study numerically two implementations of stretchable photonic crystals (SPCs) embedded in elastic polymers. Our analysis, which classifies the bandgaps in terms of two simply determined parameters, indicates that such structures exhibit bandgaps that can be readily adjusted by straining the polymer. In Ch.(3), we considered a five-level atomic system in a dense gas interacting with two lowintensity fields. By examining the influence of different parameters on the refraction index we found that by adjusting the ratio of the two magnetic amplitudes associated with the fields the effects of atomic coherence can be simply controlled. As well, a negative index of refraction can be achieved and controlled over a wide wavelength range with minimal absorption. In Ch.(4), we theoretically investigated the double Λ scheme inside a Fabry-Pérot cavity. By introducing a weak probe beam and two strong driving fields and employing an incoherent pumping mechanism we found that when the intensity of the two driving fields are equal, a single giant white band was generated. However, when they are not equal, three white bands can be present in one cavity. This procedure can also be employed to displace the center frequency of the white band. In Ch.(5), we studied the interaction of a N-type four-level atom with a single field in the presence of an intensity-dependent coupling in a nonlinear Kerr medium. The exact analytic solution is obtained in the case that the atom and electromagnetic field are initially in a higher excited state and a coherent state, respectively. It is then demonstrated that nonclassical properties such as the degree of entanglement stabilization, Kerr medium nonclassical control, and squeezed light can be
Sense of Belonging and Alumni Engagement

Abstract:Sense of belonging has widespread effects on students’ lives in higher education, particularly on students of color, but less is known if sense of belonging continues to predict important behaviors after graduation. Our study examined if sense of belonging to one’s undergraduate alma mater predicts alumni engagement such as donation and mentoring current students. Participants (100 White alumni and 293 alumni of color) were college graduates recruited through the Prolific online platform. They completed a series of survey questions regarding their undergraduate experience and engagement with their alma mater after graduation. Using step-wise regression models, we found that sense of belonging positively predicted both interests in engagement and self-reported past engagement with their alma mater. This relationship held even when controlling for important demographic factors (e.g., gender, age, race, income) as well as satisfaction with their undergraduate experience. Furthermore, the effect of belonging on alumni engagement was moderated by racial identity such that alumni of color who felt greater belonging were more engaged with their alma mater. In contrast, sense of belonging was not a significant predictor of engagement for White alumni. These findings highlight the importance of understanding and cultivating a sense of belonging to encourage alumni engagement, particularly among students and alumni of color.
Spiking spin-glass models for label propagation and community detection

In this paper we present results from a method of community detection using label propagation in undirected, unweighted graphs which incorporates elements of neural computing and spike-based data. Using a fully connected, edge-weighted system of spiking neurons driven by external currents, we generate spike responses that are decoded into a binary signal. The similarity between pairs of signals is quantified using a Hamming-distance based metric and is used to classify vertices into communities. We test our approach on a set of graph instances, each with 128 vertices and either homogeneous or heterogeneous community size distributions. We present our method as a candidate for a split-computing workflow that incorporates neuromorphic hardware and does not require extensive pre-training of network parameters.
An Ensemble Kalman Filter for the Thermosphere‐Ionosphere

A principal limitation of physics‐based modeling in the Thermosphere‐Ionosphere system is the large uncertainty associated with the implementation of the external forcing of the system, including high‐latitude convection and particle precipitation, solar UV/EUV fluxes, and waves propagating from below. As measuring these quantities with sufficient spatial and temporal resolution is prohibitively costly, a more practical approach to improve model results is to assimilate more readily available measurements of the system. We discuss considerations in implementing an Ensemble Kalman Filter (EnKF) for a strongly forced system versus a chaotic system, overview an EnKF implementation for the strongly forced Coupled Thermosphere, Ionosphere, Plasmasphere, and Electrodynamics model, and present encouraging improvements to neutral density specification obtained by assimilating Challenging Minisatellite Payload (CHAMP) neutral density measurements. The model results show improvement in comparisons with both CHAMP and Gravity Recovery and Climate Experiment (GRACE) measurements during a geomagnetically quiet period at solar minimum.
Interactive mesh cutting with Laplace coordinates and gradient

Sketch-based mesh cutting has been attended by researchers in Computer Graphics, mainly due to its success in dealing with semantic information of mesh with little user interaction. Although most existing approaches have gained great improvement, they depend on the predeﬁned geometric features sensitive to shape and complex mathematical theory, and most methods can not generate a global unique solution for the segmentation problem. In this paper, we propose a novel sketch-based mesh segmentation model with Laplace Coordinates and gradient, which is independent of the complex geometric features and could perceive the diﬀerences of mesh parts. Furthermore, our algorithm is easy to implement, mathematically simple, and a global unique solution can be guaranteed because of our convex quadratic model. Beneﬁting from the Laplace Coordinates and gradient, our method holds an anisotropic behavior and can better ﬁt the cutting boundary. Namely, triangular faces sharing similar attributes are kept closer to each other while big jumps naturally happen on the boundary between mesh parts when our method is applied. A large number of experiments illustrate the enhanced eﬃcacy of our method compared with the state-of-the-art techniques.
Illuminating a Hidden Site: The Recovery of a Sacred Black Landscape

ABSTRACT:This study was provoked by the recent detection of 541 unmarked burials signifying the recovery of the African American Burial Ground (AABG) on the grounds of Woodland Cemetery (WC) at Clemson University, South Carolina. A case study analysis of this coexistent sacred burial ground, initially for enslaved individuals of African descent interred during the pre-emancipation plantation era, seeks answers to fundamental questions: Was Clemson University aware of the AABG’s existence, and why did it take so long to act on preserving this sacred ground, historically known to the local descendant community? This study reveals that Clemson University’s negligence and cultural erasure during the 20th century has in the present day given way to an institutional commitment to truth-telling brought on by student activism and concerns about long-standing social inequities. The recovery of the AABG aligns with and contributes to work on “Black Geographies” and “Black Landscapes” (Woods, 2000; McKittrick & Woods, 2007; Boone, 2020; Hood 2020). The study examines the national reckoning among universities as they consider the legacy of slavery at their institutions and navigate justice (Wilder, 2014; Harris, et al., 2019). This research draws upon the author’s larger study on South Carolina’s landscape legacy and the synthesis of novel typologies beyond the normative classification of cultural landscapes (Padua, 2020). The commemoration of the AABG on campus is explored as a potential “reconciliatory landscape” conceptualized through the lens of “retrospective justice” drawn from the human rights literature (Russell, 2003; Roth, 2004). Status-quo attitudes are questioned as disruption to the norm instigates change and asserts social justice.
Multi-cropping Contrastive Learning and Domain Consistency for Unsupervised Image-to-Image Translation

Recently, unsupervised image-to-image translation methods based on contrastive learning have achieved state-of-the-art results in many tasks. However, in the previous works, the negatives are sampled from the input image itself, which inspires us to design a data augmentation method to improve the quality of the selected negatives. Moreover, the previous methods only preserve the content consistency via patch-wise contrastive learning in the embedding space, which ignores the domain consistency between the generated images and the real images of the target domain. In this paper, we propose a novel unsupervised image-to-image translation framework based on multi-cropping contrastive learning and domain consistency, called MCDUT. Specifically, we obtain the multi-cropping views via the center-cropping and the random-cropping with the aim of further generating the high-quality negative examples. To constrain the embeddings in the deep feature space, we formulate a new domain consistency loss, which encourages the generated images to be close to the real images in the embedding space of the same domain. Furthermore, we present a dual coordinate attention network by embedding positional information into the channel, which called DCA. We employ the DCA network in the design of generator, which makes the generator capture the horizontal and vertical global information of dependency. In many image-to-image translation tasks, our method achieves state-of-the-art results, and the advantages of our method have been proven through extensive comparison experiments and ablation research.
Neural 3D Scene Reconstruction from Multiple 2D Images without 3D Supervision

Neural 3D scene reconstruction methods have achieved impressive performance when reconstructing complex geometry and low-textured regions in indoor scenes. However, these methods heavily rely on 3D data which is costly and time-consuming to obtain in real world. In this paper, we propose a novel neural reconstruction method that reconstructs scenes using sparse depth under the plane constraints without 3D supervision. We introduce a signed distance function field, a color field, and a probability field to represent a scene. We optimize these fields to reconstruct the scene by using differentiable ray marching with accessible 2D images as supervision. We improve the reconstruction quality of complex geometry scene regions with sparse depth obtained by using the geometric constraints. The geometric constraints project 3D points on the surface to similar-looking regions with similar features in different 2D images. We impose the plane constraints to make large planes parallel or vertical to the indoor floor. Both two constraints help reconstruct accurate and smooth geometry structures of the scene. Without 3D supervision, our method achieves competitive performance compared with existing methods that use 3D supervision on the ScanNet dataset.
Effectiveness of Two Intensity Levels of Diode Laser in Debonding Metallic Brackets Regarding Enamel Surface Integrity and Pulpal Temperature: An Ex-Vivo Study

Introduction: The traditional methods of deboning metal brackets exert excessive force, resulting in enamel scratches, fractures, and patient discomfort. The objective of this study was to evaluate the effectiveness of using two intensity levels of a diode laser for debonding metallic orthodontic brackets as an alternative to the conventional debonding method. Materials and methods: Sixty intact, extracted human premolar teeth were used in this study, and metal orthodontic brackets were bonded to the buccal surface of these teeth. The teeth were divided into three groups for the experiment: (1) the control group, where conventional bracket debonding was performed using a debonding plier, (2) the first experimental group, where a diode laser (2.5W, 980nm) was utilized for laser debonding, and (3) the second experimental group, where a diode laser (5W, 980nm) was used for laser debonding. The laser was applied using a sweeping movement for 5 seconds. After debonding, the adhesive remnant index (ARI), the lengths, and the frequency of enamel cracks were compared among the groups. Additionally, an increase in intra-pulpal temperature was measured. Results: In all groups, there were no instances of enamel fractures. Laser debonding resulted in a significant reduction in both the frequency and length of newly formed enamel cracks compared to the conventional debonding method. The laser debonding group exhibited increases in intra-pulpal temperature of 2.37°C and 3.60°C in the second and third groups, respectively. These temperature increases were significantly lower than the threshold of 5.5°C. There were no significant differences observed in the ARI scores among the groups. Conclusion: With all debonding methods, an increase in the length and frequency of enamel cracks should be anticipated. However, laser-assisted debonding of metal brackets offers the advantage of reducing the risk of enamel damage while avoiding thermal damage to the pulp.
Parameter optimization of histogram-based local descriptors for facial expression recognition

An important task in automatic facial expression recognition (FER) is to describe facial image features effectively and efficiently. Facial expression descriptors must be robust to variable scales, illumination changes, face view, and noise. This article studies the application of spatially modified local descriptors to extract robust features for facial expressions recognition. The experiments are carried out in two phases: firstly, we motivate the need for face registration by comparing the extraction of features from registered and non-registered faces, and secondly, four local descriptors (Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Compound Local Binary Patterns (CLBP), and Weber’s Local Descriptor (WLD)) are optimized by finding the best parameter values for their extraction. Our study reveals that face registration is an important step that can improve the recognition rate of FER systems. We also highlight that a suitable parameter selection can increase the performance of existing local descriptors as compared with state-of-the-art approaches.
Advances in the Physiology of Transvascular Exchange and A New Look At Rational Fluid Prescription

Abstract The Starling principle is a model that explains the transvascular distribution of fluids essentially governed by hydrostatic and oncotic forces, which dynamically allow vascular refilling according to the characteristics of the blood vessel. However, careful analysis of fluid physiology has shown that the principle, while correct, is not complete. The revised Starling principle (Michel-Weinbaum model) provides relevant information on fluid kinetics. Special emphasis has been placed on the endothelial glycocalyx, whose subendothelial area allows a restricted oncotic pressure that limits the reabsorption of fluid from the interstitial space, so that transvascular refilling occurs mainly from the lymphatic vessels. The close correlation between pathological states of the endothelium (eg: sepsis, acute inflammation, or chronic kidney disease) and the prescription of fluids forces the physician to understand the dynamics of fluids in the organism; this will allow rational fluid prescriptions. A theory that integrates the physiology of exchange and transvascular refilling is the “microconstant model”, whose variables include dynamic mechanisms that can explain edematous states, management of acute resuscitation, and type of fluids for common clinical conditions. The clinical-physiological integration of the concepts will be the hinges that allow a rational and dynamic prescription of fluids.
DHA Supplementation during Pregnancy in Women with Obesity Normalizes IGF2R Levels in the Placenta of Male Newborns

Introduction Insulin-like growth factor receptor 2 (IGF2R) regulates placental nutrient transport, and its soluble form is related to obesity in adults. If the placental expression of IGF2R is altered in women with obesity is unknown. Whether maternal supplementation with docosahexaenoic acid (DHA), a polyunsaturated fatty acid with anti-inflammatory properties, has a modulatory role in IGF2R's function has not been elucidated. We hypothesized that maternal obesity (Ob) would be associated with alterations in placental IGF2R expression, which may be prevented with DHA supplementation during pregnancy. Methods At delivery, we obtained placentas from women with Ob (BMI ≥ 30 kg/m2, n = 17), Ob supplemented with 800 mg/day of DHA during pregnancy (Ob + DHA, n = 13), and normal-weight women (Nw, BMI ≥ 18.5 ≤ 24.9 kg/m2, n = 14). The IGF2R mRNA and protein were determined by RT-PCR and western blotting, respectively. Moreover, we quantified the gene expression of molecules that modulate the IGF2R function in the extracellular domain, such as TACE/ADAM17, PLAU, and IGF2. Mann–Whitney and Kruskal–Wallis nonparametric tests were used to compare results between two or three groups accordingly. Results The IGF2R levels in the Ob placentas of the male offspring were higher than in the Nw group. The DHA supplementation prevented this effect, suggesting an unknown relationship between IGF2R-Ob-DHA in placental tissues. Conclusion We report, for the first time, that DHA supplementation during pregnancy in women with obesity normalizes the increased IGF2R levels in male placentas, reducing the risk of adverse outcomes related to the IGF2/IGF2R system in male newborns.
Beyond Kuhnian paradigms: Normal science and theory dependence in ecology

Abstract The Structure of Scientific Revolutions by Thomas Kuhn has influenced scientists for decades. It focuses on a progression of science involving periodic, fundamental shifts—revolutions—from one existing paradigm to another. Embedded in this theory is the concept of normal science, that is, scientists work within the confines of established theory, a process often compared to a type of puzzle‐solving. This Kuhnian aspect of scientific research has received little attention relative to the much‐scrutinized concepts of revolutions and paradigms. We use Kuhn's normal science framework to reflect on the way ecologists practice science. This involves a discussion of how theory dependence influences each step of the scientific method, specifically, how past experiences and existing research frameworks guide the way ecologists acquire knowledge. We illustrate these concepts with ecological examples, including food web structure and the biodiversity crisis, emphasizing that the way one views the world influences how that person engages in scientific research. We conclude with a discussion of how Kuhnian ideas inform ecological research at practical levels, such as influences on grant funding allocation, and we make a renewed call for the inclusion of philosophical foundations of ecological principles in pedagogy. By studying the processes and traditions of how science is carried out, ecologists can better direct scientific insight to address the world's most pressing environmental problems.
Home-Based Rehabilitation Following Subtalar Dislocation: A Case Report

The demand for physical medicine and rehabilitation services has risen significantly. Immediate rehabilitation is not always readily available which may compromise patients' functional recovery. Here, we describe a rare subtalar dislocation case and how an unsupervised home-based rehabilitation program allowed functional recovery. A 49-year-old male presented to the emergency department with an injury to the right ankle which resulted from a 3-meter height fall with his foot in plantar flexion and inversion. Clinical and imaging findings confirmed a diagnosis of a rare case of subtalar dislocation. The post-injury AOFAS Ankle-Hindfoot Scale score was 24/100 points. After six weeks of immobilization, a patient-tailored home-based rehabilitation program was prescribed. Adherence to our home-based rehabilitation program was critical to allow a range-of-motion improvement and functional recovery. Delaying rehabilitation may lead to long-term functional impairments. Thus, acknowledging the post-acute period as critical to initiate rehabilitation is mandatory. When outpatient rehabilitation settings are not readily available due to high demand, comprehensive patient education, and home-based rehabilitation programs may constitute effective alternative interventions. We demonstrate the significant improvement obtained with an early patient-tailored home-based rehabilitation program in range-of-motion and functional outcomes in a case of medial subtalar dislocation.
Fitting Straight Lines—The Linear Functional Relationship with Replicated Observations

A medical example suggests the fitting of a linear functional model with replicated observations and inhomogeneous error variances. For a particular error structure relevant to the example being discussed, the paper considers the maximum‐likelihood estimation of the parameters in the model (slope, intercept and error variances). The estimators have no simple explicit form and need to be evaluated by iterative methods. In spite of this, simple closed‐form expressions are obtained for the asymptotic standard errors of the estimators. Some comments are made on alternative error structures which might be relevant in other biological and medical problems similar to the one considered in the paper.