Miracle-dz/UniFAR-FTUs huggingface dataset:
Supplementary Material to : Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization

1 PARAMETER STUDIES In order to further evaluate the method we provide several parameter studies in this section. As in the result section of the main paper, we have run all variants 10 times for each sequence of the EuRoC-dataset and present either the accumulation of all results or the median of the 10 runs. First of all we show the dependence on the number of points in Fig. 1. The main takeaway of this should be that because of the addition of inertial data, we can significantly reduce the number of points without loosing tracking performance.
Supplementary: Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization

GI-ReID, as a image-based cloth-changing ReID framework, with gait information as assistance, consists of an auxiliary Gait-Stream and a mainstream ReID-Stream. ReID-Stream can be arbitrary commonly-used network architectures, such as ResNet [6], and also can be some ReID-specific network architectures, such as PCB [23], OSNet [33]. Thus, in this section, we mainly introduce/describe the detailed network architecture of GaitStream which contains two key parts, GSP module for gait information prediction/augmentation and GaitSet [2] for gait features extraction. Architecture of Gait Sequence Prediction (GSP) Module: The proposed GSP module consists of a feature encoder E, a decoder D, a position embedder P , and a feature aggregator A. (1). Encoder E. The encoder E is a CNN with four Conv. layers (filter size = 4 × 4 and stride = 2). The number of
Supplementary: Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting

Here we describe the convolutional sentence representation used in Sec. 2.1. 1. We use the temporal convolutional model proposed by Kim (2014) to compute the representation of every individual sentence in the document. First, the words are converted to the distributed vector representation by a learned word embedding matrix Wemb. The sequence of the word vectors from each sentence is then fed through 1-D single-layer convolution filters with various window sizes (3, 4, 5) to capture the temporal dependencies of nearby words and then followed by relu non-linear activation and max-over-time pooling. The convolutional representation rj for the jth sentence is then obtained by concatenating the outputs from the activations of all filter window sizes.
Supplementary: Aligning Silhouette Topology for Self-Adaptive 3D Human Pose Recovery

For implementation we use the PyTorch [13] framework and train our networks on a machine with NVIDIA TITAN RTX GPU (24 GB VRAM) and Intel i7-8700 CPU (48GB RAM). The source model training on an average took 2-3 days to train, while the adaptation training varied by target dataset size. A batched inference through the model takes around 20 milli-seconds. Table 1 briefly describes and lists all the notations used in the main paper.
Supplementary : Recurrent Slice Networks for 3 D Segmentation on Point Clouds

In the main text, we used the training/testing split in [7] is used to avoid dividing areas from same building to both training and testing sets. However, in [5], the authors reported their performances using 6-fold validation. In order to comprehensively compare with [5], we also present the 6-fold validation performances of RSNet in Table.1. The results show that our RSNet outperforms PointNet by a large margin while requiring less memories and reasonable extra inference times.
Supplementary: Sunny and Dark Outside?!-Improving Answer Consistency in VQA through Entailed Question Generation

In this supplementary document, we list dataset construction details, training details, and qualitative examples from our datasets and consistency teacher module outputs. 1 Logic-ConVQA Dataset Creation We use scene graph annotations from the Visual Genome Dataset and slot-filler NLP techniques to generate a dataset of consistent QA sets (LConVQA). Currently, we only focus on attribute, existential and relational consistency. We generate groups of questions phrased differently about a certain concept to make consistent QA sets. For example, for the attribute “white” of object “cup” in the Visual Genome scene graph, we generate “is the cup white? Yes”, “Is the cup black? No” and “What color is cup? White”. Here is a summary of our consistent sets: Relational/Existential Consistency • Is ? Yes. For example, is man standing near tree? , Yes • Is there ? Yes, For example, is there man? Yes. • Is there ? Yes • Who/What is ? For example, Who is standing near tree? Man • Is ? No, Is ? No. We cross verify with scene graph to make sure these are “no”. However, the scene graph isn’t exhaustively annotated for all images and hence, these maybe noisy sometimes. Attribute Consistency • What hypernym() is ? For example, “What color is cup? White”. We get hypernyms using WordNet. • Is ? Yes • Is opposite()? No. We get opposite attributes using WordNet. Some WordNet hypernyms and opposites are noisy, so we manually generate a list of opposites for some adjectives or action words. We also observe that counting questions are often noisy because of annotations not being exhaustive and noncountable objects being annotated, hence, we skip it. We also randomly substitute “can you see” or “do you see” in place of “is there” to have diversity in questions and make them more natural sounding. We also filter by at least 15% area of bounding box to image to make sure the questions are about salient objects in the image.
Supplementary: Towards Zero-Shot Learning with Fewer Seen Class Examples

Algorithm 1 Meta-VGAN for ZSL Require: p(T ):distribution over tasks Require: η1, η2: step-size hyperparameters 1: Randomly initialize θe, θg, θd 2: while not done do 3: Sample batch of tasks Ti ∼ p(T ); where, Ti = {T tr i , T v i } such that T tr i ∩ T v i = φ 4: for all Ti do 5: Evaluate ∇θeg l G T tr i (θeg) 6: Evaluate ∇θd l T tr i (θd) 7: Compute adapted parameters: θ ′ eg = θed − η1∇θeg l G T tr i (θeg) 8: Compute adapted parameters: θ ′ d = θd + η2∇θd l T tr i (θd) 9: Update θeg ← θeg − η1∇θeg ∑ Ti∼p(T ) l V G T v i (θ ′ eg) 10: Update θd ← θd + η2∇θd ∑ Ti∼p(T ) l D T v i (θ ′ d)
Supplementary: Learning nanoscale motion patterns of vesicles in living cells

One single supplementary video is provided, which is a stitched slide presentation. The slides are numbered as #1 to #4 and are described below. #1 presents the steps of physics based simulation of microscopy videos of vesicles and their motion patterns. This illustrates the steps shown in Fig. 3 of the main manuscript. #2 presents a sample of an experimental live-cell sequence, a sample ROI extracted from the live-cell sequence, and the nanoscopy image of the ROI reconstructed using MUSICAL. #3: presents a comparison of the feature representations of vesicle motion. The raw image sequence for a vesicle in a live-cell video, the corresponding micro-motion magnified sequence, and the nanoscopy image reconstructed by MUSICAL illustrate that the nanoscopy image provides better feature of identifying the motion pattern. #4: presents the example shown in Fig. 12 of the main manuscript. The original video of a vesicle moving and interacting with a mitochondrion is shown along with the nanoscopy image obtained by stitching the MUSICAL results of the different sub ROIs.
Multi-Agent Deep Reinforcement Learning for Task Offloading in Vehicle Edge Computing

With the rapid update of the Internet of Things (IoT) in this recent period, Various equipment applications that seem to be latency savvy have emerged. Offloading data using traditional methods requires waiting until the device is in range of the MEC server for transmission. This significantly increases timing overhead and often fails to meet the latency demands of certain implementations. The high construction cost makes it impractical to deploy several MEC servers to provide complete road coverage. As a model for vehicle edge computing (VEC) task offloading utilizing a Deep Reinforcement Learning (DRL) based technique, this paper suggests a multi-vehicle aided MEC system. The capacity of vehicles' computer resources is constrained, which may limit their ability to complete duties on time. The work can be sent to the roadside units (RSU) VEC server, which has more powerful processing capabilities. We present the Actor-Critic based DRL method to advance the model's training efficiency in order to improve convergence efficiency and obtain superior system performance. Simulation findings demonstrate that our proposed Actor-Critic based DRL strategy may significantly outperform the traditional DQN technique in terms of performance, convergence speed, and total operating expenses.
Multi-Agent Deep Reinforcement Learning Based Computation Offloading Approach for LEO Satellite Broadband Networks

Conventional computation offloading approaches are originally designed for ground networks, and are not effective for low earth orbit (LEO) satellite networks. This paper proposes a multi-agent deep reinforcement learning (MADRL) algorithm for making multi-level offloading decisions in LEO satellite networks. Offloading is formulated as a partially observable Markov decision process based multi-agent decision problem. Each satellite as an agent either conducts a received task, forwards it to neighbors, or sends it to ground clouds based on its own policy. These agents are independent and their deep neural networks to make offloading decisions share identical parameter values and are trained by using the same replay buffer. A centralized training and distributed executing mechanism is adopted to ensure that agents can make globally optimized offloading decisions. Comparative experiments demonstrate that the proposed MADRL algorithm outperforms the five baselines in terms of task processing delay and bandwidth consumption with acceptable computational complexity.
Multi-Agent Deep Reinforcement Learning for Access Point Activation Strategy in Cell-Free Massive MIMO Networks

Cell-free massive MIMO network is a promising solution to support 5G communication. However, as an ultra-dense network (UDN), it suffers from two major issues: power consumption and scalability, which hinders it to be widely used in practice. The increasing and time-varying user demand, the dynamic propagation environment, and the huge amount of access points (APs) make it a challenging task to address these issues. For this purpose, we propose a distributed solution to solve the access point activation (APA) problem in cell-free massive MIMO networks to reduce power consumption considering dynamic user demand. We leverage the user-centric characteristic and design a multi-agent deep reinforcement learning (MADRL) algorithm by which each AP independently decides whether it needs to be switched on or off. The simulation results show that the MADRL outperforms the centralized and the random strategies. This research demonstrates the ability of distributed learning in solving the APA problem in cell-free massive MIMO networks.
Multi-Agent Deep Reinforcement Learning

This work introduces a novel approach for solving reinforcement learning problems in multi-agent settings. We propose a state reformulation of multi-agent problems in R that allows the system state to be represented in an image-like fashion. We then apply deep reinforcement learning techniques with a convolution neural network as the Q-value function approximator to learn distributed multi-agent policies. Our approach extends the traditional deep reinforcement learning algorithm by making use of stochastic policies during execution time and stationary policies for homogenous agents during training. We also use a residual neural network as the Q-value function approximator. The approach is shown to generalize multi-agent policies to new environments, and across varying numbers of agents. We also demonstrate how transfer learning can be applied to learning policies for large groups of agents in order to decrease convergence time. We conclude with other potential applications and extensions for future work.
MULTI-AGENT DEEP REINFORCEMENT LEARNING

A central challenge in multi-agent reinforcement learning is the induction of coordination between agents of a team. In this work, we investigate how to promote inter-agent coordination using policy regularization and discuss two possible avenues respectively based on inter-agent modelling and synchronized sub-policy selection. We test each approach in four challenging continuous control tasks with sparse rewards and compare them against three baselines including MADDPG, a state-of-the-art multi-agent reinforcement learning algorithm. To ensure a fair comparison, we rely on a thorough hyper-parameter selection and training methodology that allows a fixed hyper-parameter search budget for each algorithm and environment. We consequently assess both the hyperparameter sensitivity, sample-efficiency and asymptotic performance of each learning method. Our experiments show that the proposed methods lead to significant improvements on cooperative problems. We further analyse the effects of the proposed regularizations on the behaviors learned by the agents.
Navigating with Finesse: Leveraging Neural Network-based Lidar Perception and iLQR Control for Intelligent Agriculture Robotics

Autonomous navigation has revolutionized agriculture by enabling robots to interact with the environment autonomously. This article presents an integrated system that addresses two key aspects: generalizing environments using LiDAR perception and neural networks, and achieving optimal solutions for nonlinear systems with low computational cost. To achieve precise and efficient navigation in complex agronomic environments, the system combines LiDAR sensors and deep learning methods, specifically utilizing a ResNet-based neural network architecture. LiDAR sensors provide accurate and detailed information on terrain, crops, and obstacles, while the ResNet architecture enhances perception capabilities by extracting and analyzing features from LiDAR point cloud data. For smooth and accurate trajectory following, the system employs the iLQR algorithm, which calculates control commands using an optimization-based control method for nonlinear systems. This algorithm ensures robust guidance of the robot along the desired trajectory. By integrating LiDAR perception with the ResNet-based deep learning approach and iLQR control, the system enhances the navigation capabilities of agricultural robots, resulting in reduced operational costs, increased efficiency, and minimized environmental impact.
Analysis of Factors Affecting Interest and Behavior in the Use of Animal Health Information Systems in Central Java Province

The National Animal Health Information System (iSIKHNAS) is an innovation from the Ministry of Agriculture that is used as a tool for collecting information in the field of animal husbandry and animal health. iSIKHNAS aims to provide services and benefits for all users, create efficiency and effectiveness in data and information management, iSIKHNAS can run well if accepted and utilized by officers in the field. This research was conducted to see what factors influence the interest and behavior of using iSIKHNAS by using the UTAUT acceptance method. The method used in this study is quantitative with analysis using SEM-PLS. The results of this study show that the three variables in UTAUT that influence interest in using iSIKHNAS are performance expectations, effort expectations, and social conditions, while usage behavior and utilization of information systems are influenced by facility conditions and usage interest. The hypotheses offered by UTAUT are empirically proven in this study.
14. DATA REPORT: GEOCHEMISTRY AND MINERALOGY OF PERIPLATFORM CARBONATE SEDIMENTS: SITES 1006, 1008, AND 1009 1

An intensive mineralogic and geochemical investigation was conducted on sediments recovered during Ocean Drilling Program Leg 166 from the western Great Bahama Bank at Sites 1006, 1008, and 1009. Pleistocene through middle Miocene sediments recovered from Site 1006, the distal location on the Leg 166 transect, are a mixture of bank-derived and pelagic carbonates with lesser and varying amounts of siliciclastic clays. A thick sequence of Pleistocene periplatform carbonates was recovered near the platform edge at Sites 1008 and 1009. Detailed bulk mineralogic, elemental (Ca, Mg, Sr, and Na), and stable isotopic (δ 18 O and δ 13 C) analyses of sediments are presented from a total of 317 samples from all three sites.
Modeling apical and basal tree contribution to orientation selectivity in a mouse primary visual cortex layer 2/3 pyramidal cell

Pyramidal neurons, a mainstay of cortical regions, receive a plethora of inputs from various areas onto their morphologically distinct apical and basal trees. Both trees differentially contribute to the somatic response, defining distinct anatomical and possibly functional sub-units. To elucidate the contribution of each tree to the encoding of visual stimuli at the somatic level, we modeled the response pattern of a mouse L2/3 V1 pyramidal neuron to orientation tuned synaptic input. Towards this goal, we used a morphologically detailed computational model of a single cell that replicates electrophysiological and two-photon imaging data. Our simulations predict a synergistic effect of apical and basal trees on somatic action potential generation: basal tree activity, in the form of either depolarization or dendritic spiking, is necessary for producing somatic activity, despite the fact that most somatic spikes are heavily driven by apical dendritic spikes. This model provides evidence for synergistic computations taking place in the basal and apical trees of the L2/3 V1 neuron along with mechanistic explanations for tree-specific contributions and emphasizes the potential role of predictive and attentional feedback input in these cells.
SIMUL: Synchronized IMU Dataset of Walking People at Six Body Locations

This work presents SIMUL, a new dataset consisting of 550 minutes of annotated motion data from six synchronized IMUs placed consistently at the same strategically chosen positions on the bodies of 32 participants. With a focus on indoor localization, the selection fell on hand, feet, and trouser pockets. Due to the sensor’s synchronization, this selection allows, for example, to label the data recorded in the hand based on the data captured at the feet. For a better generalizability, the dataset was recorded freestyle under many different environmental conditions and walking speeds. Thus, indoors, numerous floor coverings such as stone, wood, carpet and PVC, as well as stairs are included in the data. The outdoor recordings additionally contain uneven surfaces such as paving stones and slopes. The annotation of the participant’s currently performed activity additionally allows this dataset to be used for activity recognition.
Contribution of tRNA sequence and modifications to the decoding preferences of E. coli and M. mycoides tRNAGlyUCC for synonymous glycine codons

Abstract tRNA superwobbling, used by certain bacteria and organelles, is an intriguing decoding concept in which a single tRNA isoacceptor is used to decode all synonymous codons of a four-fold degenerate codon box. While Escherichia coli relies on three tRNAGly isoacceptors to decode the four glycine codons (GGN), Mycoplasma mycoides requires only a single tRNAGly. Both organisms express tRNAGly with the anticodon UCC, which are remarkably similar in sequence but different in their decoding ability. By systematically introducing mutations and altering the number and type of tRNA modifications using chemically synthesized tRNAs, we elucidated the contribution of individual nucleotides and chemical groups to decoding by the E. coli and M. mycoides tRNAGly. The tRNA sequence was identified as the key factor for superwobbling, revealing the T-arm sequence as a novel pivotal element. In addition, the presence of tRNA modifications, although not essential for providing superwobbling, was shown to delicately fine-tune and balance the decoding of synonymous codons. This emphasizes that the tRNA sequence and its modifications together form an intricate system of high complexity that is indispensable for accurate and efficient decoding.
Two-Stage Rolling Scheduling Strategy For Hybrid Energy Storage With Wind Power Integration

To mitigate the impact of wind power volatility and uncertainty on power system scheduling, a two-stage rolling scheduling strategy considering the hybrid energy storage output characteristics is proposed. During day-ahead scheduling, scenario-based stochastic programming is utilized to determine the scheduling outcome. The intra-day scheduling model prolongs the rolling period to encompass the remaining day and partitions the scheduling scope into two segments with distinct resolutions, effectively addressing the “short-sightedness” issue of the energy storage resource and enhancing computational performance. Additionally, the output characteristics of the hybrid energy storage system consisting of batteries and supercapacitors are considered comprehensively, and the time window for the energy constraint of the super-capacitor is established to achieve ample utilization of the potential provided by hybrid energy storage resources. The proposed rolling scheduling strategy is validated through case studies, confirming its effectiveness.
EfficientNet-based Three Head CNN Model with CLAHE Pre-processing for Accurate Diabetic Retinopathy Detection

Diabetic retinopathy (DR) is an extreme case of diabetes that could lead to lifelong blindness if left untreated in the preliminary stages. Early detection is a challenge and usually requires expert medical inspection of the fundus images. Here we use convolutional neural networks (CNN) to assist in DR detection. Ensembling methods in CNN are known to increase performance and yield better results. A three head CNN model was ensembled to realize classification, regression and ordinal regression tasks as studied in literature. In this paper we modify the three head CNN model to obtain better results for local dataset pre-processed using CLAHE which provides greater feature extraction. This modified model observes an increase in accuracy as compared to the state of the art techniques. Further, dataset collected from Kozhikode District Medical College is used as test set to assess the model for regional differences. The ensembling of heads achieves an accuracy of 96.4% for multistage DR detection. Subsequently binary classification is done using the model to easily predict if the patient has DR or not, achieving an accuracy of 98.36%.