Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensor-topology based simplicial complex reconstruction

Abstract. We propose a new method for the reconstruction of simplicial complexes (combining points, edges and triangles) from 3D point clouds from Mobile Laser Scanning (MLS). Our main goal is to produce a reconstruction of a scene that is adapted to the local geometry of objects. Our method uses the inherent topology of the MLS sensor to define a spatial adjacency relationship between points. We then investigate each possible connexion between adjacent points and filter them by searching collinear structures in the scene, or structures perpendicular to the laser beams. Next, we create triangles for each triplet of self-connected edges. Last, we improve this method with a regularization based on the co-planarity of triangles and collinearity of remaining edges. We compare our results to a naive simplicial complexes reconstruction based on edge length.
Adversarial Structure Matching Loss for Image Segmentation

The per-pixel cross-entropy loss (CEL) has been widely used in structured output prediction tasks as a spatial extension of generic image classification. However, its i.i. d. assumption neglects the structural regularity present in natural images. Various attempts have been made to incorporate structural reasoning mostly through structure priors in a cooperative way where co-occuring patterns are encouraged. We, on the other hand, approach this problem from an opposing angle and propose a new framework for training such structured prediction networks via an adversarial process, in which we train a structure analyzer that provides the supervisory signals, the adversarial structure matching loss (ASML). The structure analyzer is trained to maximize ASML, or to exaggerate recurring structural mistakes usually among co-occurring patterns. On the contrary, the structured output prediction network is trained to reduce those mistakes and is thus enabled to distinguish fine-grained structures. As a result, training structured output prediction networks using ASML reduces contextual confusion among objects and improves boundary localization. We demonstrate that ASML outperforms its counterpart CEL especially in context and boundary aspects on figure-ground segmentation and semantic segmentation tasks with various base architectures, such as FCN, U-Net, DeepLab, and PSPNet.
Systematic In‐Depth Proteomic Analysis of Mitochondria‐Associated Endoplasmic Reticulum Membranes in Mouse and Human Testes

Mitochondria‐associated endoplasmic reticulum membranes (MAMs) regulate important cellular functions including calcium signaling, bioenergetics, and apoptosis during neurodevelopment and carcinogenesis, but its function in male reproduction and spermatogenesis remains enigmatic because the field lacks a complete understanding of the proteome within testis MAMs. To better understand the biological processes and molecular functions of MAM in testes, a global mass spectrometry‐based proteomic evaluation of MAM proteins from human and mouse testes are reported here, respectively. The evaluation and analysis showed that the components of MAM were highly conserved not only between different species (human and mouse) but also between different tissues (testes and brains). Bioinformatics interrogation of these MAM protein catalogues uncovered that 815 new potential linkages specifically existed in mouse testes compared with mouse brains. In addition, a comparative analysis showed that 1347 proteins (account for ≈96.56%) were highly conservatively expressed in both human and mouse testis MAMs. Furthermore, functional analysis revealed that testis‐specific MAM proteins were related to spermatogenesis, male gamete generation, as well as sexual reproduction. The data identified, for the first time, numerous MAM proteins in mouse and human testes, which provide a possibility to define the relationship between testis MAM proteins and reproductive diseases.
Mindfulness-based group therapy: Impact on psychiatrically hospitalized adolescents

The practice of mindfulness has long been incorporated into psychotherapy. Research on the therapeutic benefits of mindfulness exists within adult populations, and emerging empirical evidence demonstrates the benefit of such practices in the treatment of adolescents in both clinical and non-clinical settings. However, there are extremely limited data on the practice of mindfulness with adolescents in a psychiatric hospital. The iMatter (Improve Mindful ATTention, Enhance Relaxation) group is a manualized program developed to provide adolescents on a short-term psychiatric inpatient unit with an opportunity to learn and practice relaxation strategies, mindfulness exercises, and simple yoga poses. Mindfulness skills are taught in the context of the group and include self-observation of thoughts and feelings, breathing exercises, self-validation of one’s experience, loving-kindness toward self, non-judgmental stance toward self, and acceptance and observation of change within self. Participants included 65 adolescents aged 13–17 years (M = 15.06, standard deviation (SD) = 1.34) who took part in at least one session of the iMatter intervention. Improvements in self-reported mood were evident following participation in a mindfulness group. Also, participants’ heart rate significantly decreased following participation in two groups. Future directions include improved integration of mindfulness into the milieu and other unit programming. Furthermore, comparing self-reported mood and physiological measures from this sample to findings obtained for other unit groups will further clarify the impact of the iMatter intervention.
PWM Carrier Displacement in Multi-N-Phase Drives: An Additional Degree of Freedom to Reduce the DC-Link Stress

The paper presents a particular Pulse Width Modulation (PWM) strategy to reduce the (Direct Current) DC-link capacitor stress for multi-n-phase drives. A multi-n-phase drive is composed of multiple independent systems of n inverter supplying a multi-n-phase electric machine. The paper focused on the investigation of the best phase shifting between carriers for a triple-3-phase drive compared to the 3-phase counterpart in order to reduce the capacitor bench design point. Simulation and experimental results show as the control technique proposed is able to reduce the value of the DC-link capacitor current in any operating condition including fault case. In this sense, the PWM carrier displacement appears like an additional degree of freedom that can be exploited in multi-n-phase drives but also in multi-motor application.
Securing Manets with Ticket Granting for Acknowledgement Based Intrusion Detection

: Mobile ad hoc networks (MANETs) can be defined as a collection of large number of mobile nodes that form temporary network. Dynamic nature and limited transmission range of MANETs leads to many security challenges. This itself emphasizes the importance of security and the need for an efficient intrusion detection system in MANETs. IDS is a great research area as the applications of MANETs are increasing into various fields. So, an efficient Intrusion Detection System is to be developed which provides the integrity, confidentiality, non-repudiation and authentication.Enhanced Adaptive Acknowledgment (EAACK) has been developed which consists of three parts, namely, ACK, Secure ACK (S-ACK), and Misbehaviour Report Authentication (MRA). Each module works according to ACK received and so for preserving integrity each ACK is being digitally signed using DSA. The system enhances its security by introducing Ticket Granting System(TGS). TGS approves sender’s request by approving Tickets according to their privilege levels. Movement of each node is monitored by TGS and maintains Black list and White list of nodes. Tickets are granted by checking these lists and three modules of IDS works to find intruders so as to ensure security in the system.
Assessment of Cognitively Stimulating Activity in a Spanish Population

Theoretical models of active ageing and cognitive reserve emphasize the importance of leading an active life to delay age-related cognitive deterioration and maintain good levels of well-being and personal satisfaction in the elderly. The objective of this research was to construct a scale to measure cognitively stimulating activities (CSA) in the Spanish language. The sample consisted of a total of 453 older persons. The scale was constructed from a list of 28 items and validated using structural equation models. The scale obtained showed a negative correlation with age and a positive correlation with education and physical activity. Using hierarchical regression models, CSAs were found to have a significant effect on attention when controlling for the effect of age and education. Likewise, a significant interaction between age and CSA was found on the measure of episodic memory. The validated CSA scale will enable the relationships between changes in cognitive functions and stimulating activities to be studied.
Change-Point Methods on a Sequence of Graphs

Given a finite sequence of graphs, e.g. coming from technological, biological, and social networks, the paper proposes a methodology to identify possible changes in stationarity in the stochastic process that generated such graphs. We consider a general family of attributed graphs for which both topology (vertices and edges) and associated attributes are allowed to change over time, without violating the stationarity hypothesis. Novel Change-Point Methods (CPMs) are proposed that map graphs onto vectors, apply a suitable statistical test in vector space and detect changes –if any– according to a user-defined confidence level; an estimate for the change point is provided as well. In particular, we propose two multivariate CPMs: one designed to detect shifts in the mean, the other to address more complex changes affecting the distribution. We ground our methods on theoretical results that show how the inference in the numerical vector space is related to the one in graph domain, and vice-versa. We also extend the methodology to handle multiple changes occurring in a single sequence. Results show the effectiveness of what proposed in relevant application scenarios.
Development of a robust MRI fiducial system for automated fusion of MR‐US abdominal images

Abstract We present the development of a two‐component magnetic resonance (MR) fiducial system, that is, a fiducial marker device combined with an auto‐segmentation algorithm, designed to be paired with existing ultrasound probe tracking and image fusion technology to automatically fuse MR and ultrasound (US) images. The fiducial device consisted of four ~6.4 mL cylindrical wells filled with 1 g/L copper sulfate solution. The algorithm was designed to automatically segment the device in clinical abdominal MR images. The algorithm's detection rate and repeatability were investigated through a phantom study and in human volunteers. The detection rate was 100% in all phantom and human images. The center‐of‐mass of the fiducial device was robustly identified with maximum variations of 2.9 mm in position and 0.9° in angular orientation. In volunteer images, average differences between algorithm‐measured inter‐marker spacings and actual separation distances were 0.53 ± 0.36 mm. “Proof‐of‐concept” automatic MR‐US fusions were conducted with sets of images from both a phantom and volunteer using a commercial prototype system, which was built based on the above findings. Image fusion accuracy was measured to be within 5 mm for breath‐hold scanning. These results demonstrate the capability of this approach to automatically fuse US and MR images acquired across a wide range of clinical abdominal pulse sequences.
Don't Forget the Quantifiable Relationship between Words: Using Recurrent Neural Network for Short Text Topic Discovery

In our daily life, short texts have been everywhere especially since the emergence of social network. There are countless short texts in online media like twitter, online Q&A sites and so on. Discovering topics is quite valuable in various application domains such as content recommendation and text characterization. Traditional topic models like LDA are widely applied for sorts of tasks, but when it comes to short text scenario, these models may get stuck due to the lack of words. Recently, a popular model named BTM uses word co-occurrence relationship to solve the sparsity problem and is proved effectively. However, both BTM and extended models ignore the inside relationship between words. From our perspectives, more related words should appear in the same topic. Based on this idea, we propose a model named RIBS-TM which makes use of RNN for relationship learning and IDF for filtering high-frequency words. Experiments on two real-world short text datasets show great utility of our model.
ESP : A Statistical Approach to Predicting Application Interference

Independent applications co-scheduled on the same hardware will interfere with one another. Predicting this interference is a key step in scheduling independent applications in large distributed systems. In this paper we study several state-of-the-art regression models for estimating application interference. We find that linear models produce low-accuracy prediction, while interaction (i.e., non-linear) models require so many input features that their usage becomes impractical. We therefore propose ESP, a hybrid regression model that uses linear techniques to select a smaller set of input features, and then uses non-linear techniques to estimate application interference from these features. The result is a highly accurate and practical regression model for application interference estimation. To demonstrate this practicality, we implement ESP and integrate it into a scheduler for both single and multi-node Linux/x86 systems and compare the scheduling performance to state-of-theart heuristics for scheduling to avoid interference. We find that ESP-based schedulers are 1.25-1.8× faster than these heuristics depending on the scheduling scenario. Additionally, we find that ESP’s accurate interference predictions allow schedulers to avoid catastrophic decisions which heuristic approaches fundamentally cannot detect.
BourGAN: Generative Networks with Metric Embeddings

This paper addresses the mode collapse for generative adversarial networks (GANs). We view modes as a geometric structure of data distribution in a metric space. Under this geometric lens, we embed subsamples of the dataset from an arbitrary metric space into the l2 space, while preserving their pairwise distance distribution. Not only does this metric embedding determine the dimensionality of the latent space automatically, it also enables us to construct a mixture of Gaussians to draw latent space random vectors. We use the Gaussian mixture model in tandem with a simple augmentation of the objective function to train GANs. Every major step of our method is supported by theoretical analysis, and our experiments on real and synthetic data confirm that the generator is able to produce samples spreading over most of the modes while avoiding unwanted samples, outperforming several recent GAN variants on a number of metrics and offering new features.
Highly tunable charge and spin transport in silicene junctions: phase transitions and half-metallic states

We report peculiar charge and spin transport properties in S-shaped silicene junctions with the Kane–Mele tight-binding model. In this work, we investigate the effects of electric and exchange fields on the charge and spin transport properties. Our results show that by applying a perpendicular electric field, metal-semiconductor and also semimetal–semiconductor phase transitions occur in our systems. Furthermore, full spin current can be obtained in the structures, so the half-metallic states are observable. Our results enable us to control charge and spin currents and provide new opportunities and applications in silicene-based electronics, optoelectronics, and spintronics.
Video Recovery via Learning Variation and Consistency of Images

Matrix completion algorithms have been popularly used to recover images with missing entries, and they are proved to be very effective. Recent works utilized tensor completion models in video recovery assuming that all video frames are homogeneous and correlated. However, real videos are made up of different episodes or scenes, i.e. heterogeneous. Therefore, a video recovery model which utilizes both video spatiotemporal consistency and variation is necessary. To solve this problem, we propose a new video recovery method Sectional Trace Norm with Variation and Consistency Constraints (STN-VCC). In our model, capped L1-norm regularization is utilized to learn the spatial-temporal consistency and variation between consecutive frames in video clips. Meanwhile, we introduce a new low-rank model to capture the low-rank structure in video frames with a better approximation of rank minimization than traditional trace norm. An efficient optimization algorithm is proposed, and we also provide a proof of convergence in the paper. We evaluate the proposed method via several video recovery tasks and experiment results show that our new method consistently outperforms other related approaches.
Java Subtyping as an Infinite Self-Similar Partial Graph Product

Due to supporting variance annotations, such as wildcard types, the subtyping relation in Java and other generic nominally-typed OO programming languages is both interesting and intricate. In these languages, the subtyping relation between ground object types (ones with no type variables) is the basis for defining the full OO subtyping relation (i.e., that includes type variables). In this paper we present how the subtyping relation between ground Java types can be precisely constructed using a binary operation on directed graphs. The binary operation we use, called a partial Cartesian graph product, is similar in its essence to standard graph products and group products. Its definition is based in particular on that of the standard Cartesian graph product. We believe the use of this graph operation in constructing the ground generic Java subtyping relation reveals some of the not-immediately-obvious structure of the subtyping relation in generic nominally-typed OO programming languages. Accordingly, we believe that describing precisely how this graph operation is used to construct the subtyping relation of these languages, as we do in this paper, may significantly improve our understanding of wildcard types and variance annotations in those languages.
Diversity and community structure of marine microbes around the Benham Rise underwater plateau, northeastern Philippines

Microbes are central to the structuring and functioning of marine ecosystems. Given the remarkable diversity of the ocean microbiome, uncovering marine microbial taxa remains a fundamental challenge in microbial ecology. However, there has been little effort, thus far, to describe the diversity of marine microorganisms in the region of high marine biodiversity around the Philippines. Here, we present data on the taxonomic diversity of bacteria and archaea in Benham Rise, Philippines, Western Pacific Ocean, using 16S V4 rRNA gene sequencing. The major bacterial and archaeal phyla identified in the Benham Rise are Proteobacteria, Cyanobacteria, Actinobacteria, Bacteroidetes, Marinimicrobia, Thaumarchaeota and, Euryarchaeota. The upper mesopelagic layer exhibited greater microbial diversity and richness compared to surface waters. Vertical zonation of the microbial community is evident and may be attributed to physical stratification of the water column acting as a dispersal barrier. Canonical Correspondence Analysis (CCA) recapitulated previously known associations of taxa and physicochemical parameters in the environment, such as the association of oligotrophic clades with low nutrient surface water and deep water clades that have the capacity to oxidize ammonia or nitrite at the upper mesopelagic layer. These findings provide foundational information on the diversity of marine microbes in Philippine waters. Further studies are warranted to gain a more comprehensive picture of microbial diversity within the region.
ART Attrition across Health Facilities Implementing Option B+ in Haiti

Background: Describing factors related to high attrition is important in order to improve the implementation of the Option B+ strategy in Haiti. Methods: We conducted a retrospective cohort study to describe the variability of antiretroviral therapy (ART) retention across health facilities among pregnant and lactating women and assess for differences in ART retention between Option B+ clients and other ART patients. Results: There were 1989 Option B+ clients who initiated ART in 45 health facilities. The percentage of attrition varied from 9% to 81% across the facilities. The largest health facilities had 38% higher risk of attrition (relative risk [RR]: 1.38, 95% confidence interval [CI]: 1.08-1.77, P = .009). Private institutions had 18% less risk of attrition (RR: 0.82, 95% CI: 0.70-0.96, P = .020). Health facilities located in the West department and the South region had lower risk of attrition. Conclusion: Being on treatment in a large or public health facility or a facility located in the North region was a significant risk factor associated with high attrition among Option B+ clients. The implementation of the Option B+ strategy must be reevaluated in order to effectively eliminate mother-to-child HIV transmission.
Facial appearance reflects human familial longevity and cardiovascular disease risk in healthy individuals.

BACKGROUND As facial appearance can be readily quantified and skin tissue easily accessed, they could be valuable tools for determining how biological mechanisms influence tissue degeneration with age and, consequently, human health and lifespan. It is unknown, however, whether appearance reflects disease risk or lifespan independently of factors already known to associate with both health and appearance. METHODS In a cross-sectional study, we compared the amount of skin wrinkling on a sun-protected site (upper inner arm) and the facial appearance of 261 offspring (mean age 63.2 y) of nonagenarian siblings with 253 age-matched controls (mean age 62.7 y), all with no reported disease history. We next examined whether any appearance features that significantly associated with familial longevity also associated with the Framingham cardiovascular disease (CVD) risk score. All analyses were adjusted for chronological age, smoking, photodamage, and body mass index. RESULTS Female and male offspring had reduced upper inner arm skin wrinkling (p = .03 and p < .001, respectively), and the male offspring looked 1.4 y younger than the controls (p = .002). There were no significant associations between CVD risk and upper inner arm skin wrinkling. Women in the lowest quartile of CVD risk looked more than 2 y younger for their age than those in higher risk quartiles (p = .002). Systolic blood pressure was the most significant (p = .004) CVD risk factor that was associated with perceived age in women. CONCLUSIONS Facial appearance and skin wrinkling at a sun-protected site reflect the propensity to reach an extreme old age, and facial appearance reflects the risk of succumbing to CVD independently of chronological age, smoking, photodamage, and BMI.