Miracle-dz/UniFAR-FTUs huggingface dataset:
Branch-and-Pruning Optimization Towards Global Optimality in Deep Learning

It has been attracting more and more attention to understand the global optimality in deep learning (DL) recently. However, conventional DL solvers, have not been developed intentionally to seek for such global optimality. In this paper, we propose a novel approximation algorithm, BPGrad , towards optimizing deep models globally via branch and pruning. The proposed BPGrad algorithm is based on the assumption of Lipschitz continuity in DL, and as a result, it can adaptively determine the step size for the current gradient given the history of previous updates, wherein theoretically no smaller steps can achieve the global optimality. We prove that, by repeating such a branch-and-pruning procedure, we can locate the global optimality within ﬁnite iterations. Empirically an efﬁcient adaptive solver based on BPGrad for DL is proposed as well, and it outperforms conventional DL solvers such as Adagrad, Adadelta, RMSProp, and Adam in the tasks of object recognition, detection, and segmentation. The code is available at https://github.com/RyanCV/BPGrad .
Modeling Censored Mobility Demand through Quantile Regression Neural Networks

Shared mobility services require accurate demand models for effective service planning. On one hand, modeling the full probability distribution of demand is advantageous, because the full uncertainty structure preserves valuable information for decision making. On the other hand, demand is often observed through usage of the service itself, so that the observations are censored , as they are inherently limited by available supply. Since the 1980s, various works on Censored Quantile Regression models have shown them to perform well under such conditions, and in the last two decades, several works have proposed to implement them ﬂexibly through Neural Networks (CQRNN). However, apparently no works have yet applied CQRNN in the Transport domain. We address this gap by applying CQRNN to datasets from two shared mobility providers in the Copenhagen metropolitan area in Denmark, as well as common synthetic baseline datasets. The results show that CQRNN can estimate the intended distributions better than both censorship-unaware models and parametric censored models.
A DMP-based Framework for Efficiently Generating Complete Stiffness Profiles of Human-like Variable Impedance Skill from Demonstrations

Human manipulation skills can be transferred to robots conveniently through learning from demonstrations (LfD) methods. However, most of these works either only encode motion trajectories or suﬀer from the complexity and incompleteness when estimating stiﬀness proﬁles. To solve these problems, we propose a simple and eﬀective stiﬀness estimation method that estimates a complete end-eﬀector stiﬀness matrix from the variation of demonstrations. To that end, Gaussian Mixture Regression (GMR) is applied to extract the reference pose trajectory and the variability. Quaternion logarithmic map is integrated to generate complete rotational stiﬀness. Besides, the Dynamic Movement Primitives (DMPs) model is further developed to encode and schedule both the movement trajectory and stiﬀness proﬁles in task space simultaneously. Finally, the effectiveness of our approach is validated on a real-world 7 DoF robot with the variable impedance controller.
Cross-Validated Tuning of Shrinkage Factors for MVDR Beamforming Based on Regularized Covariance Matrix Estimation

This paper considers the regularized estimation of covariance matrices (CM) of high-dimensional (compound) Gaussian data for minimum variance distortionless response (MVDR) beamforming. Linear shrinkage is applied to improve the accuracy and condition number of the CM estimate for low-sample-support cases. We focus on data-driven techniques that automatically choose the linear shrinkage factors for shrinkage sample covariance matrix ($\text{S}^2$CM) and shrinkage Tyler's estimator (STE) by exploiting cross validation (CV). We propose leave-one-out cross-validation (LOOCV) choices for the shrinkage factors to optimize the beamforming performance, referred to as $\text{S}^2$CM-CV and STE-CV. The (weighted) out-of-sample output power of the beamfomer is chosen as a proxy of the beamformer performance and concise expressions of the LOOCV cost function are derived to allow fast optimization. For the large system regime, asymptotic approximations of the LOOCV cost functions are derived, yielding the $\text{S}^2$CM-AE and STE-AE. In general, the proposed algorithms are able to achieve near-oracle performance in choosing the linear shrinkage factors for MVDR beamforming. Simulation results are provided for validating the proposed methods.
Citations and gender diversity in reciprocal acknowledgement networks

Acknowledgements in scientific articles suggest not only gratitude, but also the interactions among scientists. In this study, we examine the acknowledgement interactions employing data from open-access journals (PLOS series). We built an acknowledgement network where the nodes represent authors and acknowledged people, while the links correspond to being mentioned in acknowledgements. Employing motif analysis, we showed how acknowledgement networks have developed, and how reciprocal relationships tend to emerge. To better understand these reciprocal relationships, we analysed the reciprocal sub-graphs of acknowledgement from two perspectives: citations and gender diversity. Firstly, we counted the number of citations, from both reciprocal and non-reciprocal authors. We found that reciprocal authors predominantly tend to cite other reciprocal authors rather than non-reciprocal ones. For gender diversity, we found that reciprocal pairs that include females tend to emerge more than male-male pairs of reciprocity in various fields, despite the fewer number of females.
Intersection Types for a Computational Lambda-Calculus with Global State

We study the semantics of an untyped lambda-calculus equipped with operators representing read and write operations from and to a global store. We adopt the monadic approach to model side-effects and treat read and write as algebraic operations over a monad. We introduce operational and denotational semantics and a type assignment system of intersection types and prove that types are invariant under the reduction and expansion of term and state conﬁgurations. Finally, we characterize convergent terms via their typings.
Removing Pixel Noises and Spatial Artifacts with Generative Diversity Denoising Methods

Image denoising and artefact removal are complex inverse problems admitting many potential solutions. Variational Autoencoders ( VAE s) can be used to learn a whole distribution of sensible solutions, from which one can sample efﬁciently. However, such a generative approach to image restoration is only studied in the context of pixel-wise noise removal (e.g. Poisson or Gaussian noise). While important, a plethora of application domains suffer from imaging artefacts (structured noises) that alter groups of pixels in correlated ways. In this work we show, for the ﬁrst time, that generative diversity denoising (GDD) approaches can learn to remove structured noises without supervision. To this end, we investigate two existing GDD architectures, introduce a new one based on hierarchical VAE s, and compare their performances against a total of seven state-of-the-art baseline methods on ﬁve sources of structured noise (including tomography reconstruction artefacts and microscopy artefacts). We ﬁnd that GDD methods out-perform all unsupervised baselines and in many cases not lagging far behind supervised results (in some occasions even superseding them). In addition to structured noise removal, we also show that our new GDD method produces new state-of-the-art (SOTA) results on seven out of eight benchmark datasets for pixel-noise removal. Finally, we offer insights into the daunting question of how GDD methods distinguish structured noise, which we like to see removed, from image signals, which we want to see retained.
Covariance Estimation under One‐bit Quantization

We consider the classical problem of estimating the covariance matrix of a subgaussian distribution from i.i. d. samples in the novel context of coarse quantization, i.e., instead of having full knowledge of the samples, they are quantized to one or two bits per entry. This problem occurs naturally in signal processing applications. We introduce new estimators in two different quantization scenarios and derive non‐asymptotic estimation error bounds in terms of the operator norm. In the first scenario we consider a simple, scale‐invariant one‐bit quantizer and derive an estimation result for the correlation matrix of a centered Gaussian distribution. In the second scenario, we add random dithering to the quantizer. In this case we can accurately estimate the full covariance matrix of a general subgaussian distribution by collecting two bits per entry of each sample. In both scenarios, our bounds apply to masked covariance estimation as well. We demonstrate the near‐optimality of our error bounds by deriving corresponding (minimax) lower bounds and using numerical simulations.
Robust optimization Design of a New Combined Median Barrier Based on Taguchi method and Grey Relational Analysis

Yupeng Huang , Song Yao , Peng Chen , Zhengbao Lei , Xinzhong Gan , Youwei Gan c Key Laboratory of Traffic Safety on Track, Ministry of Education, School of Traffic and Transportation Engineering, Central South University, Hunan Changsha, 410075, China Large-scale Structural Impact Laboratory, Changsha University of Science and Technology, Hunan Changsha, 410114, China Yichun Highway Administration, Jiangxi Yichun, 336000, China Jiangxi Transportation Design and Research Institute Co., Ltd., Jiangxi Nanchang, 330052, China Abstract:Accidents that vehicles cross median and enter opposite lane happen frequently, and the existing median barrier has weak anti-collision strength. A new combined median barrier (NCMB) consisted of W-beam guardrail and concrete structure was proposed to decrease deformation and enhance anti-collision strength in this paper. However, there were some uncertainties in the initial design of the NCMB. If the uncertainties were not considered in the design process, the optimization objectives were especially sensitive to the small fluctuation of the variables, and it might result in design failure. For this purpose, the acceleration and deflection were taken as objectives; post thickness, W-beam thickness and post spacing were chosen as design variables; the velocity, mass of vehicle and the yield stress of barrier components were taken as noise factors, a multi-objective robust optimization is carried out for the NCMB based on Taguchi and grey relational analysis (GRA). The results indicate that the acceleration and deflection after optimization are reduced by 47.3% and 76.7% respectively; Signal-to-noise ratio (SNR) of objectives after optimization are increased, it greatly enhances the robustness of the NCMB. The results demonstrate that the effectiveness of the methodology that based on Taguchi method and grey relational analysis.
Influence of PEG molecular masses on electrospinning of new multiblock terpoly(ester-ether-ester)s

With the growing progress in tissue engineering, the search for new scaffolding materials has recently intensified considerably. Focusing on this, we synthesized hydrophilic–hydrophobic terpolymers containing poly(ethylene glycol) (PEG) and two different polyester units: butylene terephthalate and butylene linoleate. The spinnability of the materials was verified by the electrospinning process. The influence of different molecular masses of PEG on thermal and mechanical properties as well as on electrospinning of new terpoly(ester-ether-ester) elastomeric materials was investigated. The thermal properties data revealed the crystallization of soft segments in materials containing PEG of high molecular mass (4600 g/mol). Increased crystallinity contributed to increased mechanical properties of terpolymers. We also investigated the effect of the temperature transitions and the degree of crystallinity on the electrospinning process for nanofiber preparation. It was found that polymers containing crystallizable soft segments of PEG4600 are more suitable for electrospinning compared to terpolymers (TEEE) containing PEG1000.
PTP: Parallelized Tracking and Prediction With Graph Neural Networks and Diversity Sampling

Multi-object tracking (MOT) and trajectory prediction are two critical components in modern 3D perception systems that require accurate modeling of multi-agent interaction. We hypothesize that it is beneficial to unify both tasks under one framework in order to learn a shared feature representation of agent interaction. Furthermore, instead of performing tracking and prediction sequentially which can propagate errors from tracking to prediction, we propose a parallelized framework to mitigate the issue. Also, our parallel track-forecast framework incorporates two additional novel computational units. First, we use a feature interaction technique by introducing Graph Neural Networks (GNNs) to capture the way in which agents interact with one another. The GNN is able to improve discriminative feature learning for MOT association and provide socially-aware contexts for trajectory prediction. Second, we use a diversity sampling function to improve the quality and diversity of our forecasted trajectories. The learned sampling function is trained to efficiently extract a variety of outcomes from a generative trajectory distribution and helps avoid the problem of generating duplicate trajectory samples. We evaluate on KITTI and nuScenes datasets showing that our method with socially-aware feature learning and diversity sampling achieves new state-of-the-art performance on 3D MOT and trajectory prediction. Project website is: http://www.xinshuoweng. com/projects/PTP.
Microbiological and physicochemical assessments of groundwater quality at Punjab, Pakistan

The assessment of groundwater is essential for the estimation of suitability of water for safe use. An attempt has been made to study the groundwater at the district level of Punjab, Pakistan. These samples were analyzed for various water quality parameters like pH, color, odor, conductance, total suspended solids, trace metals (Fe, cu, B, Ba, Al, Cr, Cd, Ni, Mn and Se), ionic concentration (HCO3, CO3, Cl, SO4, Na, K, Ca, Mg, NO3, NO2, NH4, F, PO4 and CN) and for microbiological enumeration (total viable count, total and fecal coliforms Escherichia coli, Salmonella spp. and Pseudomonas spp.). The data was analyzed with WHO guidelines/ recommendations. The results of physical analysis indicated that all samples are safe except the groundwater of Kasur and Khanewal District. About 66.67% samples are out of total suspended solids (TSS) limit. Microbiologically, only six groundwater of Punjab districts are found potable according to WHO limits. In the trace metals analysis, highest level of iron was detected in Jhang while the groundwater of three districts were not potable due to high level of boron and nickel but the groundwater of all districts was found safe with respect to Ba, Al and Cr. As far as ionic concentration is concerned, 11 districts were found not to be within WHO limit in the case of bicarbonates, seven in phosphates, five in floride, and eight in calcium and potassium. Only 10 districts fall within the limit of chlorine and all samples showed satisfactory results in the case of CN, CO3, NO2, NO3, Mg, SO4 and NH4. Key words: Groundwater, physicochemical, ionic concentration, traces elements, microbiology, WHO guidelines/recommendations.
A New Steel Defect Detection Algorithm Based on Deep Learning

In recent years, more and more scholars devoted themselves to the research of the target detection algorithm due to the continuous development of deep learning. Among them, the detection and recognition of small and complex targets are still a problem to be solved. The authors of this article have understood the shortcomings of the deep learning detection algorithm in detecting small and complex defect targets and would like to share a new improved target detection algorithm in steel surface defect detection. The steel surface defects will affect the quality of steel seriously. We find that most of the current detection algorithms for NEU-DET dataset detection accuracy are low, so we choose to verify a steel surface defect detection algorithm based on machine vision on this dataset for the problem of defect detection in steel production. A series of improvement measures are carried out in the traditional Faster R-CNN algorithm, such as reconstructing the network structure of Faster R-CNN. Based on the small features of the target, we train the network with multiscale fusion. For the complex features of the target, we replace part of the conventional convolution network with a deformable convolution network. The experimental results show that the deep learning network model trained by the proposed method has good detection performance, and the mean average precision is 0.752, which is 0.128 higher than the original algorithm. Among them, the average precision of crazing, inclusion, patches, pitted surface, rolled in scale and scratches is 0.501, 0.791, 0.792, 0.874, 0.649, and 0.905, respectively. The detection method is able to identify small target defects on the steel surface effectively, which can provide a reference for the automatic detection of steel defects.
Outcomes assessment pitfalls: challenges to quantifying knowledge gain in a sex education game

Background: The use of videogames as a public health tool is rapidly expanding. Accurate assessment of the efficacy of such games is complicated by many factors. We describe challenges associated with measuring the impact of playing a videogame with information about human sexual anatomy and reproduction and discuss motivations for, and solutions to, these challenges. Methods: The My Future Family Game (MFF) is a validated tool for collecting data about family planning intentions which includes information about human anatomy and sexual reproduction. We sought to assess the efficacy of the game as a tool for teaching sexual education using a pre-post model which was deployed in three schools in and around Chennai, India in summer of 2018. Results: The MFF game was successfully modified to collect data about players’ pre-gameplay knowledge of sexual anatomy and processes. The post gameplay assessment process we used did not effectively assess knowledge gain. Designing assessments for games dealing with sexuality presents challenges including: effectively communicating about biological parts and processes, designing usable and intuitive interfaces with minimal text, ensuring that all parts of the process are fun, and integrating assessments into the game in a way that makes them invisible. Conclusion: Games can be an effective means of gathering data about knowledge of sex and reproduction that it is difficult to obtain through other means. Assessing knowledge about human sexual reproduction is complicated by cultural norms and taboos, and technical hurdles which can be addressed through careful design. This study adds to the sparse literature in the field by providing information about pitfalls to avoid and best practices in this evolving area.
Mineralogy and Elemental Contents of the Sirnak Asphaltite, Southeast Turkey

This study focuses on mineralogy and elemental contents of 7 Sirnak asphaltite samples of which 6 were collected from Avgamasya mines and 1 from Segurik mine in the southeastern part of Turkey. The samples were studied using proximate analysis, X-ray powder diffraction (XRD), inductively coupled plasma-atomic emission spectrometry (ICP-AES), inductively coupled plasma-mass spectrometry (ICP-MS), and scanning electron microscopy with energy-dispersive X-ray microanalysis (SEM-EDX) techniques. The asphaltites on an as-received basis have low moisture contents (0.3-1.6%), high ash yields (38.6-42.7%), high total sulfur contents (5.39-6.80%), and low calorific values (4261-4548 kcal/kg). The mineral matter of the asphaltites from XRD is mainly made up of calcite, dolomite, pyrite, quartz, and minor quantities of feldspar, clay mineral (illite), and gypsum. The asphaltites from ICP-AES and ICP-MS proved to be enriched relative to Cd, Cr, Cu, Mo, Ni, Sb, Se, Tl, U, V, and Zn. Furthermore, concentrations of Bi, Dy, Er, and Gd in some samples exceed the currently available ranges for most world coals. Bulk chemical data from SEM-EDX confirm that Cd was detected in sphalerite and CdZn-sulfide, Ni in pentlandite, and Ni, Cu, Zn, V, and Cr traces in pyrites.
The relationship between rotator cuff integrity and acromiohumeral distance following open and arthroscopic rotator cuff repair

Background: Acromiohumeral distance (AHD) has become both a diagnostic and prognostic parameter related to rotator cuff pathology which is always measured in a 2-dimensional plane. The purposes of this study were (1) to evaluate the regional AHD with MRI following open and arthroscopic rotator cuff repair and, (2) to investigate its association to the rotator cuff integrity following medium to large size rotator cuff repair with open and arthroscopic manner. Methods: A retrospective review of 112 patients who were treated for full-thickness medium to large size rotator cuff tears either by open repair (open group) or arthroscopic repair (arthroscopic group) was done. All patients included in the study are those with at least 12 and 18 months for the post-operative MRI and clinical follow-up. Propensity score matching was used to select controls matched for age, sex, body mass index, tear size, and affected site. There were 56 patients in each group with a mean age of 63.3 years (range, 50 to 77 years). The post-operative functional and radiologic outcomes for both groups were compared. AHD was measured at three regions of interest (ROI) with MRI and compared pre-and post-operatively. Results: AHD was significantly greater in the open group when measured at the anterior third of the lateral acromion border compare to the arthroscopic group (p = 0.005). The re-tear rate was affected by AHD at the anterior third of the lateral border of the acromion for the arthroscopic and open group (p = 0.021, p = 0.029). The AHD measured at the anterior and middle third of lateral acromion border were significantly greater in healed compared to the re-tear rotator cuff group (p = 0.019, p = 0.022). Conclusions: Open rotator cuff repair showed greater AHD at the anterior third of the lateral border of the acromion. Regional AHD measured at anterior third of the lateral border of acromion significantly associated with rotator cuff integrity following repair. Level of evidence: propensity-matched case-control (Level II)