Miracle-dz/UniFAR-FTUs huggingface dataset:
A Dual Reinforcement Learning Framework for Weakly Supervised Phrase Grounding

Weakly-supervised phrase grounding aims to localize a specific region in an image that corresponds to the given textual phrase, where the mapping between noun phrases and image regions is not available in the training stage. Previous methods typically exploit an additional proxy task (e.g., phrase reconstruction or image-phrase alignment) to provide supervision for training, since the lack of region-level annotations in the weakly-supervised setting. However, there exists a significant gap in optimization objectives between the proxy tasks and the target grounding task, which may result in low-efficient optimization for the target model. Therefore, in this paper, we propose a novel dual reinforcement learning framework to directly optimize the phrase grounding model. Specifically, we consider the duality of phrase grounding and phrase generation tasks. These two tasks form a closed loop that can provide quality feedback signals to measure the performance of each other. In this way, we can measure the correctness of the localized regions and thus be able to optimize the grounding model directly. We design two reward functions to quantify the feedback signals and train the models via reinforcement learning. In addition, to relieve the training difficulty of our framework, we present a heuristic algorithm to generate pseudo region-phrase pairs to warm-start our models. We perform experiments on two popular phrase grounding datasets: ReferItGame and Flickr30K Entities, and the results demonstrate that our method outperforms the previous methods by a large margin.
WIB-SAR: Write Intensity Based Selective Address Remapping

The transistor density within chips has increased dramatically in recent years. This leads to the development of many core systems which allows more than one application to run concurrently resulting in a high pressure on the memory subsystems. Researchers are trying to find new alternatives to traditional DRAM based memories because of their inherent limitations like low density and high leakage power. Emerging Non-Volatile memories such as Phase Change Memory (PCM) and Resistive RAM can be considered the most promising candidates for main memories due to many useful properties, such as non-volatility, high density, and low leakage power. However, drawbacks such as limited write endurance and high write energy reduce the chances of replacing traditional DRAM with non-volatile memory. In this paper, we propose a write variation-aware selective address remapping (WIB-SAR) scheme to improve the lifetime of PCM memories. The proposed scheme judiciously monitors the write accesses to different locations within the memory. Through this monitoring, WIB-SAR remaps heavily written addresses with less written addresses within a segment to evenly distribute the writes. Our method tries to improve the lifetime with minimum energy overhead. Experimental results show that the proposed technique improves lifetime by 168% with a minimum energy overhead of 1.1% over existing techniques.
Interacting Multiple Model Kalman Filtering for Optimal Vehicle State Estimation

In this paper, we propose a new method for optimal vehicle condition estimation by utilizing low-cost sensor fusion using in-vehicle sensors and a standalone Global Positioning System (GPS). Estimation targets are non-measurable vehicle conditions such as vehicle side slip angles and tire cornering stiffness values of front and rear axles. Interacting Multiple Model (IMM) Kalman filters are designed to combine the outputs of two Kalman filters, each based on vehicle kinematics and bike models. To optimally combine the outputs of these two Kalman filter outputs, we compute the weighted probabilities of each output based on a probabilistic method that reflects each model feature in real time. The final estimated performance of the proposed IMM Kalman filter was confirmed based on the experimental results. In particular, comparisons of single Kalman filters and estimation accuracy are performed in detail. The main advantages of the proposed estimation algorithm are summarized as 1) optimality according to vehicle model combination and 2) accurate estimation of tire cornering stiffness.
Deep Transferable Intelligence for Spatial Variability Characterization and Data-Efficient Learning in Biomechanical Measurement

Biomechanical measurement is of promising value for rehabilitation, assisted living, and lifestyle management applications. Nevertheless, the understanding is still limited on the spatial variability of biomechanical dynamics that is essential for optimal motion sensor configuration. Besides, training physical activity detectors is usually data-heavy and time-consuming. Targeting these two challenges, in this study, we propose a novel deep transfer intelligence framework, which leverages deep learning to characterize the spatial variability of different motion sensors on diverse body locations, and further leverages intersubject transfer learning to maximize data efficiency in challenging scarce data learning. More specifically, to characterize the spatial variability, we propose deep convolutional neural networks (CNNs) to investigate the capabilities of both different sensor locations and channels on physical activity measurement. The characterization determines both optimal sensor configuration and optimal channel configuration. Further, we propose a transfer learning approach to mine intersubject similarity and then share learned knowledge among subjects, thereby minimizing the training effort and maximizing the data efficiency in the wearable scarce data learning scenario. Our evaluation experiments have determined the optimal sensor location from seven options as thigh, and the optimal sensor and channel configuration from 42 options as thigh-accelerometer-axis- $Y$ . Our experiments have further demonstrated that, with transfer learning under the optimal sensor and channel configuration, only 10% of data from the target subject for model fine-tuning can yield a physical activity detection (PAD) accuracy of up to 91.6%, with a performance boosting of 9% compared with direct learning without transfer learning. Therefore, the deep transferable learning framework will greatly advance spatial variability characterization for optimal sensor and channel configuration and efficient scarce data learning in biomedical measurement.
Cyanogenesis, a Plant Defence Strategy against Herbivores

Plants and phytophagous arthropods have coevolved in a long battle for survival. Plants respond to phytophagous feeders by producing a battery of antiherbivore chemical defences, while herbivores try to adapt to their hosts by attenuating the toxic effect of the defence compounds. Cyanogenic glucosides are a widespread group of defence chemicals that come from cyanogenic plants. Among the non-cyanogenic ones, the Brassicaceae family has evolved an alternative cyanogenic pathway to produce cyanohydrin as a way to expand defences. When a plant tissue is disrupted by an herbivore attack, cyanogenic substrates are brought into contact with degrading enzymes that cause the release of toxic hydrogen cyanide and derived carbonyl compounds. In this review, we focus our attention on the plant metabolic pathways linked to cyanogenesis to generate cyanide. It also highlights the role of cyanogenesis as a key defence mechanism of plants to fight against herbivore arthropods, and we discuss the potential of cyanogenesis-derived molecules as alternative strategies for pest control.
Financial and Demographic Factors Associated with Cooking Self-Efficacy Among College Students from Three Minority Groups

ABSTRACT Background There are many socio-environmental factors that can limit cooking self-efficacy among university students, where minorities are usually neglected. Purpose To identify the dynamics of income source and money expenditure with cooking self-efficacy of three university minority groups. Methods For this cross-sectional study, recruitment was between October and November 2019, via online survey through Qualtrics platform. A total of 2817 participants completed the survey online which provided data from cooking self-efficacy (20 points scale), money expenditure and financial issues. Results An average of 13.5 was found as cooking self-efficacy. Four of the independent variables: first generation, internationals, age, and employment status show significative relationship (<0.01) with cooking self-efficacy. Discussion This study supports the importance of prioritizing university minority groups, helping to understand their financial support and how they distribute it when managing and prioritizing their finances. Moreover, each group faces different limitations in terms of what the construction of these skills encompasses. Translation to Health Education Practice The findings present in this study are significant for health education specialist focused on identifying factors that impact priority populations.
Humour and the stretchy temporality of peer conflict in a group early childhood setting: An analysis of relational power

While research on children’s humour is growing, few investigations have focused on how children use humour in conflict interactions, and specifically in group early childhood settings. Using data extracts from a project that investigated children’s naturally occurring conflict interactions in a multi-ethnic early childhood setting, we use interactional sociolinguistics to analyse how children used humour at unexpected moments during conflict situations. The analysis probes different meanings carried in the children’s use of humour, illustrating how humour intersected with personal and relational power to resolve or defuse conflict, or to coerce compliance with existing peer relational positions. The analysis broadens understandings of the significance of humour in children’s lives in early childhood settings, and particularly in the context of conflict interactions that have a ‘stretchy temporality’ connecting interactive moves to others in the past, and to existing power positions in peer relationships.
Local coordinate systems-based method to analyze high-order modes of n-step Timoshenko beam

High-frequency transverse vibration of stepped beams has attracted increasing attention in various industrial areas. For an n-step Timoshenko beam, the governing differential equations of transverse vibration have been well established in the literature on the basis of assembling classic Timoshenko beam equations for uniform beam segments. However, solving the governing differential equation has not been resolved well to date, manifested by a computational bottleneck: only the first k modes (k ≤ 12) are solvable for i-step (i ≥ 0) Timoshenko beams. This bottleneck diminishes the completeness of stepped Timoshenko beam theory. To address this problem, this study first reveals the root cause of the bottleneck in solving the governing differential equations for high-order modes, and then creates a sophisticated method, based on local coordinate systems, that can overcome the bottleneck to accomplish high-order mode shapes of an n-step Timoshenko beam. The proposed method uses a set of local coordinate systems in place of the conventional global coordinate system to characterize the transverse vibration of an n-step Timoshenko beam. With the method, the local coordinate systems can simplify the frequency equation for the vibration of an n-step Timoshenko beam, making it possible to obtain high-order modes of the beam. The accuracy, capacity, and efficiency of the method based on local coordinate systems in acquiring high-order modes are corroborated using the well-known exact dynamic stiffness method underpinned by the Wittrick-Williams algorithm as a reference. Removal of the bottlenecks in solving the governing differential equations for high-order modes contributes usefully to the completeness of stepped Timoshenko beam theory.
A Multifunctional Coating on Sulfur-Containing Carbon-Based Anode for High-Performance Sodium-Ion Batteries

A sulfur doping strategy has been frequently used to improve the sodium storage specific capacity and rate capacity of hard carbon. However, some hard carbon materials have difficulty in preventing the shuttling effect of electrochemical products of sulfur molecules stored in the porous structure of hard carbon, resulting in the poor cycling stability of electrode materials. Here, a multifunctional coating is introduced to comprehensively improve the sodium storage performance of a sulfur-containing carbon-based anode. The physical barrier effect and chemical anchoring effect contributed by the abundant C-S/C-N polarized covalent bond of the N, S-codoped coating (NSC) combine to protect SGCS@NSC from the shuttling effect of soluble polysulfide intermediates. Additionally, the NSC layer can encapsulate the highly dispersed carbon spheres inside a cross-linked three-dimensional conductive network, improving the electrochemical kinetic of the SGCS@NSC electrode. Benefiting from the multifunctional coating, SGCS@NSC exhibits a high capacity of 609 mAh g−1 at 0.1 A g−1 and 249 mAh g−1 at 6.4 A g−1. Furthermore, the capacity retention of SGCS@NSC is 17.6% higher than that of the uncoated one after 200 cycles at 0.5 A g−1.
Design of Conformal Helical Antenna with Selective Bands

The paper presents design of U-shaped partial cavity which backs a 11⁄2 turn helical antenna. The antenna is operating between C-Ku bands. Various selective resonant bands are achieved by varying the orientation of helix with respect to the walls inside the designed cavity and thus making the design reconfigurable in nature. The antenna has a highest bandwidth of 2.77 GHz and a peak gain of 10.8 dB.
PAST, PRESENT AND FUTURE ACTIVITIES FOR RADIATION EFFECTS TESTING AT JULIC/COSY

The testing of radiation effects (displacement damage, single event effects) with energetic protons for electronics used in space and accelerators is of growing importance. Setup and past experience of a dedicated test stand used by Fraunhofer INT at the JULIC cyclotron will be presented. During solar proton events, as well as at high energy accelerators (CERN, FAIR), electronics are confronted with protons of much higher energy. Recent scientific studies have shown that for single event upsets as well as destructive failures (e.g, single event latch-ups) a cross section measured at energies in the tens or one/two-hundred MeV range (e.g. PIF@PSI) can significantly underestimate the failure rate. To avoid unnecessary high safety margins there is a growing need for the opportunity to test electronics at several GeV, like the beam provided by the Cooler-Synchrotron COSY in Julich.
Operational and Performance Aspects of a Turn-Around Tracking System

Results obtained using operational four-way TAR data are analyzed and compared to results obtained with both 2-station range measurements and 1-station range and angle measurements. Four-way turn around ranging (TAR) is currently in use operationally by Measat for their new satellite, M-3a (formerly M-1R). The orbital accuracies being obtained for the TAR are similar to ones obtained using classical 2-station ranging when the same two stations used for the TAR are compared. TAR does not make the satellite operations more complex however there are slightly different calibrations that need to be performed. Several cases are considered, including free space propagation and maneuver estimation, both with solar radiation pressure scale factor estimation. The test cases were analyzed using GMV’s focusGEO software. The tracking scenario using the full set of tracking measurement including TAR, range, and angles yields the baseline for orbital accuracies used for comparison to the other TAR scenarios. The TAR only orbit determination produces an orbit determination that is on par with the full measurement set solution as does the TAR plus range case. Therefore the use of TAR measurements in place of more costly scenarios is a viable option for GEO satellites. The test case demonstrate focusGEO’s ability to consistently mix tracking data types while estimating dynamic parameters and produce a reliable orbit determination solution from TAR measurements independent of the ground stations producing the raw measurements.
Design of a Wideband Balun Using IPD Technology for Compensating the Frequency Dependency

In this paper, we propose a wideband balun using integrated passive device (IPD) technology. The proposed circuit is designed to compensate for the magnitude deviations by differentiating the magnitude S-parameter and calculating the lumped component value when the derivative value is zero. For a smaller size, the proposed circuit is implemented using IPD technology, and the fabricated circuit’s size is 2.4 mm × 1.02 mm. The magnitude of the measurement results has 3.5 ± 0.3 dB deviations from 2.8–4.58 GHz, and the phase difference has 180° ± 10° from 2.8–5 GHz at the center frequency of 3.6 GHz. Therefore, the measurement results show that the theory of the proposed circuit is well matched compared with the measurement results for smaller sizes, wideband magnitude, and phase characteristics.
Fabrication of Nanoformulation Containing Carvedilol and Silk Protein Sericin against Doxorubicin Induced Cardiac Damage in Rats

Nanotechnology has emerged as an inspiring tool for the effective delivery of drugs to help treat Coronary heart disease (CHD) which represents the most prevalent reason for mortality and morbidity globally. The current study focuses on the assessment of the cardioprotective prospective ofanovel combination nanoformulation of sericin and carvedilol. Sericin is a silk protein obtained from Bombyx mori cocoon and carvedilol is a synthetic nonselective β-blocker. In this present study, preparation of chitosan nanoparticles was performed via ionic gelation method and were evaluated for cardioprotective activity in doxorubicin (Dox)-induced cardiotoxicity. Serum biochemical markers of myocardial damage play a substantial role in the analysis of cardiovascular ailments and their increased levels have been observed to be significantly decreased in treatment groups. Treatment groups showed a decline in the positivity frequency of the Troponin T test as well. The NTG (Nanoparticle Treated Group), CSG (Carvedilol Standard Group), and SSG (Sericin Standard Group) were revealed to have reduced lipid peroxide levels (Plasma and heart tissue) highly significantly at a level of p < 0.01 in comparison with the TCG (Toxic Control Group). Levels of antioxidants in the plasma and the cardiac tissue were also established to be within range of the treated groups in comparison to TCG. Mitochondrial enzymes in cardiac tissue were found to be elevated in treated groups. Lysosomal hydrolases accomplish a significant role in counteracting the inflammatory pathogenesis followed by disease infliction, as perceived in the TCG group. These enzyme levels in the cardiac tissue were significantly improved after treatment with the nanoformulation. Total collagen content in the cardiac tissue of the NTG, SSG, and CSG groups was established to be highly statistically significant at p < 0.001 as well as statistically significant at p < 0.01, respectively. Hence, the outcomes of this study suggest that the developed nanoparticle formulation is effective against doxorubicin-induced cardiotoxicity.
IRAK2, an Immune and Radiation-Response Gene, Correlates with Advanced Disease Features but Predicts Higher Post-Irradiation Local Control in Non-Metastatic and Resected Oral Cancer Patients

Gene Ontology (GO) analysis can provide a comprehensive function analysis for investigating genes, allowing us to identify the potential biological roles of genes. The present study conducted GO analysis to explore the biological function of IRAK2 and performed a case analysis to define its clinical role in disease progression and mediating tumor response to RT. Methods: We performed a GO enrichment analysis on the RNA-seq data to validate radiation-induced gene expression. A total of 172 I-IVB specimens from oral squamous cell carcinoma patients were collected for clinical analysis, from which IRAK2 expression was analyzed by immunohistochemistry. This was a retrospective study conducted between IRAK2 expression and the outcomes of oral squamous cell carcinoma patients after radiotherapy treatment. We conducted Gene Ontology (GO) analysis to explore the biological function of IRAK2 and performed a case analysis to define its clinical role in mediating tumor response to radiotherapy. GO enrichment analysis to validate radiation-induced gene expression was performed. Clinically, 172 stage I-IVB resected oral cancer patients were used to validate IRAK2 expression in predicting clinical outcomes. GO enrichment analysis showed that IRAK2 is involved in 10 of the 14 most enriched GO categories for post-irradiation biological processes, focusing on stress response and immune modulation. Clinically, high IRAK2 expression was correlated with adverse disease features, including pT3-4 status (p = 0.01), advanced overall stage (p = 0.02), and positive bone invasion (p = 0.01). In patients who underwent radiotherapy, the IRAK2-high group was associated with reduced post-irradiation local recurrence (p = 0.025) compared to the IRAK2-low group. IRAK2 plays a crucial role in the radiation-induced response. Patients with high IRAK2 expression demonstrated more advanced disease features but predicted higher post-irradiation local control in a clinical setting. These findings support IRAK2 as a potential predictive biomarker for radiotherapy response in non-metastatic and resected oral cancer patients.
Design of a Computational Microspectrometer Based on Metasurfaces and Multilayer Thin Films

A computational microspectrometer consisting of a planar array of broadband optical filters is proposed, which are formed by low-loss dielectric metasurfaces and multilayer thin films (MTF). The rich features in the spectral responses of the filters increase the spectral diversity to improve reconstruction performance. By applying compressive sensing (CS), the incident spectrum can be reconstructed. Simulation results show the proposed design can successfully reconstruct the spectra over the 401 to 600 nm spectral range with a resolution of 2 nm. The design provides a feasible solution to miniaturization of spectrometers.
Emergent Schwarzschild : symplectic, geometric and topological perspective

In the bottom-up approach of emergent gravity we attempt to find out symplectic gauge fields emerging from Euclidean Schwarzschild metric which is a Ricci flat manifold but non-K\"ahler. The Euclidean Schwarzschild solution is studied extensively as electromagnetism defined on the symplectic space $(M,\omega)$. A detailed geometrical engineering with the emergent metric help us set up the Seiberg Witten map between commutative and non- commutative gauge fields and further paves the path for the evaluation of the topological invariants in terms of gauge theory quantities.