Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel 2D CRLH TL and Its ZOR and FOR Applied on Dual-Band Omnidirectional Radiation Antenna

Abstract A new type of two-dimensional (2D) composite right/left-handed transmission lines (CRLH TL) which is composed of four one-dimensional (1D) CRLH TL is proposed in this letter. Each 1D CRLH TL consists of three metallic vias added for shunt inductance and an etched patch slot for series capacitance. Based on this structure, an antenna operating on zeroth-order resonance (ZOR) and first-order resonance (FOR) is designed and fabricated. By taking advantage of coaxially center feed and symmetric structure, a well omnidirectional radiation in XoY plane both in ZOR and FOR and a homogeneously suppressed cross-polarization is obtained. Also, the antenna has a gain value of 2.06 dB in ZOR f1 = 3.52 GHz and 2.94 dB in FOR f2 = 5.25 GHz, respectively.
GAMS, a user's guide

The most widely used is GAMS, which is specifically designed for Further details on GAMS can be found in the GAMS User's Guide. The GAMS User's Guide. Mathematical Programming, 87:153–176, 2000. (4). A. Brooke, D. Kendrick, and A. Meeraus. GAMS: A User's Guide. The Scientific Press, South San Francisco. JuMP is an open-source modeling language that allows users to express a wide D. Kendrick, A. Meeraus, and R. Raman, GAMS: A User's Guide, Scientific.
Searching 100M Images by Content Similarity.

In this paper we present the web user interface of a scalable and distributed system for image retrieval based on visual features and annotated text, developed in the context of the SAPIR project. Its architecture makes use of Peer-to-Peer networks to achieve scalability and efficiency allowing the management of huge amount of data and simultaneous access by a large number of users. Describing the SAPIR web user interface we want to encourage final users to use SAPIR to search by content similarity, together with the usual text search, on a large image collection (100 million images crawled from Flickr) with realistic response time. On the ground of the statistics collected, it will be possible, for the first time, to study the user behavior (e.g., the way they combine text and image content search) in this new realistic environment.
Identifying partial mouse brain microscopy images from Allen reference atlas using a contrastively learned semantic space

Precise identiﬁcation of mouse brain microscopy images is a crucial ﬁrst step when anatomical structures in the mouse brain are to be registered to a reference atlas. Practitioners usually rely on manual comparison of images or tools that assume the presence of complete images. This work explores Siamese Networks as the method for ﬁnding corresponding 2D reference atlas plates for given partial 2D mouse brain images. Siamese networks are a class of convolutional neural networks (CNNs) that use weight-shared paths to obtain low dimensional embeddings of pairs of input images. The correspondence between the partial mouse brain image and reference atlas plate is determined based on the distance between low dimensional embeddings of brain slices and atlas plates that are obtained from Siamese networks using contrastive learning. Experiments showed that Siamese CNNs can precisely identify brain slices using the Allen mouse brain atlas when training and testing images come from the same source. They achieved TOP-1 and TOP-5 accuracy of 25% and 100%, respectively, taking only 7.2 seconds to identify 29 images 1 .
Tensor-based regression models and applications

With the advancement of modern technologies, high-order tensors are quite widespread and abound in a broad range of applications such as computational neuroscience, computer vision, signal processing and so on. The primary reason that classical regression methods fail to appropriately handle high-order tensors is due to the fact that those data contain multiway structural information which cannot be directly captured by the conventional vector-based or matrix-based regression models, causing substantial information loss during the regression. Furthermore, the ultrahigh dimensionality of tensorial input produces huge amount of parameters, which breaks the theoretical guarantees of classical regression approaches. Additionally, the classical regression models have also been shown to be limited in terms of difficulty of interpretation, sensitivity to noise and absence of uniqueness. To deal with these challenges, we investigate a novel class of regression models, called tensorvariate regression models, where the independent predictors and (or) dependent responses take the form of high-order tensorial representations. We also apply them in numerous real-world applications to verify their efficiency and effectiveness. Concretely, we first introduce hierarchical Tucker tensor regression, a generalized linear tensor regression model that is able to handle potentially much higher order tensor input. Then, we work on online local Gaussian process for tensor-variate regression, an efficient nonlinear GPbased approach that can process large data sets at constant time in a sequential way. Next, we present a computationally efficient online tensor regression algorithm with general tensorial input and output, called incremental higher-order partial least squares, for the setting of infinite time-dependent tensor streams. Thereafter, we propose a super-fast sequential tensor regression framework for general tensor sequences, namely recursive higher-order partial least squares, which addresses issues of limited storage space and fast processing time allowed by dynamic environments. Finally, we introduce kernel-based multiblock tensor partial least squares, a new generalized nonlinear framework that is capable of predicting a set of tensor blocks by merging a set of tensor blocks from different sources with a boosted predictive power.
Navigating Change

Employees often perceive periods of change—no matter how warranted or beneficial—as crises, exhibiting both cognitive and emotional reactions including feelings of insecurity and uncertainty, even fear, chaos, stress, betrayal, grief, and anger. Management must have a clear strategy for communicating with employees through change, as employee expectations for open and honest communication are increased in times of shift and uncertainty. Open, honest, and regular communication is essential to keeping employees motivated and productive. Benefits of effective communication in times of change include higher employee satisfaction and engagement, lower turnover, and stronger long-term commitment. Solid internal relationships also strengthen ethics-related outcomes such as fraud reduction and reputation management.
Pulmonary rehabilitation and COPD: providing patients a good environment for optimizing therapy

: Chronic obstructive pulmonary disease (COPD) is an obstructive and progressive airway disease associated with an important reduction in daily physical activity and psychological problems that contribute to the patient’s disability and poor health-related quality of life (HRQoL). Nowadays, pulmonary rehabilitation (PR) plays an essential role in the management of symptomatic patients with COPD, by breaking the vicious circle of dyspnea–decreased activity–deconditioning–isolation. Indeed the main benefits of comprehensive PR programs for patients with COPD include a decrease in symptoms (dyspnea and fatigue), improvements in exercise tolerance and HRQoL, reduction of health care utilization (particularly bed-days), as well as an increase in physical activity. Several randomized studies and meta-analyses greatly established the benefits of PR, which additionally, is recommended in a number of influential guidelines. This review aimed to highlight the impact of PR on COPD patients, focusing on the clinical usefulness of PR, which provides patients a good support for change.
Ethylene Oxide: Cancer Evidence Integration and Dose–Response Implications

The International Agency for Research on Cancer (IARC) and the United States Environmental Protection Agency (USEPA) classified ethylene oxide (EtO) as a known human carcinogen. Critically, both noted that the epidemiological evidence based on lymphoid and breast cancers was “limited,” but that the evidence in animal studies was “sufficient” and “extensive” (respectively) and that EtO is genotoxic. The USEPA derived one of the highest published inhalation unit risk (IUR) values (3 × 10−3 per [µg/m3 EtO]), based on results from 2 epidemiological studies. We performed focused reviews of the epidemiological and toxicological evidence on the carcinogenicity of EtO and considered the USEPA’s reliance on a genotoxic mode of action to establish EtO’s carcinogenicity and to determine likely dose–response patterns. Higher quality epidemiological studies demonstrated no increased risk of breast cancers or lymphohematopoietic malignancies (LHM). Similarly, toxicological studies and studies of early effect biomarkers in animals and humans provided no strong indication that EtO causes LHM or mammary cancers. Ultimately, animal data are inadequate to define the actual dose–response shape or predict tumor response at very low doses with any confidence. We conclude that the IARC and USEPA classification of EtO as a known human carcinogen overstates the underlying evidence and that the IUR derived by USEPA grossly overestimates risk.
Using forward end-sweep to reduce transonic cantilevered stator losses to improve compressor performance

ABSTRACT The complicated flows in a transonic cantilevered stator would cause serious aerodynamic losses and reducing these losses would contribute much to improving the overall aerodynamic performance of a compressor. The paper conducts an overview study of the potential aerodynamic losses in a transonic cantilevered stator and presents a systematic numerical study for the effects of forward end sweep on reducing aerodynamic losses. A 2-D stator blade is used to throw light upon the physical mechanism of the forward end sweep on reducing the losses. At the casing region, forward end sweep has led to a redistribution of the blade load along the chordwise direction, higher in the front part and lower in the rear part. This result leads to a lower pressure gradient from the 30% chord location to the trailing edge and can be beneficial for the reduction of the corner separation. At the hub region, on one aspect, forward end sweep reduces both the peak Mach number and the size of the high-Ma region of the blade suction surface, thus decreasing the shock loss near the hub. On the other aspect, it also reduces the peak load and the strength of the leakage vortex, therefore alleviating the flow blockages. Simultaneously, the forward end sweep is applied to a commercial and high-performance multistage compressor to further validate its feasibility. When the technique is applied to the transonic first four stator rows, an improvement of adiabatic efficiency is achieved by ∼ 0.85% and the compressor stable operating range is slightly increased. The results suggest that the forward end-swept transonic cantilevered stators have a significant potential for reducing the stator losses. They would provide guidelines to advance the cantilevered stator design and further improve the compressor aerodynamic performance. Abbreviations: h, stagnation enthalpy; , stagnation pressure; p, static pressure; U, blade rotational speed at tip; V, velocity; , non-dimensional wall distance; , total pressure loss coefficient; , flow coefficient (Vx/U); , stage load coefficient (Δh/U2); , adiabatic efficiency; , total pressure ratio; in, inlet; out, outlet; LE/TE, leading/trailing edge; PS/SS, pressure/suction surface
Max-Sum diversification, monotone submodular functions and dynamic updates

Result diversification has many important applications in databases, operations research, information retrieval, and finance. In this paper, we study and extend a particular version of result diversification, known as max-sum diversification. More specifically, we consider the setting where we are given a set of elements in a metric space and a set valuation function f defined on every subset. For any given subset S, the overall objective is a linear combination of f(S) and the sum of the distances induced by S. The goal is to find a subset S satisfying some constraints that maximizes the overall objective. This problem is first studied by Gollapudi and Sharma in [17] for modular set functions and for sets satisfying a cardinality constraint (uniform matroids). In their paper, they give a 2-approximation algorithm by reducing to an earlier result in [20]. The first part of this paper considers an extension of the modular case to the monotone submodular case, for which the algorithm in [17] no longer applies. Interestingly, we are able to maintain the same 2-approximation using a natural, but different greedy algorithm. We then further extend the problem by considering any matroid constraint and show that a natural single swap local search algorithm provides a 2-approximation in this more general setting. This extends the Nemhauser, Wolsey and Fisher approximation result [20] for the problem of submodular function maximization subject to a matroid constraint (without the distance function component). The second part of the paper focuses on dynamic updates for the modular case. Suppose we have a good initial approximate solution and then there is a single weight-perturbation either on the valuation of an element or on the distance between two elements. Given that users expect some stability in the results they see, we ask how easy is it to maintain a good approximation without significantly changing the initial set. We measure this by the number of updates, where each update is a swap of a single element in the current solution with a single element outside the current solution. We show that we can maintain an approximation ratio of 3 by just a single update if the perturbation is not too large.
Physical‐chemical and microbiological characterization, and mutagenic activity of airborne PM sampled in a biomass‐fueled electrical production facility

Biomass combustion is used in heating and electric power generation in many areas of the world. Airborne particulate matter (PM) is released when biomass is brought to a facility, stored, and combusted. Occupational exposure to airborne PM within biomass‐fueled facilities may lead to health problems. In March and August of 2006, airborne PM was collected from a biomass‐fueled facility located in Denmark. In addition, source‐specific PM was generated from straw and wood pellets using a rotating drum. The PM was analyzed for polycyclic aromatic hydrocarbons (PAHs), metals, microbial components, mutagenic activity, and ability to generate highly reactive oxygen species (hROS) in cell‐free aqueous suspensions. PM collected from the boiler room and the biomass storage hall had higher levels of mutagenic activity, PAHs and metals, and a higher hROS generating potential than the source specific PM. The mutagenic activity was generally more potent without S9 activation, and on the metabolically enhanced strain YG1041, relative to TA98. Significant correlations were found between mutagenicity on YG1041 (without S9) and PAH concentration and mutagenicity on YG1041 (with S9) and hROS generating ability. PM collected in March was more toxic than PM collected in August. Overall, airborne PM collected from the facility, especially that from the boiler room, were more toxic than PM generated from straw and wood chips. The results suggest that exposure to combustion PM in a biomass‐fueled facility, which likely includes PM from biomass combustion as well as internal combustion vehicles, may contribute to an elevated risk of adverse health effects. Environ. Mol. Mutagen. , 2011. © 2010 Wiley‐Liss, Inc.
Scalable Community Detection in Massive Networks using Aggregated Relational Data

Scalable Community Detection in Massive Networks using Aggregated Relational Data Timothy Jones The analysis of networks is used in many fields of study including statistics, social science, computer sciences, physics, and biology. The interest in networks is diverse as it usually depends on the field of study. For instance, social scientists are interested in interpreting how edges arise, while biologists seek to understand underlying biological processes. Among the problems being explored in network analysis, community detection stands out as being one of the most important. Community detection seeks to find groups of nodes with a large concentration of links within but few between. Inferring groups are important in many applications as they are used for further downstream analysis. For example, identifying clusters of consumers with similar purchasing behavior in a customer and product network can be used to create better recommendation systems. Finding a node with a high concentration of its edges to other nodes in the community may give insight into how the community formed. Many statistical models for networks implicitly define the notion of a community. Statistical inference aims to fit a model that posits how vertices are connected to each other. One of the most common models for community detection is the stochastic block model (SBM) [Holland et al., 1983]. Although simple, it is a highly expressive family of random graphs. However, it does have its drawbacks. First, it does not capture the degree distribution of real-world networks. Second, it allows nodes to only belong to one community. In many applications, it is useful to consider overlapping communities. The Mixed Membership Stochastic Blockmodel (MMSB) is a Bayesian extension of the SBM that allows nodes to belong to multiple communities. Fitting large Bayesian network models quickly become computationally infeasible when the number of nodes grows into the hundred of thousands and millions. In particular, the number of parameters in the MMSB grows as the number of nodes squared. This thesis introduces an efficient method for fitting a Bayesian model to massive networks through use of aggregated relational data. Our inference method converges faster than existing methods by leveraging nodal information that often accompany real world networks. Conditioning on this extra information leads to a model that admits a parallel variational inference algorithm. We apply our method to a citation network with over three million nodes and 25 million edges. Our method converges faster than existing posterior inference algorithms for the MMSB and recovers parameters better on simulated networks generated according to the MMSB.
Generalizations of multisets and rough approximations

Three classes of generalizations of multisets are reviewed. They are real‐valued multisets, fuzzy number–valued multisets, and fuzzy multisets. A family of generalized multisets that includes these three classes is proposed. A generalized multiset in this family has a membership of a closed region on a plane for each object. Upper and lower approximations of the generalized multisets are defined whereby rough approximations of real‐valued multisets, fuzzy number–valued multisets, and fuzzy multisets are derived. Applications to fuzzy databases and information retrieval are suggested. © 2004 Wiley Periodicals, Inc.
A p r 2023 C O - TRAINING WITH H IGH -C ONFIDENCE P SEUDO L ABELS FOR S EMI - SUPERVISED M EDICAL I MAGE S EGMENTATION

Consistency regularization and pseudo labeling-based semi-supervised methods perform co-training using the pseudo labels from multi-view inputs. However, such co-training models tend to converge early to a consensus, degenerating to the self-training ones, and produce low-conﬁdence pseudo labels from the perturbed inputs during training. To address these issues, we propose an U ncertainty-guided C ollaborative M ean- T eacher (UCMT) for semi-supervised semantic segmentation with the high-conﬁdence pseudo labels. Concretely, UCMT consists of two main components: 1) collaborative mean-teacher (CMT) for encouraging model disagreement and performing co-training between the sub-networks, and 2) uncertainty-guided region mix (UMIX) for manipulating the input images according to the uncertainty maps of CMT and facilitating CMT to produce high-conﬁdence pseudo labels. Combining the strengths of UMIX with CMT, UCMT can retain model disagreement and enhance the quality of pseudo labels for the co-training segmentation. Extensive experiments on four public medical image datasets including 2D and 3D modalities demonstrate the superiority of UCMT over the state-of-the-art
Unique properties of graphene quantum dots and their applications in photonic/electronic devices

In recent years, graphene quantum dots (GQDs) have been recognized as an attractive building block for electronic, photonic, and bio-molecular device applications. This paper reports the current status of studies on the novel properties of GQDs and their hybrids with conventional and low-dimensional materials for device applications. In this review, more emphasis is placed on the structural, electronic, and optical properties of GQDs, and device structures based on the combination of GQDs with various semiconducting/insulating materials such as graphene, silicon dioxide, Si quantum dots, silica nanoparticles, organic materials, and so on. Because of GQDs’ unique properties, their hybrid structures are employed in high-efficiency devices, including photodetectors, solar cells, light-emitting diodes, flash memory, and sensors.
Review of Time Series Analysis and Its Applications With R Examples (3rd Edition), by Robert H. Shumway & David S. Stoffer

The earlier edition of Time Series Analysis and Its Applications With R Examples was the textbook used in the two-semester graduate courses I took with Dr. Robert H. Shumway in graduate school. In this book, Shumway and Stoffer provide comprehensive descriptions on theories and applications of various time series models. They illustrate the utility of models using time series observed from economics, social sciences, medical science, neuroscience, physical and environmental sciences, geophysics, and more. The R examples embedded in the text make the third edition even more practically useful for applied researchers. However, this book might seem highly technical to people who lack the necessary background or training in mathematics and statistics.
The fourth generation: A comprehensive analysis

The subject of this thesis is a systematic phenomenological analysis of the SM4, the standard model of particle physics extended by a fourth generation of quarks and leptons. In this framework we study the impact of direct measurements of the involved parameters, electroweak precision tests, as well as flavor and CP violation observables in the quark and lepton sector. We show that electroweak precision measurements do not exclude a fourth generation, but impose severe bounds on the involved parameter space. Subsequently, we calculate more than 30 flavor changing neutral current and CP-violating processes in the quark and lepton sector. A global numerical analysis of all calculated observables reveals characteristic patterns of correlations between certain measurable quantities and allows to clearly distinguish the SM4 from other new physics scenarios.
Insight and empathy in schizophrenia

Age (years) 32.26 (5.58) 27.19 (5.17) −2.99 . 005⁎ Education (years) 12.63 (1.61) 14.60 (2.21) 3.19 . 003⁎⁎ Estimated FSIQ 102.24 (14.81) 112.10 (10.22) 2.33 . 03⁎ Social EQ 5.11 (3.40) 7.79 (2.27) 2.96 . 001⁎ Cognitive EQ 9.89 (6.33) 15.63 (9.89) 3.01 b . 001⁎⁎ Emotional EQ 8.89 (4.15) 14.00 (4.19) 3.77 . 001⁎⁎ Total EQ 35.47 (11.88) 51.21 (13.06) 3.89 b . 001⁎⁎ QIDS 11.37 (5.71) 4.23 (2.65) −4.75 b . 001⁎ SUMD-AV 4.50 (1.76) Positive PANSS 18.93 (4.31) Negative PANSS 16.5 (4.47) General PANSS 37.14 (4.72) Total PANSS 72.57 (11.11)