Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep-3DAligner: Unsupervised 3D Point Set Registration Network With Optimizable Latent Vector

Point cloud registration is the process of aligning a pair of point sets via searching for a geometric transformation. Unlike classical optimization-based methods, recent learning-based methods leverage the power of deep learning for registering a pair of point sets. In this paper, we propose to develop a novel model that organically integrates the optimization to learning, aiming to address the technical challenges in 3D registration. More specifically, in addition to the deep transformation decoding network, our framework introduce an optimizable deep \underline{S}patial \underline{C}orrelation \underline{R}epresentation (SCR) feature. The SCR feature and weights of the transformation decoder network are jointly updated towards the minimization of an unsupervised alignment loss. We further propose an adaptive Chamfer loss for aligning partial shapes. To verify the performance of our proposed method, we conducted extensive experiments on the ModelNet40 dataset. The results demonstrate that our method achieves significantly better performance than the previous state-of-the-art approaches in the full/partial point set registration task.
The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications

While deep machine learning technologies are now pervasive in state-of-the-art image recognition and natural language processing applications, only in recent years have these technologies started to sufficiently mature in applications related to wireless communications. In particular, recent research has shown deep machine learning to be an enabling technology for cognitive radio applications as well as a useful tool for supplementing expertly defined algorithms for spectrum sensing applications such as signal detection, estimation, and classification (termed here as Radio Frequency Machine Learning, or RFML). A major driver for the usage of deep machine learning in the context of wireless communications is that little, to no, a priori knowledge of the intended spectral environment is required, given that there is an abundance of representative data to facilitate training and evaluation. However, in addition to this fundamental need for sufficient data, there are other key considerations, such as trust, security, and hardware/software issues, that must be taken into account before deploying deep machine learning systems in real-world wireless communication applications. This paper provides an overview and survey of prior work related to these major research considerations. In particular, we present their unique considerations in the RFML application space, which are not generally present in the image, audio, and/or text application spaces.
Understanding the Role of Momentum in Non-Convex Optimization: Practical Insights from a Lyapunov Analysis

Momentum methods are now used pervasively within the machine learning community for training non-convex models such as deep neural networks. Empirically, they out perform traditional stochastic gradient descent (SGD) approaches. In this work we develop a Lyapunov analysis of SGD with momentum (SGD+M), by utilizing a equivalent rewriting of the method known as the stochastic primal averaging (SPA) form. This analysis is much tighter than previous theory in the non-convex case, and due to this we are able to give precise insights into when SGD+M may out-perform SGD, and what hyper-parameter schedules will work and why.
Analyzing Koopman approaches to physics-informed machine learning for long-term sea-surface temperature forecasting

Accurately predicting sea-surface temperature weeks to months into the future is an important step toward long term weather forecasting. Standard atmosphere-ocean coupled numerical models provide accurate sea-surface forecasts on the scale of a few days to a few weeks, but many important weather systems require greater foresight. In this paper we propose machine-learning approaches sea-surface temperature forecasting that are accurate on the scale of dozens of weeks. Our approach is based in Koopman operator theory, a useful tool for dynamical systems modelling. With this approach, we predict sea surface temperature in the Gulf of Mexico up to 180 days into the future based on a present image of thermal conditions and three years of historical training data. We evaluate the combination of a basic Koopman method with a convolutional autoencoder, and a newly proposed "consistent Koopman" method, in various permutations. We show that the Koopman approach consistently outperforms baselines, and we discuss the utility of our additional assumptions and methods in this sea-surface temperature domain.
Onset of Coronary Heart Disease is Associated with HCMV Infection and Increased CD14CD16 Monocytes in a Population of Weifang, China*

Objective To investigate the relationship between human cytomegalovirus (HCMV) infection and peripheral blood CD14CD16 monocytes in the pathogenesis of coronary heart disease (CHD), and to elucidate the mechanism of pathogenesis in CHD by analyzing the correlation between infection, inflammation, and CHD, to provide a basis for the prevention, evaluation, and treatment of the disease. Methods In total, 192 patients with CHD were divided into three groups: latent CHD, angina pectoris, and myocardial infarction. HCMV-IgM and -IgG antibodies were assessed using ELISA; CD14CD16 monocytes were counted using a five-type automated hematology analyzer; mononuclear cells were assessed using fluorescence-activated cell sorting; and an automatic biochemical analyzer was used to measure the levels of triglyceride, cholesterol, highand low-density lipoprotein cholesterols, lipoprotein, hs-CRp and Hcy. Results The positive rates of HCMV-IgM and -IgG were significantly higher in the CHD groups than in the control group. HCMV infection affects lipid metabolism to promote immune and inflammatory responses. Conclusion HCMV infection has a specific correlation with the occurrence and development of CHD. The expression of CD14CD16 mononuclear cells in the CHD group was increased accordingly and correlated with acute HCMV infection. Thus, HCMV antibody as well as peripheral blood CD14CD16 mononuclear cells can be used to monitor the occurrence and development of CHD.
Molecular characterization of pediatric spindle cell tumors

Congenital fibrosarcoma (CFS) is a cellular, mitotically active neoplasm of soft tissues. It affects infants less than two years of age, has a low metastatic rate and a relatively high propensity for local recurrence. One of the predominant clinical issues surrounding CFS is its distinction from other histologically identical and virtually indistinguishable pediatric spindle cell tumors including adult-type fibrosarcoma (ATFS) and infantile fibromatosis (IFB). ATFS is a malignant lesion that is treated more aggressively than CFS, while IFB is a benign lesion which is treated less aggressively. Reliable distinction between these entities is therefore clinically very important. We therefore wanted to identify a diagnostic tool to distinguish CFS from other fibroblastic tumors such as ATFS and IFB. Cytogenetic analysis of CFS cases has shown a nonrandom gain in chromosomes 8, 11, 17, and 20 with trisomy for chromosome 11 being present in most cases. Cytogeneticists at the Department of Pathology of B.C.C.H. recently identified recurrent cytogenetic alterations involving chromosome 12pl3 and 15q25 in three CFS cases, which were not present in ATFS, IFB, and aggressive fibromatosis. Cloning of the chromosomal breakpoints revealed a novel fusion between the ETS transcription factor member, ETV6, and the gene encoding the neurotrophin-3 cell surface receptor, NTRK3. This fusion results in the juxtaposition of the H L H dimerization domain of ETV6 to the protein tyrosine kinase (PTK) domain of NTRK3. We hypothesized that this molecule acts as an aberrant PTK signaling molecule i n which the H L H domain mediates ligand independent dimerization resulting i n
PDFlib PLOP: PDF Linearization, Optimization, Protection Page inserted by evaluation version

Summary The obesity epidemic affects all, including women of reproductive age. One in ﬁve women attending prenatal care in the UK is obese. Prepregnancy obesity is associated with serious short-and long-term complications for mother and child. Furthermore, gestational weight gain (GWG) of obese pregnant women generally exceeds the Institute of Medicine recommended ranges. This observation can partially be explained by an unbalanced diet and lack of daily physical activity. Despite this, few lifestyle intervention trials in obese pregnant women are available. Two out of seven intervention trials focusing on GWG, nutrition and physical activity, reached a signiﬁcant decrease in GWG. Developing guidelines to promote appropriated weight gain and healthy lifestyle in overweight and obese pregnant women remains a challenge. This review aims to summarize the complications associated with maternal prepregnancy overweight and obesity and to discuss possible strategies to improve the lifestyle habits of pregnant women.
Emergence of a finite-size-scaling function in the supervised learning of the Ising phase transition

We investigate the connection between the supervised learning of the binary phase classification in the ferromagnetic Ising model and the standard finite-size-scaling theory of the second-order phase transition. Proposing a minimal one-free-parameter neural network model, we analytically formulate the supervised learning problem for the canonical ensemble being used as a training data set. We show that just one free parameter is capable enough to describe the data-driven emergence of the universal finite-size-scaling function in the network output that is observed in a large neural network, theoretically validating its critical point prediction for unseen test data from different underlying lattices yet in the same universality class of the Ising criticality. We also numerically demonstrate the interpretation with the proposed one-parameter model by providing an example of finding a critical point with the learning of the Landau mean-field free energy being applied to the real data set from the uncorrelated random scale-free graph with a large degree exponent.
Distribusi serotipe dan pola resistensi antibiotika dari isolat Salmonella nontifoid di Jakarta

Salmonellosis is an important medical problem worldwide. Although infection with nontyphoid Salmonella often causes mild self-limited illness, serious sequelae including death may occur, especially in immunocompromised hosts. A study to detect nontyphoid Salmonella species in diarrheal patients was conducted involving 1810 rectal swab samples examined from February 2002 through August 2004. A number of 135 (7.5%) samples were positive for Salmonella. Salmonella ser. Typhimurium and Salmonella ser. Enteritidis were found most frequent among the patients with an isolation rate of 29.6% and 23.1%, respectively. Antimicrobial susceptibility test which included 8 antibiotics showed low (4%) to moderate (39%) number of the microorganisms resistant to nalidixic acid, ampicillin, trimethoprim-sulfamethoxazole, tetracycline and chloramphenicol. Although the majority of Salmonella isolates were still susceptible to ciprofloxacin and norfloxacin, the emergence of a small number of resistance of Salmonella ser. Typhimurium resistant to norfloxacin (1.0%) and to ceftriaxone (9.0%) was noted. In conclusion of this study showed that Salmonella spp. is the frequent cause of diarrheal disease.
Exoplanets as New Sub-GeV Dark Matter Detectors

We present exoplanets as new targets to discover Dark Matter (DM), with advantages due to their large expected abundance, low temperatures, and large sizes. Throughout the Milky Way, DM can scatter, become captured, deposit annihilation energy, and increase the heat flow within exoplanets. We estimate upcoming infrared telescope sensitivity to this scenario, finding actionable discovery or exclusion searches. We find that DM with masses above about an MeV can be probed with exoplanets, with DM-proton and DM-electron scattering cross sections down to about $10^{-37}$cm$^2$, stronger than existing limits by up to six orders of magnitude. Supporting evidence of a DM origin can be identified through DM-induced exoplanet heating correlated with Galactic position, and hence DM density. This also allows a potential tracer of DM overdensities. Our results provide new motivation to measure the temperature of the billions of brown dwarfs, rogue planets, and gas giants peppered throughout our Galaxy.
EigenGame: PCA as a Nash Equilibrium

We present a novel view on principal component analysis (PCA) as a competitive game in which each approximate eigenvector is controlled by a player whose goal is to maximize their own utility function. We analyze the properties of this PCA game and the behavior of its gradient based updates. The resulting algorithm which combines elements from Oja's rule with a generalized Gram-Schmidt orthogonalization is naturally decentralized and hence parallelizable through message passing. We demonstrate the scalability of the algorithm with experiments on large image datasets and neural network activations. We discuss how this new view of PCA as a differentiable game can lead to further algorithmic developments and insights.
Approximation of the invariant distribution for a class of ergodic SPDEs using an explicit tamed exponential Euler scheme

We consider the long-time behavior of an explicit tamed exponential Euler scheme applied to a class of parabolic semilinear stochastic partial differential equations driven by additive noise, under a one-sided Lipschitz continuity condition. The setting encompasses nonlinearities with polynomial growth. First, we prove that moment bounds for the numerical scheme hold, with at most polynomial dependence with respect to the time horizon. Second, we apply this result to obtain error estimates, in the weak sense, in terms of the time-step size and of the time horizon, to quantify the error to approximate averages with respect to the invariant distribution of the continuous-time process. We justify the efficiency of using the explicit tamed exponential Euler scheme to approximate the invariant distribution, since the computational cost does not suffer from the at most polynomial growth of the moment bounds. To the best of our knowledge, this is the first result in the literature concerning the approximation of the invariant distribution for SPDEs with non-globally Lipschitz coefficients using an explicit tamed scheme.
Bringing Network Coding into SDN: A Case-study for Highly Meshed Heterogeneous Communications

Modern communications have moved away from point-to-point models to increasingly heterogeneous network models. In this article, we propose a novel controller-based protocol to deploy adaptive causal network coding in heterogeneous and highly-meshed communication networks. Specifically, we consider using Software-Defined-Network (SDN) as the main controller. We first present an architecture for the highly-meshed heterogeneous multi-source multi-destination networks that represents the practical communication networks encountered in the fifth generation of wireless networks (5G) and beyond. Next, we present a promising solution to deploy network coding over the new architecture. In fact, we investigate how to generalize adaptive and causal random linear network coding (AC-RLNC), proposed for multipath multi-hop (MP-MH) communication channels, to a protocol for the new multi-source multi-destination network architecture using controller. To this end, we present a modularized implementation of AC-RLNC solution where the modules work together in a distributed fashion and perform the AC-RLNC technology. We also present a new controller-based setting through which the network coding modules can communicate and can attain their required information. Finally, we briefly discuss how the proposed architecture and network coding solution provide a good opportunity for future technologies, e.g., distributed coded computation and storage, mmWave communication environments, and innovative and efficient security features.
On the Compression of Translation Operator Tensors in FMM-FFT-Accelerated SIE Simulators via Tensor Decompositions

Tensor decomposition methodologies are proposed to reduce the memory requirement of translation operator tensors arising in the fast multipole method-fast Fourier transform (FMM-FFT)-accelerated surface integral equation (SIE) simulators. These methodologies leverage Tucker, hierarchical Tucker (H-Tucker), and tensor train (TT) decompositions to compress the FFT’ed translation operator tensors stored in 3-D and 4-D array formats. Extensive numerical tests are performed to demonstrate the memory saving achieved by and computational overhead introduced by these methodologies for different simulation parameters. The numerical results show that the H-Tucker-based methodology for 4-D array format yields the maximum memory saving, while Tucker-based methodology for 3-D array format introduces the minimum computational overhead. For many practical scenarios, all methodologies yield a significant reduction in the memory requirement of translation operator tensors while imposing negligible/acceptable computational overhead.
Population density and performance of Melasoma lapponica (Coleoptera : Chrysomelidae) in surroundings of smelter complex

Environ. Entomol. 24(3) : 707-715 (1995) We studied populations of the willow feeding leaf beetle, Melasoma lapponica L. , in the vicinity of the Severonikel smelter complex at the Kola Peninsula, northwest Russia. Increased abundance of M. lapponica around the smelter is explained by habitat changes that have resulted in higher density of the most preferred host plant, Salix borealis (Fries) Nasar. Within the polluted territory, 10- to 20-fold higher densities occurred in moderately polluted sites than both in heavily and slightly polluted areas. Laboratory experiments demonstrated the highest survival of insects fed with leaves of S. borealis from the moderately polluted site, whereas female life span, fecundity, and egg viability decreased toward the smelter. Host-plant quality together with parasite-induced mortality explained the density pattern observed along pollution gradients.
Computing Graph Neural Networks: A Survey from Algorithms to Accelerators

Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to their capability to model and learn from graph-structured data. Such an ability has strong implications in a wide variety of fields whose data are inherently relational, for which conventional neural networks do not perform well. Indeed, as recent reviews can attest, research in the area of GNNs has grown rapidly and has lead to the development of a variety of GNN algorithm variants as well as to the exploration of ground-breaking applications in chemistry, neurology, electronics, or communication networks, among others. At the current stage research, however, the efficient processing of GNNs is still an open challenge for several reasons. Besides of their novelty, GNNs are hard to compute due to their dependence on the input graph, their combination of dense and very sparse operations, or the need to scale to huge graphs in some applications. In this context, this article aims to make two main contributions. On the one hand, a review of the field of GNNs is presented from the perspective of computing. This includes a brief tutorial on the GNN fundamentals, an overview of the evolution of the field in the last decade, and a summary of operations carried out in the multiple phases of different GNN algorithm variants. On the other hand, an in-depth analysis of current software and hardware acceleration schemes is provided, from which a hardware-software, graph-aware, and communication-centric vision for GNN accelerators is distilled.
How LSTM Encodes Syntax: Exploring Context Vectors and Semi-Quantization on Natural Text

Long Short-Term Memory recurrent neural network (LSTM) is widely used and known to capture informative long-term syntactic dependencies. However, how such information are reflected in its internal vectors for natural text has not yet been sufficiently investigated. We analyze them by learning a language model where syntactic structures are implicitly given. We empirically show that the context update vectors, i.e. outputs of internal gates, are approximately quantized to binary or ternary values to help the language model to count the depth of nesting accurately, as Suzgun et al. (2019) recently show for synthetic Dyck languages. For some dimensions in the context vector, we show that their activations are highly correlated with the depth of phrase structures, such as VP and NP. Moreover, with an L1 regularization, we also found that it can accurately predict whether a word is inside a phrase structure or not from a small number of components of the context vector. Even for the case of learning from raw text, context vectors are shown to still correlate well with the phrase structures. Finally, we show that natural clusters of the functional words and the part of speeches that trigger phrases are represented in a small but principal subspace of the context-update vector of LSTM.
Evaluating Multilingual BERT for Estonian

Recently, large pre-trained language models, such as BERT, have reached state-of-the-art performance in many natural language processing tasks, but for many languages, including Estonian, BERT models are not yet available. However, there exist several multilingual BERT models that can handle multiple languages simultaneously and that have been trained also on Estonian data. In this paper, we evaluate four multilingual models---multilingual BERT, multilingual distilled BERT, XLM and XLM-RoBERTa---on several NLP tasks including POS and morphological tagging, NER and text classification. Our aim is to establish a comparison between these multilingual BERT models and the existing baseline neural models for these tasks. Our results show that multilingual BERT models can generalise well on different Estonian NLP tasks outperforming all baselines models for POS and morphological tagging and text classification, and reaching the comparable level with the best baseline for NER, with XLM-RoBERTa achieving the highest results compared with other multilingual models.
Workflow provenance in the lifecycle of scientific machine learning

Machine learning (ML) has already fundamentally changed several businesses. More recently, it has also been profoundly impacting the computational science and engineering domains, like geoscience, climate science, and health science. In these domains, users need to perform comprehensive data analyses combining scientific data and ML models to provide for critical requirements, such as reproducibility, model explainability, and experiment data understanding. However, scientific ML is multidisciplinary, heterogeneous, and affected by the physical constraints of the domain, making such analyses even more challenging. In this work, we leverage workflow provenance techniques to build a holistic view to support the lifecycle of scientific ML. We contribute with (i) characterization of the lifecycle and taxonomy for data analyses; (ii) design decisions to build this view, with a W3C PROV compliant data representation and a reference system architecture; and (iii) lessons learned after an evaluation in an Oil & Gas case using an HPC cluster with 393 nodes and 946 GPUs. The experiments show that the decisions enable queries that integrate domain semantics with ML models while keeping low overhead (<1%), high scalability, and an order of magnitude of query acceleration under certain workloads against without our representation.