Miracle-dz/UniFAR-FTUs huggingface dataset:
CNN-Assisted Steganography - Integrating Machine Learning with Established Steganographic Techniques

We propose a method to improve steganography by increasing the resilience of stego-media to discovery through steganalysis. Our approach enhances a class of steganographic approaches through the inclusion of a steganographic assistant convolutional neural network (SA-CNN). Previous research showed success in discovering the presence of hidden information within stego-images using trained neural networks as steganalyzers that are applied to stego-images. Our results show that such steganalyzers are less effective when SA-CNN is employed during the generation of a stego-image. We also explore the advantages and disadvantages of representing all the possible outputs of our SA-CNN within a smaller, discrete space, rather than a continuous space. Our SA-CNN enables certain classes of parametric steganographic algorithms to be customized based on characteristics of the cover media in which information is to be embedded. Thus, SA-CNN is adaptive in the sense that it enables the core steganographic algorithm to be especially configured for each particular instance of cover media. Experimental results are provided that employ a recent steganographic technique, S-UNIWARD, both with and without the use of SA-CNN. We then apply both sets of stego-images, those produced with and without SA-CNN, to an exmaple steganalyzer, Yedroudj-Net, and we compare the results. We believe that this approach for the integration of neural networks with hand-crafted algorithms increases the reliability and adaptability of steganographic algorithms.
Spectral Energy Distributions for 258 Local Volume Galaxies

We present model spectral energy distribution (SED) fits to ultraviolet/optical/infrared observations for the 258 nearby galaxies in the Local Volume Legacy survey, a sample dominated by lower-luminosity dwarf irregular systems. The data for each galaxy include up to 26 spatially-integrated broadband and narrowband fluxes from the Galaxy Evolution Explorer, Spitzer Space Telescope, and Infrared Astronomical Satellite space-based platforms, and from the Sloan Digital Sky Survey, Two Micron All Sky Survey, and other ground-based efforts. The CIGALE SED fitting package is employed using a delayed star-formation history with an optional late burst or quenching episode to constrain 11 different free parameters that characterize the properties of each galaxy’s stellar and dust emission, with the overriding constraint that the ultraviolet/optical emission absorbed by interstellar dust grains is emitted in equal energy portions at infrared wavelengths. The main results are: (i) 94% of the SED fits yield reduced χ 2 values less than 3; (ii) the modeled stellar masses agree with those derived from 3.6 μm-based measures with a scatter of 0.07 dex; (iii) for a typical galaxy in the sample the SED-derived star-formation rate averaged over the past 100 Myr is about 88% of the value derived from standard hybrid indicators on similar timescales; and (iv) there is a statistically significant inverse relation between the stellar mass fraction appearing in the late burst and the total stellar mass. These results build upon prior SED modeling efforts in the local volume and lay the groundwork for future studies of more distant low-metallicity galaxies with JWST.
Learnable Pillar-based Re-ranking for Image-Text Retrieval

Image-text retrieval aims to bridge the modality gap and retrieve cross-modal content based on semantic similarities. Prior work usually focuses on the pairwise relations (i.e., whether a data sample matches another) but ignores the higher-order neighbor relations (i.e., a matching structure among multiple data samples). Re-ranking, a popular post-processing practice, has revealed the superiority of capturing neighbor relations in single-modality retrieval tasks. However, it is ineffective to directly extend existing re-ranking algorithms to image-text retrieval. In this paper, we analyze the reason from four perspectives, i.e., generalization, flexibility, sparsity, and asymmetry, and propose a novel learnable pillar-based re-ranking paradigm. Concretely, we first select top-ranked intra- and intermodal neighbors as pillars, and then reconstruct data samples with the neighbor relations between them and the pillars. In this way, each sample can be mapped into a multimodal pillar space only using similarities, ensuring generalization. After that, we design a neighbor-aware graph reasoning module to flexibly exploit the relations and excavate the sparse positive items within a neighborhood. We also present a structure alignment constraint to promote crossmodal collaboration and align the asymmetric modalities. On top of various base backbones, we carry out extensive experiments on two benchmark datasets, i.e., Flickr30K and MS-COCO, demonstrating the effectiveness, superiority, generalization, and transferability of our proposed re-ranking paradigm.
Evolution of resonant self-interacting dark matter halos

Recent analysis on the stellar kinematics of ultra-faint dwarf (UFD) galaxies has put a stringent upper limit on the self-scattering cross section of dark matter, i.e., $\sigma/m<{\cal O}(0.1)\,{\rm cm^2/g}$ at the scattering velocity of ${\cal O}(10)\,{\rm km/s}$. Resonant self-interacting dark matter (rSIDM) is one possibility that can be consistent with the UFDs and explain the low central densities of rotation-supported galaxies; the cross section is resonantly enhanced to be $\sigma/m = {\cal O}(1)\,{\rm cm^2/g}$ around the scattering velocity of ${\cal O}(100)\,{\rm km/s}$ while being suppressed at lower velocities. To further assess this possibility, since the inferred dark matter distribution of halos from astrophysical observations is usually compared to that in constant-cross section SIDM (cSIDM), whether the structures of rSIDM halos can be approximated by the cSIDM halo profiles needs to be clarified. In this work, we employ the grovothermal fluid method to investigate the structural evolution of rSIDM halos in a wide mass range. We find that except for halos in a specific mass range, the present structures of rSIDM halos are virtually indistinguishable from those of the cSIDM halos. For halos in the specific mass range, the resonant self-scattering renders a break in their density profile. We demonstrate how such a density-profile break appears in astrophysical observations, e.g., rotation curves and line-of-sight velocity dispersion profiles. We show that for halos above the specific mass range, the density-profile break thermalizes to disappear before the present. We demonstrate that such distinctive thermalization dynamics can leave imprints on the orbital classes of stars with similar ages and metallicities.
Evaluating Inter-Bilingual Semantic Parsing for Indian Languages

Despite significant progress in Natural Language Generation for Indian languages (IndicNLP), there is a lack of datasets around complex structured tasks such as semantic parsing. One reason for this imminent gap is the complexity of the logical form, which makes English to multilingual translation difficult. The process involves alignment of logical forms, intents and slots with translated unstructured utterance. To address this, we propose an Inter-bilingual Seq2seq Semantic parsing dataset IE-SemParse Suite for 11 distinct Indian languages. We highlight the proposed task’s practicality, and evaluate existing multilingual seq2seq models across several train-test strategies. Our experiment reveals a high correlation across performance of original multilingual semantic parsing datasets (such as mTOP, multilingual TOP and multiATIS++) and our proposed IE-SemParse suite.
Moving an Atom towards Right or Left Side by Applying Quantum Mechanical Matter Wave Near a Surface

The area of trapping the atoms or molecules using light has advanced tremendously in the last few decades. In contrast, the idea of controlling (not only trapping) the movement of atomic-sized particles using quantum mechanical matter waves is a completely new emerging area of particle manipulation. Though a single previous report has suggested the pulling of atoms based on matter wave tractor beams, an attempt is yet to be made to produce a lateral force (moving the atoms towards left or right) using this technique. This article demonstrates a matter wave-based manipulation scenario that gives rise to reversible lateral force on an atom due to the interaction energy of the quantum mechanical matter wave in the presence of a metal surface creating an asymmetrical set-up. For a symmetric set-up, no lateral force has been observed. We have performed several full wave simulations and analytical calculations on a particular set-up of Xenon scatterer atoms placed near a Copper surface, with two plane matter waves of Helium impinging in the direction parallel to the surface from two sides of the scatterer. By solving the time-independent Schrodinger equation and using the solution, quantum mechanical stress tensor formalism has been applied to compute the force acting on the particle. The full wave simulation results have been found in excellent agreement with the analytical calculations. The results for the adsorbed scatterer case suggests that our proposed technique can be an efficient cleaning procedure similar to electron-stimulated desorption for futuristic applications.
Improved Trust in Human-Robot Collaboration With ChatGPT

Human-robot collaboration is becoming increasingly important as robots become more involved in various aspects of human life in the era of Artificial Intelligence. However, the issue of human operators’ trust in robots remains a significant concern, primarily due to the lack of adequate semantic understanding and communication between humans and robots. The emergence of Large Language Models (LLMs), such as ChatGPT, provides an opportunity to develop an interactive, communicative, and robust human-robot collaboration approach. This paper explores the impact of ChatGPT on trust in a human-robot collaboration assembly task. This study designs a robot control system called RoboGPT using ChatGPT to control a 7-degree-of-freedom robot arm to help human operators fetch, and place tools, while human operators can communicate with and control the robot arm using natural language. A human-subject experiment showed that incorporating ChatGPT in robots significantly increased trust in human-robot collaboration, which can be attributed to the robot’s ability to communicate more effectively with humans. Furthermore, ChatGPT’s ability to understand the nuances of human language and respond appropriately helps to build a more natural and intuitive human-robot interaction. The findings of this study have significant implications for the development of trustworthy human-robot collaboration systems.
Hyperproperty Verification as CHC Satisfiability

Hyperproperties govern the behavior of a system or systems across multiple executions, and are being recognized as an important extension of regular temporal properties. So far, such properties have resisted comprehensive treatment by modern software model-checking approaches such as IC3/PDR, due to the need to find not only an inductive invariant but also a \emph{total} alignment of different executions that facilitates simpler inductive invariants. We show how this treatment is achieved via a reduction from the verification problem of $\forall^k\exists^l$ properties to Constrained Horn Clauses. The approach is based on combining the inference of an alignment and inductive invariant in a single CHC encoding; and, for existential quantification over traces, incorporating also inference of a witness function for the existential choices, based on a game semantics with a sound-and-complete encoding to CHCs as well.
GREAD: Graph Neural Reaction-Diffusion Networks

Graph neural networks (GNNs) are one of the most popular research topics for deep learning. GNN methods typically have been designed on top of the graph signal processing theory. In particular, diffusion equations have been widely used for designing the core processing layer of GNNs, and therefore they are inevitably vulnerable to the notorious oversmoothing problem. Recently, a couple of papers paid attention to reaction equations in conjunctions with diffusion equations. However, they all consider limited forms of reaction equations. To this end, we present a reaction-diffusion equation-based GNN method that considers all popular types of reaction equations in addition to one special reaction equation designed by us. To our knowledge, our paper is one of the most comprehensive studies on reaction-diffusion equation-based GNNs. In our experiments with 9 datasets and 28 baselines, our method, called GREAD, outperforms them in a majority of cases. Further synthetic data experiments show that it mitigates the oversmoothing problem and works well for various homophily rates.
Mesospheric dynamics and ground-layer optical turbulence studies for the performance of ground-based telescopes

Modern astronomical instrumentation employs adaptive optics (AO) systems that correct for atmospheric distortion in real time in order to produce sharper images. The design and performance of these systems relies on the knowledge of the atmosphere both at low and high altitude. This thesis investigates the first kilometer of the atmosphere, the ground layer (GL), as well as the sodium layer at ∼92 km. Newly-designed lunar scintillometers provide turbulence profiles of the GL, and high spatio-temporally resolved sodium profiles are obtained using a newly-designed lidar system for UBC’s 6-m liquidmirror. For ground layer adaptive optics systems, knowledge of the local heightand time-resolved GL turbulence is crucial to link local topography to optical turbulence and has been obtained with the help of three lunar scintillometers deployed in Chile, Hawaii and in the Canadian High Arctic. Results from measurements inside the Canada-France-Hawaii Telescope (CFHT) dome indicate severe degradation of image quality due to a poorly vented dome and thus provide input for dome modifications. The outside median GL seeing was determined to be 0.48±0.01”. Initial results from the Arctic show exceptional GL seeing conditions, better than have been found anywhere on Earth although data quantity is limited. Extremely large telescopes must correct not only for GL turbulence, but also higher atmospheric disturbances in the troposphere. The use of laser guide stars (LGS) increases sky coverage and the field of view, but relies on resonantly excited sodium atoms in
Effects of Exergaming on Musculoskeletal Pain in Older Adults: Systematic Review and Meta-analysis

Background Exercise is effective for musculoskeletal pain. However, physical, social, and environmental factors make it difficult for older adults to persist in exercising. Exergaming is a new pathway that combines exercise with gameplay and may be helpful for older adults to overcome these difficulties and engage in regular exercise. Objective This systematic review aimed to determine the efficacy of exergaming to improve musculoskeletal pain in older adults. Methods The search was performed in 5 databases (PubMed, Embase, CINAHL, Web of Science, and Cochrane Library). The risk of bias for randomized controlled studies was assessed using the revised Cochrane Risk of Bias tool in randomized trials (RoB 2), and the methodological quality was assessed using the Physiotherapy Evidence-Based Database scale. Standardized mean difference and 95% CI were calculated using fixed-effects model meta-analyses in the Review Manager version 5.3 (RevMan 5.3). Results Seven randomized controlled studies were included, which contained 264 older adults. Three of the 7 studies reported significant improvements in pain after the exergaming intervention, but only 1 reported a significant difference between groups after adjustment for baseline (P<.05), and another reported a significant improvement in thermal pain between the 2 groups (P<.001). The results of the meta-analysis of the 7 studies showed no statistically significant improvement in pain compared to the control group (standardized mean difference –0.22; 95% CI –0.47 to 0.02; P=.07). Conclusions Although the effects of exergames on musculoskeletal pain in older adults are unknown, exergame training is generally safe, fun, and appealing to older adults. Unsupervised exercise at home is feasible and cost-effective. However, most of the current studies have used commercial exergames, and it is recommended that there should be more cooperation between industries in the future to develop professional rehabilitation exergames that are more suitable for older adults. The sample sizes of the studies included are small, the risk of bias is high, and the results should be interpreted with caution. Further randomized controlled studies with large sample sizes, high quality, and rigor are needed in the future. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42022342325; https://www.crd. york.ac. uk/prospero/display_record. php? RecordID=342325
The impact of Danmaku-based and synchronous peer feedback on L2 oral performance: A mixed-method investigation

Advancement of research in education has propelled the augmentation of theoretical and practical knowledge in learning-oriented feedback. In recent years, the channels, modes, and orientations of feedback became manifold. Copious empirical evidence from the body of literature supported the strength of feedback in enhancing learning outcomes and promoting the motivation of learners. However, compared to the popularity in implementation and fruitfulness of findings in other educational domains, the application of state-of-the-art technology-enhanced feedback in fostering students’ L2 oral abilities remain few and far between. To address the knowledge gap, the present study endeavored to investigate the effect of Danmaku-based and synchronous peer feedback on L2 oral performance and the acceptance thereof among students. Adopting a mixed-method design, the study recruited 74 (n = 74) undergraduate English majors from a Chinese university for a 16-week 2x2 experiment. The collected data were analyzed through statistical and thematic analysis respectively. The findings revealed that Danmaku-based and synchronous peer feed-back was impactful on students’ performance in L2 oral production. Furthermore, the impacts of peer feedback on subdomains of L2 competence were statistically analyzed. Regarding students’ perceptions, the incorporation of peer feedback was generally favored by participants who were satisfied and motivated in the learning process but lacked confidence in their assessment literacy. Furthermore, students expressed their agreement with the benefit of reflective learning and the subsequent enrichment in knowledge and horizon. The research was significant for its conceptual and practical contribution for follow-up researchers and educators in L2 education and learning-oriented feedback.
Patient-, Clinician-, and Institution-level Variation in Inotrope Use for Cardiac Surgery: A Multicenter Observational Analysis

Background: Conflicting evidence exists regarding the risks and benefits of inotropic therapies during cardiac surgery, and the extent of variation in clinical practice remains understudied. Therefore, the authors sought to quantify patient-, anesthesiologist-, and hospital-related contributions to variation in inotrope use. Methods: In this observational study, nonemergent adult cardiac surgeries using cardiopulmonary bypass were reviewed across a multicenter cohort of academic and community hospitals from 2014 to 2019. Patients who were moribund, receiving mechanical circulatory support, or receiving preoperative or home inotropes were excluded. The primary outcome was an inotrope infusion (epinephrine, dobutamine, milrinone, dopamine) administered for greater than 60 consecutive min intraoperatively or ongoing upon transport from the operating room. Institution-, clinician-, and patient-level variance components were studied. Results: Among 51,085 cases across 611 attending anesthesiologists and 29 hospitals, 27,033 (52.9%) cases received at least one intraoperative inotrope, including 21,796 (42.7%) epinephrine, 6,360 (12.4%) milrinone, 2,000 (3.9%) dobutamine, and 602 (1.2%) dopamine (non–mutually exclusive). Variation in inotrope use was 22.6% attributable to the institution, 6.8% attributable to the primary attending anesthesiologist, and 70.6% attributable to the patient. The adjusted median odds ratio for the same patient receiving inotropes was 1.73 between 2 randomly selected clinicians and 3.55 between 2 randomly selected institutions. Factors most strongly associated with increased likelihood of inotrope use were institutional medical school affiliation (adjusted odds ratio, 6.2; 95% CI, 1.39 to 27.8), heart failure (adjusted odds ratio, 2.60; 95% CI, 2.46 to 2.76), pulmonary circulation disorder (adjusted odds ratio, 1.72; 95% CI, 1.58 to 1.87), loop diuretic home medication (adjusted odds ratio, 1.55; 95% CI, 1.42 to 1.69), Black race (adjusted odds ratio, 1.49; 95% CI, 1.32 to 1.68), and digoxin home medication (adjusted odds ratio, 1.48; 95% CI, 1.18 to 1.86). Conclusions: Variation in inotrope use during cardiac surgery is attributable to the institution and to the clinician, in addition to the patient. Variation across institutions and clinicians suggests a need for future quantitative and qualitative research to understand variation in inotrope use affecting outcomes and develop evidence-based, patient-centered inotrope therapies. While presenting patient comorbidities and characteristics are related to variability in inotrope use during cardiac surgery with cardiopulmonary bypass, the institution where surgery is performed and the attending anesthesiologist caring for the cardiac surgical patient are also factors associated with increased variability in inotrope usage.
Acute Effects of Whole-Body Electrostimulation Combined with Stretching on Lower Back Pain

Abstract Data from recent studies suggest that whole-body electromyostimulation (WB-EMS) is a time-effective and tailored intervention for chronic lower back pain (CLBP). The aim of this non-randomized controlled study was to compare the efficacy of a WB-EMS training and the association between WB-EMS specific training with passive stretching (Well Back System, [WBS]) on CLBP. Forty patients with CLBP, 43–81 years old, were assigned to one of the two groups: WB-EMS (n=20) and WB-EMS+WBS (n=20). Both groups completed 12 sessions (8 weeks) of the WB-EMS protocol (2×20 minutes/week). The second group performed core-specific exercises with WB-EMS plus 6 extra stretching sessions (30 minutes each). Primary study endpoints were based on changes on the visual analogue scale (VAS) and changes on the Oswestry Low Back Disability Questionnaire (ODI). Secondary study endpoints were percentage changes of maximum trunk flexion (Sit & Reach, [SR]) and changes in consumption of painkillers. Both interventions significantly improved VAS, ODI, and SR values ( p range: 0.04;<0.001). However, the change of VAS (–46% vs. –17%, p <0.001), ODI (–53% vs. –17%, p <0.001), and SR (+ 7 vs.+3 cm, p =0.001) were significantly higher in the WB-EMS+WBS group compared to the WB-EMS group. The working method with WB-EMS+WBS can be a joint-friendly, individualized form to decrease CLBP.