Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse Bitmap Compression for Memory-Efficient Training on the Edge

Training on the Edge enables neural networks to learn continu-ously from new data after deployment on memory-constrained edge devices. Previous work is mostly concerned with reducing the number of model parameters which is only beneficial for in-ference. However, memory footprint from activations is the main bottleneck for training on the edge. Existing incremental training methods fine-tune the last few layers sacrificing accuracy gains from re-training the whole model. In this work, we investigate the memory footprint of training deep learning models, and use our observations to propose BitTrain. In BitTrain, we exploit activation sparsity and propose a novel bitmap compression technique that reduces the memory footprint during training. We save the activations in our proposed bitmap compression format during the forward pass of the training, and restore them during the backward pass for the optimizer computations. The proposed method can be integrated seamlessly in the computation graph of modern deep learning frameworks. Our implementation is safe by construction, and has no negative impact on the accuracy of model training. Experimental results show up to 34% reduction in the memory footprint at a sparsity level of 50%. Further pruning during training results in more than 70% sparsity, which can lead to up to 56% re-duction in memory footprint. BitTrain advances the efforts towards bringing more machine learning capabilities to edge devices. Our source code is available at https://github.com/scale-lab/BitTrain.
FAST: DNN Training Under Variable Precision Block Floating Point with Stochastic Rounding

Block Floating Point (BFP) can efficiently support quantization for Deep Neural Network (DNN) training by providing a wide dynamic range via a shared exponent across a group of values. In this paper, we propose a Fast First, Accurate Second Training (FAST) system for DNNs, where the weights, activations, and gradients are represented in BFP. FAST supports matrix multiplication with variable precision BFP input operands, enabling incremental increases in DNN precision throughout training. By increasing the BFP precision across both training iterations and DNN layers, FAST can greatly shorten the training time while reducing overall hardware resource usage. Our FAST Multipler-Accumulator (fMAC) supports dot product computations under multiple BFP precisions. We validate our FAST system on multiple DNNs with different datasets, demonstrating a 2-6× speedup in training on a single-chip platform over prior work based on mixed-precision or block floating point number systems while achieving similar performance in validation accuracy.
Contact Transfer: A Direct, User-Driven Method for Human to Robot Transfer of Grasps and Manipulations

We present a novel method for the direct transfer of grasps and manipulations between objects and hands through utilization of contact areas. Our method fully preserves contact shapes, and in contrast to existing techniques, is not dependent on grasp families, requires no model training or grasp sampling, makes no assumptions about manipulator morphology or kinematics, and allows user control over both transfer parameters and solution optimization. Despite these accommodations, we show that our method is capable of synthesizing kinematically-feasible whole hand poses in seconds even for poor initializations or hard-to-reach contacts. We additionally highlight the method's benefits in both response to design alterations as well as fast approximation over in-hand manipulation sequences. Finally, we demonstrate a solution generated by our method on a physical, custom-designed prosthetic hand.
Stand-alone device for IoT applications

Internet of Things (IoT) is a digital world of connected and talking devices, providing room for countless and diverse smart applications. This paper proposes one such IoT enabled stand-alone device with numerous capabilities: (i) interaction with user, (ii) required application selection, (iii) data sensing, (iv) data publish, and (v) decision making and actuation. The algorithm allows user to pick an application and input specific data for calibration, which on completion enables the device for further working. To verify and test its capability, a smart home garden environment is created using this device, temperature, humidity, soil moisture sensors and actuators. As it is implicit that real-time communication is inevitable for an IoT application, the sensor data is published to a Mosquitto MQTT broker to permit real-time remote access. The decision taken by the device is sent to actuators via relay, thus a continuous monitoring process is achieved. Results are obtained for the application which proves the device suitability for IoT applications.
Integrating Deep Event-Level and Script-Level Information for Script Event Prediction

Scripts are structured sequences of events together with the participants, which are extracted from the texts. Script event prediction aims to predict the subsequent event given the historical events in the script. Two kinds of information facilitate this task, namely, the event-level information and the script-level information. At the event level, existing studies view an event as a verb with its participants, while neglecting other useful properties, such as the state of the participants. At the script level, most existing studies only consider a single event sequence corresponding to one common protagonist. In this paper, we propose a Transformer-based model, called MCPredictor, which integrates deep event-level and script-level information for script event prediction. At the event level, MCPredictor utilizes the rich information in the text to obtain more comprehensive event semantic representations. At the script-level, it considers multiple event sequences corresponding to different participants of the subsequent event. The experimental results on the widely-used New York Times corpus demonstrate the effectiveness and superiority of the proposed model.
Comparing Machine Learning-Centered Approaches for Forecasting Language Patterns During Frustration in Early Childhood

When faced with self-regulation challenges, children have been known the use their language to inhibit their emotions and behaviors. Yet, to date, there has been a critical lack of evidence regarding what patterns in their speech children use during these moments of frustration. In this paper, eXtreme Gradient Boosting, Random Forest, Long Short-Term Memory Recurrent Neural Networks, and Elastic Net Regression, have all been used to forecast these language patterns in children. Based on the results of a comparative analysis between these methods, the study reveals that when dealing with high-dimensional and dense data, with very irregular and abnormal distributions, as is the case with self-regulation patterns in children, decision tree-based algorithms are able to outperform traditional regression and neural network methods in their shortcomings.
Some Remarks on Bayesian Multiple Hypothesis Testing

We consider Bayesian multiple hypothesis problem with independent and identically distributed observations. The classical, Sanov's theorem-based, analysis of the error probability allows one to characterize the best achievable error exponent. However, this analysis does not generalize to the case where the true distributions of the hypothesis are not exact or partially known via some nominal distributions. This problem has practical significance, because the nominal distributions may be quantized versions of the true distributions in a hardware implementation, or they may be estimates of the true distributions obtained from labeled training sequences as in statistical classification. In this paper, we develop a type-based analysis to investigate Bayesian multiple hypothesis testing problem. Our analysis allows one to explicitly calculate the error exponent of a given type and extends the classical analysis. As a generalization of the proposed method, we derive a robust test and obtain its error exponent for the case where the hypothesis distributions are not known but there exist nominal distribution that are close to true distributions in variational distance.
Estimating the distribution of ratio of paired event times in phase II oncology trials

With the rapid development of new anti‐cancer agents which are cytostatic, new endpoints are needed to better measure treatment efficacy in phase II trials. For this purpose, Von Hoff (1998) proposed the growth modulation index (GMI), that is, the ratio between times to progression or progression‐free survival times in two successive treatment lines. An essential task in studies using GMI as an endpoint is to estimate the distribution of GMI. Traditional methods for survival data have been used for estimating the GMI distribution because censoring is common for GMI data. However, we point out that the independent censoring assumption required by traditional survival methods is always violated for GMI, which may lead to severely biased results. In this paper, we construct both nonparametric and parametric estimators for the distribution of GMI, accounting for the dependent censoring of GMI. Extensive simulation studies show that our nonparametric estimators perform well in practical situations and outperform existing estimators, and our parametric estimators perform better than our nonparametric estimators and existing estimators when the parametric model is correctly specified. A phase II clinical trial using GMI as the primary endpoint is provided for illustration.
Batch-Softmax Contrastive Loss for Pairwise Sentence Scoring Tasks

The use of contrastive loss for representation learning has become prominent in computer vision, and it is now getting attention in Natural Language Processing (NLP). Here, we explore the idea of using a batch-softmax contrastive loss when fine-tuning large-scale pre-trained transformer models to learn better task-specific sentence embeddings for pairwise sentence scoring tasks.We introduce and study a number of variations in the calculation of the loss as well as in the overall training procedure; in particular, we find that a special data shuffling can be quite important.Our experimental results show sizable improvements on a number of datasets and pairwise sentence scoring tasks including classification, ranking, and regression.Finally, we offer detailed analysis and discussion, which should be useful for researchers aiming to explore the utility of contrastive loss in NLP.
Vulnerability Characterization and Privacy Quantification for Cyber-Physical Systems

Cyber-physical systems (CPS) data privacy protection during sharing, aggregating, and publishing is a challenging problem. Several privacy protection mechanisms have been developed in the literature to protect sensitive data from adversarial analysis and eliminate the risk of re-identifying the original properties of shared data. However, most of the existing solutions have drawbacks, such as (i) lack of a proper vulnerability characterization model to accurately identify where privacy is needed, (ii) ignoring data providers privacy preference, (iii) using uniform privacy protection which may create inadequate privacy for some provider while over-protecting others, and (iv) lack of a comprehensive privacy quantification model assuring data privacy-preservation. To address these issues, we propose a personalized privacy preference framework by characterizing and quantifying the CPS vulnerabilities as well as ensuring privacy. First, we introduce a Standard Vulnerability Profiling Library (SVPL) by arranging the nodes of an energy-CPS from maximum to minimum vulnerable based on their privacy loss. Based on this model, we present our personalized privacy framework (PDP) in which Laplace noise is added based on the individual node's selected privacy preferences. Finally, combining these two proposed methods, we demonstrate that our privacy characterization and quantification model can attain better privacy preservation by eliminating the trade-off between privacy, utility, and risk of losing information.
ARviz - An Augmented Reality-enabled Visualization Platform for ROS Applications

Current robot interfaces such as teach pendants and 2D screen displays used for task visualization and interaction often seem unintuitive and limited in terms of information flow. This compromises task efficiency as interacting with the interface can distract the user from the task at hand. Augmented Reality (AR) technology offers the capability to create visually rich displays and intuitive interaction elements in situ. In recent years, AR has shown promising potential to enable effective human-robot interaction. We introduce ARviz - a versatile, extendable AR visualization platform built for robot applications developed with the widely used Robot Operating System (ROS) framework. ARviz aims to provide both a universal visualization platform with the capability of displaying any ROS message data type in AR, as well as a multimodal user interface for interacting with robots over ROS. ARviz is built as a platform incorporating a collection of plugins that provide visualization and/or interaction components. Users can also extend the platform by implementing new plugins to suit their needs. We present three use cases as well as two potential use cases to showcase the capabilities and benefits of the ARviz platform for human-robot interaction applications. The open access source code for our ARviz platform is available at: https://github.com/hri-group/arviz.
Variable star classification across the Galactic bulge and disc with the VISTA Variables in the V\'ia L\'actea survey

We present VIVACE, the VIrac VAriable Classiﬁcation Ensemble, a catalogue of variable stars extracted from an automated classiﬁcation pipeline for the Vista Variables in the Vía Láctea (VVV) infrared survey of the Galactic bar/bulge and southern disc. Our procedure utilises a two-stage hierarchical classiﬁer to ﬁrst isolate likely variable sources using simple variability summary statistics and training sets of non-variable sources from the Gaia early third data release, and then classify candidate variables using more detailed light curve statistics and training labels primarily from OGLE and VSX. The methodology is applied to point-spread-function photometry for ∼ 490 million light curves from the VIRAC v2 astrometric and photometric catalogue resulting in a catalogue of ∼ 1 . 4 million likely variable stars, of which ∼ 39 , 000 are high-conﬁdence (classiﬁcation probability > 0 . 9) RR Lyrae ab stars, ∼ 8000 RR Lyrae c/d stars, ∼ 187 , 000 detached/semi-detached eclipsing binaries, ∼ 18 , 000 contact eclipsing binaries, ∼ 1400 classical Cepheid variables and ∼ 2200 Type II Cepheid variables. Comparison with OGLE-4 suggests a completeness of around 90 per cent for RRab and (cid:46) 60 per cent for RRc/d, and a misclassiﬁcation rate for known RR Lyrae stars of around 1 per cent for the high conﬁdence sample. We close with two science demonstrations of our new VIVACE catalogue: ﬁrst, a brief investigation of the spatial and kinematic properties of the RR Lyrae stars within the disc/bulge, demonstrating the spatial elongation of bar-bulge RR Lyrae stars is in the same sense as the more metal-rich red giant population whilst having a slower rotation rate of ∼ 40 km s − 1 kpc − 1 ; and secondly, an investigation of the Gaia EDR3 parallax zeropoint using contact eclipsing binaries across the Galactic disc plane and bulge.
A Survey on Threat Situation Awareness Systems: Framework, Techniques, and Insights

Cyberspace is full of uncertainty in terms of advanced and sophisticated cyber threats which are equipped with novel approaches to learn the system and propagate themselves, such as AI-powered threats. To debilitate these types of threats, a modern and intelligent Cyber Situation Awareness (SA) system need to be developed which has the ability of monitoring and capturing various types of threats, analyzing and devising a plan to avoid further attacks. This paper provides a comprehensive study on the current state-of-the-art in the cyber SA to discuss the following aspects of SA: key design principles, framework, classifications, data collection, and analysis of the techniques, and evaluation methods. Lastly, we highlight misconceptions, insights and limitations of this study and suggest some future work directions to address the limitations.
The divide-and-conquer sequential Monte Carlo algorithm: Theoretical properties and limit theorems

We provide a comprehensive characterisation of the theoretical properties of the divide-and-conquer sequential Monte Carlo (DaC-SMC) algorithm. We firmly establish it as a well-founded method by showing that it possesses the same basic properties as conventional sequential Monte Carlo (SMC) algorithms do. In particular, we derive pertinent laws of large numbers, $L^p$ inequalities, and central limit theorems; and we characterize the bias in the normalized estimates produced by the algorithm and argue the absence thereof in the unnormalized ones. We further consider its practical implementation and several interesting variants; obtain expressions for its globally and locally optimal intermediate targets, auxiliary measures, and proposal kernels; and show that, in comparable conditions, DaC-SMC proves more statistically efficient than its direct SMC analogue. We close the paper with a discussion of our results, open questions, and future research directions.
ST-ABN: Visual Explanation Taking into Account Spatio-temporal Information for Video Recognition

It is difficult for people to interpret the decision-making in the inference process of deep neural networks. Visual explanation is one method for interpreting the decision-making of deep learning. It analyzes the decision-making of 2D CNNs by visualizing an attention map that highlights discriminative regions. Visual explanation for interpreting the decision-making process in video recognition is more difficult because it is necessary to consider not only spatial but also temporal information, which is different from the case of still images. In this paper, we propose a visual explanation method called spatio-temporal attention branch network (ST-ABN) for video recognition. It enables visual explanation for both spatial and temporal information. ST-ABN acquires the importance of spatial and temporal information during network inference and applies it to recognition processing to improve recognition performance and visual explainability. Experimental results with Something-Something datasets V1 \&V2 demonstrated that ST-ABN enables visual explanation that takes into account spatial and temporal information simultaneously and improves recognition performance.
A LIGHTWEIGHT TARGET DETECTION ALGORITHM BASED ON MOBILENET CONVOLUTION

Target detection algorithm based on deep learning needs high computer GPU configuration, even need to use high performance deep learning workstation, this not only makes the cost increase, also greatly limits the realizability of the ground, this paper introduces a kind of lightweight algorithm for target detection under the condition of the balance accuracy and computational efficiency, MobileNet as Backbone performs parameter The processing speed is 30fps on the RTX2060 card for images with the CNN separator layer. The processing speed is 30fps on the RTX2060 card for images with a resolution of 320×320.
A long-term monthly assessment of land surface temperature and normalized difference vegetation index using Landsat data

Abstract The present study assesses the monthly variation of land surface temperature (LST) and the relationship between LST and normalized difference vegetation index (NDVI) in Raipur City of India using one hundred and eighteen Landsat images from 1988 to 2019. The results show that a monthly variation is observed in the mean LST. The highest mean LST is found in April (38.79oC), followed by May (36.64oC), June (34.56oC), and March (32.11oC). The lowest mean LST is observed in January (23.01oC), followed by December (23.76oC), and November (25.83oC). A moderate range of mean LST is noticed in September (27.18oC), October (27.22oC), and February (27.88oC). Pearson's linear correlation method is used to correlate LST with NDVI. The LST-NDVI correlation is strong negative in October (-0.62), September (-0.55), and April (-0.51). The moderate negative correlation is developed in March (-0.40), May (-0.44), June (-0.47), and November (-0.39). A weak negative correlation is observed in December (-0.21), January (-0.24), and February (-0.29). The change in weather elements and variation in land surface characteristics contribute to the monthly fluctuation of mean LST and LST-NDVI correlation. The study will be an effective one for the town and country planners for their future estimation of land conversion.
Consensus Control for Coordinating Grid-Forming and Grid-Following Inverters in Microgrids

In a 100% inverter-based microgrid, both grid-forming (GFM) and grid-following (GFL) inverters will have a crucial role to play in frequency/voltage regulation and maintaining power sharing through their grid support capabilities. Consequently, the coordination between these two technologies becomes increasingly important for optimal system performance. However, the existing work does not consider GFL’s potential to participate in a secondary control in coordination with GFM, thus not utilizing the full capability of inverter resources. In this paper, we present an automatic secondary control scheme that fully coordinates the GFL and GFM inverters to achieve frequency/voltage restoration, accurate power sharing, and circulating var mitigation in networked microgrids even without the support of any synchronous generators or the bulk power system. We use the leader-follower consensus framework to develop the GFM-GFL coordinated secondary control, in the absence of a centralized controller. The proposed coordination is effective under different disturbances, and can work in a plug-and-play manner. In addition, we find that the proposed fully-coordinated secondary control outperforms other approaches such as un-coordinated and partially-coordinated secondary controls in aspects of load sharing and frequency and voltage regulation. Overall, this study emphasizes the need and benefits of GFL-GFM coordination in microgrids.
PANTHER: Making genome‐scale phylogenetics accessible to all

Phylogenetics is a powerful tool for analyzing protein sequences, by inferring their evolutionary relationships to other proteins. However, phylogenetics analyses can be challenging: they are computationally expensive and must be performed carefully in order to avoid systematic errors and artifacts. Protein Analysis THrough Evolutionary Relationships (PANTHER; http://pantherdb.org) is a publicly available, user‐focused knowledgebase that stores the results of an extensive phylogenetic reconstruction pipeline that includes computational and manual processes and quality control steps. First, fully reconciled phylogenetic trees (including ancestral protein sequences) are reconstructed for a set of “reference” protein sequences obtained from fully sequenced genomes of organisms across the tree of life. Second, the resulting phylogenetic trees are manually reviewed and annotated with function evolution events: inferred gains and losses of protein function along branches of the phylogenetic tree. Here, we describe in detail the current contents of PANTHER, how those contents are generated, and how they can be used in a variety of applications. The PANTHER knowledgebase can be downloaded or accessed via an extensive API. In addition, PANTHER provides software tools to facilitate the application of the knowledgebase to common protein sequence analysis tasks: exploring an annotated genome by gene function; performing “enrichment analysis” of lists of genes; annotating a single sequence or large batch of sequences by homology; and assessing the likelihood that a genetic variant at a particular site in a protein will have deleterious effects.