Miracle-dz/UniFAR-FTUs huggingface dataset:
Flame retardation of glass-fiber-reinforced polyamide 6 by combination of aluminum phenylphosphinate with melamine pyrophosphate

The aluminum phenylphosphinate (BPA-Al) was synthesized and its chemical structure was confirmed by FT-IR, 1H NMR, and inductively coupled plasma-atomic emission spectrometry (ICP-AES). BPA-Al was used to prepare a series of flame-retardant glass-fiber-reinforced polyamide 6 (GFPA6) composites together with melamine pyrophosphate (MPyP) and sodium tungstate (ST) via melt compounding. The flame retardancy of the composites was investigated by vertical burning test and cone calorimeter test. The thermal behaviors and decomposition kinetics were investigated by thermogravimetric analysis (TGA). BPA-Al alone could not improve the flame retardancy of GFPA6 composites, but the combination of BPA-Al and MPyP resulted in a UL-94 V-2 rating, and the addition of a small amount of ST to the flame retardant system could endow the composites a V-0 rating. The flame retardant systems also resulted in a decrease in PHRR and THR in cone calorimeter test. The analysis of thermal decomposition kinetics showed that the activation energies (Eα) of the flame-retardant composites dramatically decreased compared with that of GFPA6. Copyright © 2011 John Wiley & Sons, Ltd.
Boundary-Aware Geometric Encoding for Semantic Segmentation of Point Clouds

Boundary information plays a significant role in 2D image segmentation, while usually being ignored in 3D point cloud segmentation where ambiguous features might be generated in feature extraction, leading to misclassification in the transition area between two objects. In this paper, firstly, we propose a Boundary Prediction Module (BPM) to predict boundary points. Based on the predicted boundary, a boundary-aware Geometric Encoding Module (GEM) is designed to encode geometric information and aggregate features with discrimination in a neighborhood, so that the local features belonging to different categories will not be polluted by each other. To provide extra geometric information for boundary-aware GEM, we also propose a light-weight Geometric Convolution Operation (GCO), making the extracted features more distinguishing. Built upon the boundary-aware GEM, we build our network and test it on benchmarks like ScanNet v2, S3DIS. Results show our methods can significantly improve the baseline and achieve state-of-the-art performance.
Detecting Suspicious Events in Fast Information Flows

We describe a computational feather-light and intuitive, yet provably efficient algorithm, named HALFADO. HALFADO is designed for detecting suspicious events in a high-frequency stream of complex entries, based on a relatively small number of examples of human judgement. Operating a sufficiently accurate detection system is vital for assisting teams of human experts in many different areas of the modern digital society. These systems have intrinsically a far-reaching normative effect, and public knowledge of the workings of such technology should be a human right. On a conceptual level, the present approach extends one of the most classical learning algorithms for classification, inheriting its theoretical properties. It however works in a semi-supervised way integrating human and computational intelligence. On a practical level, this algorithm transcends existing approaches (expert systems) by managing and boosting their performance into a single global detector. We illustrate HALFADO’s efficacy on two challenging applications: (1) for detecting hate speech messages in a flow of text messages gathered from a social media platform, and (2) for a Transaction Monitoring System (TMS) in FinTech detecting fraudulent transactions in a stream of financial transactions. This algorithm illustrates that contrary to popular belief advanced methods of machine learning need not require neither advanced levels of computation power nor expensive annotation efforts.
Information-theoretic bounds on quantum advantage in machine learning

We study the performance of classical and quantum machine learning (ML) models in predicting outcomes of physical experiments. The experiments depend on an input parameter x and involve execution of a (possibly unknown) quantum process E. Our figure of merit is the number of runs of E required to achieve a desired prediction performance. We consider classical ML models that perform a measurement and record the classical outcome after each run of E, and quantum ML models that can access E coherently to acquire quantum data; the classical or quantum data are then used to predict the outcomes of future experiments. We prove that for any input distribution D(x), a classical ML model can provide accurate predictions on average by accessing E a number of times comparable to the optimal quantum ML model. In contrast, for achieving an accurate prediction on all inputs, we prove that the exponential quantum advantage is possible. For example, to predict the expectations of all Pauli observables in an n-qubit system ρ, classical ML models require 2^{Ω(n)} copies of ρ, but we present a quantum ML model using only O(n) copies. Our results clarify where the quantum advantage is possible and highlight the potential for classical ML models to address challenging quantum problems in physics and chemistry.
Efficient 3D Point Cloud Feature Learning for Large-Scale Place Recognition

Point cloud based retrieval for place recognition is still a challenging problem since the drastic appearance changes of scenes due to seasonal or artificial changes in the environments. Existing deep learning based global descriptors for the retrieval task usually consume a large amount of computational resources ( $e.g$ ., memory), which may not be suitable for the cases of limited hardware resources. In this paper, we develop an efficient point cloud learning network (EPC-Net) to generate global descriptors of point clouds for place recognition. While obtaining good performance, it can greatly reduce computational memory and inference time. First, we propose a lightweight but effective neural network module, called ProxyConv, to aggregate the local geometric features of point clouds. We leverage the adjacency matrix and proxy points to simplify the original edge convolution for lower memory consumption. Then, we design a lightweight grouped VLAD network to form global descriptors for retrieval. Compared with the original VLAD network, we propose a grouped fully connected layer to decompose the high-dimensional vectors into a group of low-dimensional vectors, which can reduce the number of parameters of the network and maintain the discrimination of the feature vector. Finally, we further develop a simple version of EPC-Net, called EPC-Net-L, which consists of two ProxyConv modules and one max pooling layer to aggregate global descriptors. By distilling the knowledge from EPC-Net, EPC-Net-L can obtain discriminative global descriptors for retrieval. Extensive experiments on the Oxford dataset and three in-house datasets demonstrate that our method achieves good results with lower parameters, FLOPs, GPU memory, and shorter inference time. Our code is available at https://github.com/fpthink/EPC-Net.
In Situ Performance of the Low Frequency Array for Advanced ACTPol

The AdvancedAtacama Cosmology Telescope Polarimeter (AdvACT) [1] is an upgrade for the Atacama Cosmology Telescope using Transition Edge Sensor (TES) detector arrays to measure cosmic microwave background (CMB) temperature and polarization anisotropies in multiple frequencies. The low frequency (LF) array was deployed early 2020. It consists of 292 TES bolometers observing in two bands centered at 27 GHz and 39 GHz. At these frequencies, it is sensitive to synchrotron radiation from our galaxy as well as to the CMB, and complements the AdvACT arrays operating at 90, 150 and 230 GHz. We present the initial LF array on-site characterization, including the time constant, optical efficiency and array sensitivity.
Planning for Multi-stage Forceful Manipulation

Multi-stage forceful manipulation tasks, such as twisting a nut on a bolt, require reasoning over interlocking constraints over discrete and continuous choices. The robot must choose a sequence of discrete actions, or strategy, such as whether to pick up an object, and the continuous parameters of each of those actions, such as how to grasp that object. In forceful manipulation tasks, the force requirements substantially impact the choices of both strategy and parameters. To enable planning and executing forceful manipulation, we augment an existing task and motion planner with controllers that exert wrenches and constraints that explicitly consider torque and frictional limits. In two domains, opening a childproof bottle and twisting a nut, we demonstrate how the system considers a combinatorial number of strategies and how choosing actions that are robust to parameter variations impacts the choice of strategy. https://mcube.mit. edu/forceful-manipulation/
Learning Anthropometry from Rendered Humans

Accurate estimation of anthropometric body measurements from RGB images has many potential applications in industrial design, online clothing, medical diagnosis and ergonomics. Research on this topic is limited by the fact that there exist only generated datasets which are based on fitting a 3D body mesh to 3D body scans in the commercial CAESAR dataset. For 2D only silhouettes are generated. To circumvent the data bottleneck, we introduce a new 3D scan dataset of 2,675 female and 1,474 male scans. We also introduce a small dataset of 200 RGB images and tape measured ground truth. With the help of the two new datasets we propose a part-based shape model and a deep neural network for estimating anthropometric measurements from 2D images. All data will be made publicly available.
Spatial K-anonymity: A Privacy-preserving Method for COVID-19 Related Geospatial Technologies

There is a growing need for spatial privacy considerations in the many geo-spatial technologies that have been created as solutions for COVID-19-related issues. Although effective geo-spatial technologies have already been rolled out, most have significantly sacrificed privacy for utility. In this paper, we explore spatial k-anonymity, a privacy-preserving method that can address this unnecessary tradeoff by providing the best of both privacy and utility. After evaluating its past implications in geo-spatial use cases, we propose applications of spatial k-anonymity in the data sharing and managing of COVID-19 contact tracing technologies as well as heat maps showing a user's travel history. We then justify our propositions by comparing spatial k-anonymity with several other spatial privacy methods, including differential privacy, geo-indistinguishability, and manual consent based redaction. Our hope is to raise awareness of the ever-growing risks associated with spatial privacy and how they can be solved with Spatial K-anonymity.
Global dynamics of a Lotka–Volterra competition patch model

The global dynamics of the two-species Lotka–Volterra competition patch model with asymmetric dispersal is classified under the assumptions that the competition is weak and the weighted digraph of the connection matrix is strongly connected and cycle-balanced. We show that in the long time, either the competition exclusion holds that one species becomes extinct, or the two species reach a coexistence equilibrium, and the outcome of the competition is determined by the strength of the inter-specific competition and the dispersal rates. Our main techniques in the proofs follow the theory of monotone dynamical systems and a graph-theoretic approach based on the tree-cycle identity.
CODEX weak lensing mass catalogue and implications on the mass–richness relation

The COnstrain Dark Energy with X-ray clusters (CODEX) sample contains the largest flux limited sample of X-ray clusters at 0.35 < z < 0.65. It was selected from ROSAT data in the 10,000 square degrees of overlap with BOSS, mapping a total number of 2770 high-z galaxy clusters. We present here the full results of the CFHT CODEX program on cluster mass measurement, including a reanalysis of CFHTLS Wide data, with 25 individual lensingconstrained cluster masses. We employ lensfit shape measurement and perform a conservative colour-space selection and weighting of background galaxies. Using the combination of shape noise and an analytic covariance for intrinsic variations of cluster profiles at fixed mass due to large scale structure, miscentring, and variations in concentration and ellipticity, we determine the likelihood of the observed shear signal as a function of true mass for each cluster. We combine 25 individual cluster mass likelihoods in a Bayesian hierarchical scheme with the inclusion of optical and X-ray selection functions to derive constraints on the slope α, normalization β, and scatter σlnλ |μ of our richness–mass scaling relation model in logspace: 〈lnλ |μ〉 = αμ + β, with μ = ln(M200c/Mpiv), and Mpiv = 1014.81M . We find a slope α = 0.49+0.20 −0.15, normalization exp(β) = 84.0 +9.2 −14.8 and σlnλ |μ = 0.17 +0.13 −0.09 using CFHT richness estimates. In comparison to other weak lensing richness-mass relations, we find the normalization of the richness statistically agreeing with the normalization of other scaling relations from a broad redshift range (0.0 < z < 0.65) and with different cluster selection (X-ray, Sunyaev-Zeldovich, and optical).
Object Detection for Understanding Assembly Instruction Using Context-aware Data Augmentation and Cascade Mask R-CNN

Understanding assembly instruction has the potential to enhance the robot s task planning ability and enables advanced robotic applications. To recognize the key components from the 2D assembly instruction image, We mainly focus on segmenting the speech bubble area, which contains lots of information about instructions. For this, We applied Cascade Mask R-CNN and developed a context-aware data augmentation scheme for speech bubble segmentation, which randomly combines images cuts by considering the context of assembly instructions. We showed that the proposed augmentation scheme achieves a better segmentation performance compared to the existing augmentation algorithm by increasing the diversity of trainable data while considering the distribution of components locations. Also, we showed that deep learning can be useful to understand assembly instruction by detecting the essential objects in the assembly instruction, such as tools and parts.
Sequential Design of Computer Experiments with Quantitative and Qualitative Factors in Applications to HPC Performance Optimization

Computer experiments with both qualitative and quantitative factors are widely used in many applications. Motivated by the emerging need of optimal configuration in the high-performance computing (HPC) system, this work proposes a sequential design, denoted as adaptive composite exploitation and exploration (CEE), for optimization of computer experiments with qualitative and quantitative factors. The proposed adaptive CEE method combines the predictive mean and standard deviation based on the additive Gaussian process to achieve a meaningful balance between exploitation and exploration for optimization. Moreover, the adaptiveness of the proposed sequential procedure allows the selection of next design point from the adaptive design region. Theoretical justification of the adaptive design region is provided. The performance of the proposed method is evaluated by several numerical examples in simulations. The case study of HPC performance optimization further elaborates the merits of the proposed method.
Towards Optimally Efficient Tree Search with Deep Temporal Difference Learning

—This paper investigates the classical integer least-squares problem which estimates integer signals from linear models. The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning, to name a few. Since the existing optimal search strategies involve prohibitive complexities, they are hard to be adopted in large-scale problems. To address this issue, we propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal heuristic for the underlying simpliﬁed memory-bounded A* algorithm, and the proposed algorithm can be easily generalized with other heuristic search algorithms. Inspired by the temporal difference learning, we further propose a training strategy which enables the network to approach the optimal heuristic precisely and consistently, thus the proposed algorithm can reach nearly the optimal efﬁciency when the estimation error is small enough. Experiments show that the proposed algorithm can reach almost the optimal maximum likelihood estimate performance in large-scale problems, with a very low complexity in both time and space.
Linear Raman Spectroscopy on Droplet Chains: A New Experimental Method for the Analysis of Fast Transport Processes and Reactions on Microparticles

A new experimental method for the analysis of mass and energy transport and reactions on microparticles is presented. A chain of microdroplets from a vibrating orifice generator was injected into a quiescent gas phase. Linear Raman spectra from the microparticles and the surrounding gas were taken at different distances from the generator. Concentration changes were measured as a function of droplet lifetime. A period of time of up to 20 ms could be studied with a resolution of 10 μs. An argon-ion laser in a 90° scattering geometry was used for excitation. Spectra were taken through a modified double monochromator with a two-dimensional charge-coupled device (CCD) detector, one axis of which was used for spatial resolution. Profiles of gaseous components near the droplets could be measured with a resolution of 50 μm. The method has been applied to analysis of absorption, dissociation, and isomerization in the SO2–H2O system and to the investigation of the desorption process of CO2 from water droplets. Chemical components in gas and liquid phase could be separated. The detection limit in aqueous media was 1 mmol/L.
The Road to Ubiquitous Personal Fabrication: Modeling-Free Instead of Increasingly Simple

The tools for personal digital fabrication (DF) are on the verge of reaching mass-adoption beyond technology enthusiasts, empowering consumers to fabricate personalized artifacts. We argue that to achieve similar outreach and impact as personal computing, personal fabrication research may have to venture beyond ever-simpler interfaces for creation, toward lowest-effort workflows for remixing. We surveyed novice-friendly DF workflows from the perspective of HCI. Through this survey, we found two distinct approaches for this challenge: 1) simplifying expert modeling tools (AutoCAD →Tinkercad) and 2) enriching tools not involving primitive-based modeling with powerful customization (e.g., Thingiverse). Drawing parallel to content creation domains such as photography, we argue that the bulk of content is created via remixing (2). In this article, we argue that to be able to include the majority of the population in DF, research should embrace omission of workflow steps, shifting toward automation, remixing, and templates, instead of modeling from the ground up.
Multitask Learning for Emotion and Personality Detection

In recent years, deep learning-based automated personality trait detection has received a lot of attention, especially now, due to the massive digital footprints of an individual. Moreover, many researchers have demonstrated that there is a strong link between personality traits and emotions. In this paper, we build on the known correlation between personality traits and emotional behaviors, and propose a novel multitask learning framework, SoGMTL that simultaneously predicts both of them. We also empirically evaluate and discuss different information-sharing mechanisms between the two tasks. To ensure the high quality of the learning process, we adopt a MAML-like framework for model optimization. Our more computationally efficient CNN-based multitask model achieves the state-of-the-art performance across multiple famous personality and emotion datasets, even outperforming Language Model based models.
A Low-Power In-Memory Multiplication and Accumulation Array With Modified Radix-4 Input and Canonical Signed Digit Weights

Data transfer between the processing and storage units has become a significant bottleneck in modern von Neumann computing systems for artificial intelligence (AI) tasks. Computing in memory (CIM) has emerged as a promising candidate for lowering latency and power consumption. However, the conventional analog CIM schemes are suffering from reliability issues, which may significantly degenerate the accuracy of the computation. Recently, digitized input data and weights have been utilized for high-reliable in-memory computing. However, the properties of the digital memory and input data are not fully utilized. This article presents a novel low-power CIM scheme to further reduce the power consumption by using a modified radix-4 (M-RD4) booth algorithm at the input and a modified canonical signed digit (M-CSD) for the network weights. The simulation results show that M-RD4 and M-CSD reduce the number of nonzero activation bits by 24.2% and the number of nonzero weight bits by 36.0% in AlexNet, respectively. The power consumption can be reduced by 41.6% on average. The computing-power ratio at the fixed-point 8 bit is 60.7 tera operations per second per watt (TOPS/W), and the density is 0.177 TOPS/mm2.
Max-Affine Spline Insights Into Deep Network Pruning

In this paper, we study the importance of pruning in Deep Networks (DNs) and the yin&yang relationship between (1) pruning highly overparametrized DNs that have been trained from random initialization and (2) training small DNs that have been"cleverly"initialized. As in most cases practitioners can only resort to random initialization, there is a strong need to develop a grounded understanding of DN pruning. Current literature remains largely empirical, lacking a theoretical understanding of how pruning affects DNs' decision boundary, how to interpret pruning, and how to design corresponding principled pruning techniques. To tackle those questions, we propose to employ recent advances in the theoretical analysis of Continuous Piecewise Affine (CPA) DNs. From this perspective, we will be able to detect the early-bird (EB) ticket phenomenon, provide interpretability into current pruning techniques, and develop a principled pruning strategy. In each step of our study, we conduct extensive experiments supporting our claims and results; while our main goal is to enhance the current understanding towards DN pruning instead of developing a new pruning method, our spline pruning criteria in terms of layerwise and global pruning is on par with or even outperforms state-of-the-art pruning methods.
Electrochemical behaviors of Indium

Many researchers focus on indium contained semiconductors and alloy compounds for their various applications. Electrochemists want to obtain indium contained compounds simply via one-step electrodeposition. First of all, electrochemistry of constituent elements must be understood in order to develop the best condition for the electrodeposition of indium contained compounds. We will review the electrochemistry of indium. Equilibria between indium metal and indium ions and the standard electrode potentials of the equilibria will be reviewed. The electrochemical reactions of indium species are affected by surrounding conditions. Thus dependences of electrochemical behaviors of indium metal and indium ions on various parameters will be reviewed.