Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatio-Temporal Consistency to Detect and Segment Carried Objects

We present a new method to detect carried objects and to segment them accurately after detection. The proposed method includes several contributions: first, a new superpixelbased descriptor is proposed to identify carried object-like candidate regions using human shape modelling. Second, integrating spatio-temporal information of candidate regions to detect carried objects. We exploit the consistency of recurring carried object candidates viewed over time to detect the final carried object locations based on their motion and location priors. Last, the detected carried object regions are accurately segmented. Compared to existing methods, our approach is not only focusing on detecting carried objects. It takes a step forward and accurately segment them. Our method to carried object segmentation couples local appearance cues with location priors of the detected carried objects to produce accurate segmentation. Experimental evaluation on two datasets demonstrates that both our carried object detection and segmentation methods significantly outperform competing algorithms.
Bilateral Asymmetries in Ultrasound Assessments of the Rectus Femoris throughout an NCAA Division I Volleyball Preseason

The purpose of the study was to assess glycogen content of the rectus femoris (RF) muscles utilizing high-frequency ultrasound throughout an intensive, nine-day preseason training period in NCAA division I volleyball athletes. In the morning prior to the beginning of practice, athletes (n = 13) left and right RF muscles were assessed via ultrasound to quantify muscle fuel ratings (0–100 score range). The recommended location of the RF ultrasound scans were based on manufacturer guidelines, and the same technician recorded the daily measurements. To assess daily training load, session ratings of perceived exertion (s-RPE) were utilized. A paired t-test revealed a large significant difference between left (51.7 ± 17.9) and right (32.8 ± 17.4) RF muscle fuel ratings (p < 0.001). There was also a major effect of time on s-RPE (p < 0.001) and left (dominant) RF fuel rating (p = 0.001). s-RPE decreased from the beginning to the end of the training camp. However, left RF fuel ratings increased from the first to the second day, then remained elevated all throughout the preseason. In conclusion, all athletes were left-leg dominant and had a 57.6% bilateral asymmetry between their left and right RF muscle fuel ratings despite changes in training load. High-frequency ultrasounds are a noninvasive assessment tool that can determine glycogen replenishment asymmetries in the RF.
Burnout and Related Factors in Organ Donation Coordinators: A Cross-Sectional Study in China

Background Organ donation coordinators play an important role in the organ transplantation process. Job burnout can seriously affect their turnover rates and the organ donation rate. The present study investigated the level of job burnout and its related factors among organ donation coordinators in China. Material/Methods From March to May 2017, we administered questionnaire surveys to 320 organ donation coordinators from 32 cities. The questionnaire included the following: the Chinese version of MBI-GS was used to evaluate the burnout levels of organ donation coordinators, the Chinese version of GSES was used to assess related personal factors, and the Job Demands Scale and the SSRS were used to evaluate related environmental factors. Results We received a total of 283 questionnaires with a response rate of 88.4%. Out of 283 organ donation coordinators, 169 coordinators (59.7%) showed burnout symptoms. Among them, 161 (56.9%) had mild burnout and 8 (2.8%) had severe burnout. Analysis by multiple linear regression reveals that gender, marital status, educational level, work unit, type of coordinator, job demands, self-efficacy, social support, and job satisfaction are the main factors affecting job burnout in organ donation coordinators. Conclusions The prevalence of job burnout among Chinese organ donation coordinators is relatively high, with mild burnout as the main symptom and reduced personal accomplishment as the main clinical feature. We hope this study will provide a reference for the recruitment and reduction of coordinator burnout.
The expression and correlation between chemokine CCL7 and ABCE1 in non-small cell lung cancer

Lung cancer is a malignant disease, and has the highest incidence and mortality worldwide. Lung cancer is also a popular subject in the field of cancer research. The molecular mechanisms of lung cancer development, invasion and metastasis need to be determined to prolong survival times and improve the quality of life. Recent studies have demonstrated that ATP-binding cassette sub-family E member 1 (ABCE1) is one of the factors that contributes to the development and metastasis of lung cancer, but the specific mechanism of this phenomenon remains unclear. A polymerase chain reaction microarray was used in the present study to screen for chemokine (C-C motif) ligand 7 (CCL7) expression in cell lines that highly expressed ABCE1, and the results showed that CCL7 was highly expressed in H1299 cells (P<0.01). The expression of CCL7 and ABCE1 in lung cancer tissues obtained from 30 patients with non-small cell lung cancer (NSCLC) was higher than that in adjacent normal lung tissues (P<0.01), and a positive correlation between the expression levels of the two genes in NSCLC was observed. These findings indicate that ABCE1 is involved in the development and progression of lung cancer through the CCL7 signaling pathway.
Isolation and characterization of SSR and EST‐SSR loci in Chamaecyparis formosensis (Cupressaceae)

Premise of the Study Simple sequence repeat (SSR) and expressed sequence tag (EST)–SSR markers were developed as tools for marker‐assisted selection of Chamaecyparis formosensis and for the molecular differentiation of cypress species. Methods and Results Based on the SSR‐enriched genomic libraries and transcriptome data of C. formosensis, 300 primer pairs were selected for initial confirmation, of which 19 polymorphic SSR and eight polymorphic EST‐SSR loci were chosen after testing in 92 individuals. The number of alleles observed for these 27 loci ranged from one to 17. The levels of observed and expected heterozygosity ranged from 0.000 to 1.000 and from 0.000 to 0.903, respectively. Most markers also amplified in C. obtusa var. formosana. Conclusions The developed SSR and EST‐SSR sequences are the first reported markers specific to C. formosensis. These markers will be useful for individual identification of C. formosensis and to distinguish cypress species such as C. obtusa var. formosana.
Magnetic Noise Prediction and Evaluation in Tunneling Magnetoresistance Sensors

We propose a simple model for prediction of magnetic noise level in tunneling magnetoresistance (TMR) sensors. The model reproduces experimental magnetic 1/f and white noise components, which are dependent on sensors resistance and field sensitivity. The exact character of this dependence is determined by comparing the results with experimental data using a statistical cross-validation procedure. We show that the model is able to correctly predict magnetic noise level for systems within wide range of resistance, volume and sensitivity, and that it can be used as a robust method for noise evaluation in TMR sensors based on a small number of easily measurable parameters only.
Alterations of Signaling Pathways Related to the Immune System in Breast Cancer: New Perspectives in Patient Management

In recent years, immune manipulation for cancer treatment, including breast cancer, has been increasingly gaining consent, and many attempts have been made, mainly by either strengthening the immune response (IR) or by inhibiting immune evasion. Therefore, elucidating the related mechanisms is of importance due to the potential to improve the management of cancer patients by immunotherapy. This review article summarized some recent experimental studies, which have discovered novel alterations of signaling pathways related to the immune system in breast cancer. These altered signaling pathways have been grouped according to the general biological mechanism involved: tumor-initiating cells (TICs), cancer stem cells (CSCs), immune evasion, tumor growth and progression, prediction of clinical outcome and prediction of response, or resistance to chemotherapy. These altered pathways related to the immune system open clinical opportunities for the prognosis or treatment of patients. Many of these pathways are related to the origin of breast cancer and immune evasion. We recommended development of new drugs which act on these molecular pathways, and the designing of clinical trials to be carried out mainly in breast cancer patients who required adjuvant treatment.
Design procedure for multifinger MOSFET two-stage OTA with shallow trench isolation effect

Nanoscale complementary metal–oxide–semiconductor (CMOS) circuit design extensively employs multifinger layout technique to alleviate the performance degrading parasitic and mismatch effects that are typically observed with single-finger layout. However, a continuous increase in the number of fingers accompanied by a simultaneous decrease in their finger width could lead to the penalty of a higher degree of variation in the MOSFET's small-signal parameters. It is due to the heightened shallow trench isolation (STI) stress that gets developed in such devices. The optimisation of circuit performance with the arbitrarily fixed number and width of fingers would be ambiguous. In this work, an analysis of current–voltage ( I – V ) characteristics of a MOSFET as a function of number of fingers has been proposed. It was found that both the drain current and gate transconductance get affected by the number of fingers. The authors proposed a Miller-compensated two-stage [operational transconductance amplifier (OTA)] and common source amplifier by considering STI effect. It is also found that the parameters of the proposed design matched well with the set of desired specifications. Also, the area of multifinger MOSFET OTA is lowered by up to 60% relative to that from the conventional. All post-layout simulations were performed using standard UMC 65 nm CMOS technology.
Bounded Symbolic Execution for Runtime Error Detection of Erlang Programs

Dynamically typed languages, like Erlang, allow developers to quickly write programs without explicitly providing any type information on expressions or function definitions. However, this feature makes those languages less reliable than statically typed languages, where many runtime errors can be detected at compile time. In this paper, we present a preliminary work on a tool that, by using the well-known techniques of metaprogramming and symbolic execution, can be used to perform bounded verification of Erlang programs. In particular, by using Constraint Logic Programming, we develop an interpreter that, given an Erlang program and a symbolic input for that program, returns answer constraints that represent sets of concrete data for which the Erlang program generates a runtime error.
Sim-to-Real Transfer Learning using Robustified Controllers in Robotic Tasks involving Complex Dynamics

Learning robot tasks or controllers using deep reinforcement learning has been proven effective in simulations. Learning in simulation has several advantages. For example, one can fully control the simulated environment, including halting motions while performing computations. Another advantage when robots are involved, is that the amount of time a robot is occupied learning a task—rather than being productive—can be reduced by transferring the learned task to the real robot. Transfer learning requires some amount of fine-tuning on the real robot. For tasks which involve complex (non-linear) dynamics, the fine-tuning itself may take a substantial amount of time. In order to reduce the amount of fine-tuning we propose to learn robustified controllers in simulation. Robustified controllers are learned by exploiting the ability to change simulation parameters (both appearance and dynamics) for successive training episodes. An additional benefit for this approach is that it alleviates the precise determination of physics parameters for the simulator, which is a non-trivial task. We demonstrate our proposed approach on a real setup in which a robot aims to solve a maze game, which involves complex dynamics due to static friction and potentially large accelerations. We show that the amount of fine-tuning in transfer learning for a robustified controller is substantially reduced compared to a non-robustified controller.
Lower Price or Higher Reward? Measuring the Effect of Consumers' Preferences on Reward Programs

Despite criticisms regarding their effectiveness, reward programs today represent a prevalent and apparently successful form of marketing investment for several industries such as airlines, hotels, gas stations, and credit cards. To understand what might contribute to their success, this paper investigates the purchase behavior of consumers who participate in a reward program from the travel industry. We estimate a dynamic demand model of gasoline purchase, and compare the value that consumers attach to rewards with the value they attach to the money spent for gasoline. We find that there exists a significant portion of frequent travelers who extract more value from one dollar’s worth of rewards than from one dollar spent for gasoline. Most of the effectiveness of the reward program is due to the behavior of these consumers. Their insensitivity to price induces firms to increase fuel prices by 1 euro cent per liter, which corresponds to about 10% increase in their margins. The program also contributes to .
Robust Densest Subgraph Discovery

Dense subgraph discovery is an important primitive in graph mining, which has a wide variety of applications in diverse domains. In the densest subgraph problem, given an undirected graph G = (V, E) with an edge-weight vector w, we aim to find a subset of vertices S that maximizes the density, i.e., w(S) / |S|, where w(S) is the sum of the weights of the edges in the subgraph induced by S. Although the densest subgraph problem is one of the most well-studied optimization problems for dense subgraph discovery, there is an implicit strong assumption; it is assumed that the weights of all the edges are known exactly as input. In real-world applications, there are often cases where we have only uncertain information of the edge weights. In this study, we provide a framework for dense subgraph discovery under the uncertainty of edge weights. Specifically, we address such an uncertainty issue using the theory of robust optimization. First, we formulate our fundamental problem, the robust densest subgraph problem, and present a simple algorithm. We then formulate the robust densest subgraph problem with sampling oracle that models dense subgraph discovery using an edge-weight sampling oracle, and present an algorithm with a strong theoretical performance guarantee. Computational experiments using both synthetic graphs and popular real-world graphs demonstrate the effectiveness of our proposed algorithms.
Secure cognitive radio networks with source selection and unreliable backhaul connections

In this study, the secrecy performance of cooperative half-duplex cognitive relay networks (CRNs) comprised of multiple primary users (PUs) and multiple eavesdroppers under the effect of unreliable wireless backhauls is studied. The authors consider relay networks with single relay assisting multiple sources under power constraints at PUs and secondary transmitters, and based on the availability of channel-state information at the sources, they propose two best source selection schemes, namely: (i) optimal source selection (OSS) and (ii) sub-optimal source selection (SoSS). For both scenarios, they obtain exact closed-form and asymptotic expressions for the system's outage probability and carry out numerical simulations to justify their analyses. From the results, source selection is proven to be capable of counteracting the negative impact of unreliable backhaul on CRNs. The secrecy performance limitations are also verified to be influenced by the backhaul reliability. Furthermore, OSS is more desirable compared to SoSS in low signal-to-noise ratio (SNR) regime, while the secrecy performance for both schemes converge to an asymptotic limit in high-SNR ranges.
The Binding of BF-227-Like Benzoxazoles to Human α-Synuclein and Amyloid β Peptide Fibrils

Development of an α-synuclein (α-Syn) positron emission tomography agent for the diagnosis and evaluation of Parkinson disease therapy is a key goal of neurodegenerative disease research. BF-227 has been described as an α-Syn binder and hence was employed as a lead to generate a library of α-Syn-binding compounds. [3H]BF-227 bound to α-Syn and amyloid β peptide (Aβ) fibrils with affinities (KD) of 46.0 nM and 15.7 nM, respectively. Affinities of BF-227-like compounds (expressed as Ki) for α-Syn and Aβ fibrils were determined, along with 5 reference compounds (flutafuranol, flutemetamol, florbetapir, BF-227, and PiB). Selectivity for α-Syn binding, defined as the Ki(Aβ)/Ki(α-Syn) ratio, was 0.23 for BF-227. A similar or lower ratio was measured for analogues decorated with alkyl or oxyethylene chains attached to the oxygen at the 6 position of BF-227, suggesting a lack of involvement of the side chain in fibril binding. BF-227-like iodobenzoxazoles had lower affinities and poor α-Syn selectivity. However, BF-227-like fluorobenzoxazoles had improved α-Syn selectively having Ki(Aβ)/Ki(α-Syn) ranging from 2.2 to 5.1 with appreciable fibril affinity, although not sufficient to warrant further investigation. Compounds based on fluorobenzoxazoles might offer an approach to obtaining an α-Syn imaging agent with an appropriate affinity and selectivity.
Part-based Graph Convolutional Network for Action Recognition

Human actions comprise of joint motion of articulated body parts or `gestures'. Human skeleton is intuitively represented as a sparse graph with joints as nodes and natural connections between them as edges. Graph convolutional networks have been used to recognize actions from skeletal videos. We introduce a part-based graph convolutional network (PB-GCN) for this task, inspired by Deformable Part-based Models (DPMs). We divide the skeleton graph into four subgraphs with joints shared across them and learn a recognition model using a part-based graph convolutional network. We show that such a model improves performance of recognition, compared to a model using entire skeleton graph. Instead of using 3D joint coordinates as node features, we show that using relative coordinates and temporal displacements boosts performance. Our model achieves state-of-the-art performance on two challenging benchmark datasets NTURGB+D and HDM05, for skeletal action recognition.
Learning to group and label fine-grained shape components

A majority of stock 3D models in modern shape repositories are assembled with many fine-grained components. The main cause of such data form is the component-wise modeling process widely practiced by human modelers. These modeling components thus inherently reflect some function-based shape decomposition the artist had in mind during modeling. On the other hand, modeling components represent an over-segmentation since a functional part is usually modeled as a multi-component assembly. Based on these observations, we advocate that labeled segmentation of stock 3D models should not overlook the modeling components and propose a learning solution to grouping and labeling of the fine-grained components. However, directly characterizing the shape of individual components for the purpose of labeling is unreliable, since they can be arbitrarily tiny and semantically meaningless. We propose to generate part hypotheses from the components based on a hierarchical grouping strategy, and perform labeling on those part groups instead of directly on the components. Part hypotheses are mid-level elements which are more probable to carry semantic information. A multi-scale 3D convolutional neural network is trained to extract context-aware features for the hypotheses. To accomplish a labeled segmentation of the whole shape, we formulate higher-order conditional random fields (CRFs) to infer an optimal label assignment for all components. Extensive experiments demonstrate that our method achieves significantly robust labeling results on raw 3D models from public shape repositories. Our work also contributes the first benchmark for component-wise labeling.
Environmental Influences on Neuromorphology in the Non-Native Starling Sturnus vulgaris

Cognitive traits are predicted to be under intense selection in animals moving into new environments and may determine the success, or otherwise, of dispersal and invasions. In particular, spatial information related to resource distribution is an important determinant of neural development. Spatial information is predicted to vary for invasive species encountering novel environments. However, few studies have tested how cognition or neural development varies intraspecifically within an invasive species. In Australia, the non-native common starling Sturnus vulgaris inhabits a range of habitats that vary in seasonal resource availability and distribution. We aimed to identify variations in the brain mass and hippocampus volume of starlings in Australia related to environmental variation across two substantially different habitat types. Specifically, we predicted variation in brain mass and hippocampal volume in relation to environmental conditions, latitude, and climatic variables. To test this, brain mass and volumes of the hippocampus and two control brain regions (telencephalon and tractus septomesencephalicus) were quantified from starling brains gathered from across the species’ range in south eastern Australia. When comparing across an environmental gradient, there was a significant interaction between sex and environment for overall brain mass, with greater sexual dimorphism in brain mass in inland populations compared to those at the coast. There was no significant difference in hippocampal volume in relation to environmental measures (hippocampus volume, n = 17) for either sex. While these data provide no evidence for intraspecific environmental drivers for changes in hippocampus volume in European starlings in Australia, they do suggest that environmental factors contribute to sex differences in brain mass. This study identifies associations between the brain volume of a non-native species and the environment; further work in this area is required to elucidate the mechanisms driving this relationship.
Unsupervised Machine Commenting with Neural Variational Topic Model

Article comments can provide supplementary opinions and facts for readers, thereby increase the attraction and engagement of articles. Therefore, automatically commenting is helpful in improving the activeness of the community, such as online forums and news websites. Previous work shows that training an automatic commenting system requires large parallel corpora. Although part of articles are naturally paired with the comments on some websites, most articles and comments are unpaired on the Internet. To fully exploit the unpaired data, we completely remove the need for parallel data and propose a novel unsupervised approach to train an automatic article commenting model, relying on nothing but unpaired articles and comments. Our model is based on a retrieval-based commenting framework, which uses news to retrieve comments based on the similarity of their topics. The topic representation is obtained from a neural variational topic model, which is trained in an unsupervised manner. We evaluate our model on a news comment dataset. Experiments show that our proposed topic-based approach significantly outperforms previous lexicon-based models. The model also profits from paired corpora and achieves state-of-the-art performance under semi-supervised scenarios.