OpenRaiser/Intern-Atlas huggingface dataset:
Anchor Distance for 3D Multi-Object Distance Estimation from 2D Single Shot
Venue: arXiv cs.CV 2021
Year: 2021

Visual perception of the objects in a 3D environment is a key to successful performance in autonomous driving and simultaneous localization and mapping (SLAM). In this paper, we present a real time approach for estimating the distances to multiple objects in a scene using only a single-shot image. Given a 2D Bounding Box (BBox) and object parameters, a 3D distance to the object can be calculated directly using 3D reprojection; however, such methods are prone to significant errors because an error from the 2D detection can be amplified in 3D. In addition, it is also challenging to apply such methods to a real-time system due to the computational burden. In the case of the traditional multi-object detection methods, %they mostly pay attention to existing works have been developed for specific tasks such as object segmentation or 2D BBox regression. These methods introduce the concept of anchor BBox for elaborate 2D BBox estimation, and predictors are specialized and trained for specific 2D BBoxes. In order to estimate the distances to the 3D objects from a single 2D image, we introduce the notion of \textit{anchor distance} based on an object's location and propose a method that applies the anchor distance to the multi-object detector structure. We let the predictors catch the distance prior using anchor distance and train the network based on the distance. The predictors can be characterized to the objects located in a specific distance range. By propagating the distance prior using a distance anchor to the predictors, it is feasible to perform the precise distance estimation and real-time execution simultaneously. The proposed method achieves about 30 FPS speed, and shows the lowest RMSE compared to the existing methods.
Cloud, Fog or Edge: Where to Compute?
Venue: arXiv cs.DC 2021
Year: 2021

The computing continuum extends the high-performance cloud data centers with energy-efficient and low-latency devices close to the data sources located at the edge of the network. However, the heterogeneity of the computing continuum raises multiple challenges related to application management. These include where to offload an application - from the cloud to the edge - to meet its computation and communication requirements. To support these decisions, we provide in this article a detailed performance and carbon footprint analysis of a selection of use case applications with complementary resource requirements across the computing continuum over a real-life evaluation testbed.
Resource Allocation for Vehicle Platooning in 5G NR-V2X via Deep Reinforcement Learning
Venue: arXiv cs.NI 2021
Year: 2021

Vehicle platooning, one of the advanced services supported by 5G NR-V2X, improves traffic efficiency in the connected intelligent transportation systems (C-ITSs). However, the packet delivery ratio of platoon communication, especially in the out-of-coverage area, is significantly impacted by the random selection algorithms employed in the current resource allocation scheme. In this paper, we first analyze the collision probability via the random selection algorithm adopted in the current standard. Subsequently, we then investigate the deep reinforcement learning (DRL) algorithm that decreases the collision probability by letting the agent (vehicle) learn from the communication environment. Monte Carlo simulation is utilized to verify the results obtained in the analytical model and to compare the results between the two discussed algorithms. Numerical results show that the proposed DRL algorithm outperforms the random selection algorithm in terms of different vehicle density, which at least lowering the collision probability by 73% and 45% in low and high vehicle density respectively.
GnetSeg: Semantic Segmentation Model Optimized on a 224mW CNN Accelerator Chip at the Speed of 318FPS
Venue: arXiv cs.CV 2021
Year: 2021

Semantic segmentation is the task to cluster pixels on an image belonging to the same class. It is widely used in the real-world applications including autonomous driving, medical imaging analysis, industrial inspection, smartphone camera for person segmentation and so on. Accelerating the semantic segmentation models on the mobile and edge devices are practical needs for the industry. Recent years have witnessed the wide availability of CNN (Convolutional Neural Networks) accelerators. They have the advantages on power efficiency, inference speed, which are ideal for accelerating the semantic segmentation models on the edge devices. However, the CNN accelerator chips also have the limitations on flexibility and memory. In addition, the CPU load is very critical because the CNN accelerator chip works as a co-processor with a host CPU. In this paper, we optimize the semantic segmentation model in order to fully utilize the limited memory and the supported operators on the CNN accelerator chips, and at the same time reduce the CPU load of the CNN model to zero. The resulting model is called GnetSeg. Furthermore, we propose the integer encoding for the mask of the GnetSeg model, which minimizes the latency of data transfer between the CNN accelerator and the host CPU. The experimental result shows that the model running on the 224mW chip achieves the speed of 318FPS with excellent accuracy for applications such as person segmentation.
Dairy Cow rumination detection: A deep learning approach
Venue: arXiv cs.CV 2021
Year: 2021

Cattle activity is an essential index for monitoring health and welfare of the ruminants. Thus, changes in the livestock behavior are a critical indicator for early detection and prevention of several diseases. Rumination behavior is a significant variable for tracking the development and yield of animal husbandry. Therefore, various monitoring methods and measurement equipment have been used to assess cattle behavior. However, these modern attached devices are invasive, stressful and uncomfortable for the cattle and can influence negatively the welfare and diurnal behavior of the animal. Multiple research efforts addressed the problem of rumination detection by adopting new methods by relying on visual features. However, they only use few postures of the dairy cow to recognize the rumination or feeding behavior. In this study, we introduce an innovative monitoring method using Convolution Neural Network (CNN)-based deep learning models. The classification process is conducted under two main labels: ruminating and other, using all cow postures captured by the monitoring camera. Our proposed system is simple and easy-to-use which is able to capture long-term dynamics using a compacted representation of a video in a single 2D image. This method proved efficiency in recognizing the rumination behavior with 95%, 98% and 98% of average accuracy, recall and precision, respectively.
Shift-Table: A Low-latency Learned Index for Range Queries using Model Correction
Venue: arXiv cs.DB 2021
Year: 2021

Indexing large-scale databases in main memory is still challenging today. Learned index structures -- in which the core components of classical indexes are replaced with machine learning models -- have recently been suggested to significantly improve performance for read-only range queries. However, a recent benchmark study shows that learned indexes only achieve limited performance improvements for real-world data on modern hardware. More specifically, a learned model cannot learn the micro-level details and fluctuations of data distributions thus resulting in poor accuracy; or it can fit to the data distribution at the cost of training a big model whose parameters cannot fit into cache. As a consequence, querying a learned index on real-world data takes a substantial number of memory lookups, thereby degrading performance. In this paper, we adopt a different approach for modeling a data distribution that complements the model fitting approach of learned indexes. We propose Shift-Table, an algorithmic layer that captures the micro-level data distribution and resolves the local biases of a learned model at the cost of at most one memory lookup. Our suggested model combines the low latency of lookup tables with learned indexes and enables low-latency processing of range queries. Using Shift-Table, we achieve a speedup of 1.5X to 2X on real-world datasets compared to trained and tuned learned indexes.
Constant Amortized Time Enumeration of Eulerian trails
Venue: arXiv cs.DS 2021
Year: 2021

In this paper, we consider enumeration problems for edge-distinct and vertex-distinct Eulerian trails. Here, two Eulerian trails are \emph{edge-distinct} if the edge sequences are not identical, and they are \emph{vertex-distinct} if the vertex sequences are not identical. As the main result, we propose optimal enumeration algorithms for both problems, that is, these algorithm runs in $\mathcal{O}(N)$ total time, where $N$ is the number of solutions. Our algorithms are based on the reverse search technique introduced by [Avis and Fukuda, DAM 1996], and the push out amortization technique introduced by [Uno, WADS 2015].
Cyber Kittens, or Some First Steps Towards Categorical Cybernetics
Venue: arXiv cs.NE 2021
Year: 2021

We define a categorical notion of cybernetic system as a dynamical realisation of a generalized open game, along with a coherence condition. We show that this notion captures a wide class of cybernetic systems in computational neuroscience and statistical machine learning, exposes their compositional structure, and gives an abstract justification for the bidirectional structure empirically observed in cortical circuits. Our construction is built on the observation that Bayesian updates compose optically, a fact which we prove along the way, via a fibred category of state-dependent stochastic channels.
Reverse Derivative Ascent: A Categorical Approach to Learning Boolean Circuits
Venue: arXiv cs.LO 2021
Year: 2021

We introduce Reverse Derivative Ascent: a categorical analogue of gradient based methods for machine learning. Our algorithm is defined at the level of so-called reverse differential categories. It can be used to learn the parameters of models which are expressed as morphisms of such categories. Our motivating example is boolean circuits: we show how our algorithm can be applied to such circuits by using the theory of reverse differential categories. Note our methodology allows us to learn the parameters of boolean circuits directly, in contrast to existing binarised neural network approaches. Moreover, we demonstrate its empirical value by giving experimental results on benchmark machine learning datasets.
Behavioral Mereology: A Modal Logic for Passing Constraints
Venue: arXiv cs.LO 2021
Year: 2021

Mereology is the study of parts and the relationships that hold between them. We introduce a behavioral approach to mereology, in which systems and their parts are known only by the types of behavior they can exhibit. Our discussion is formally topos-theoretic, and agnostic to the topos, providing maximal generality; however, by using only its internal logic we can hide the details and readers may assume a completely elementary set-theoretic discussion. We consider the relationship between various parts of a whole in terms of how behavioral constraints are passed between them, and give an inter-modal logic that generalizes the usual alethic modalities in the setting of symmetric accessibility.
Model-agnostic interpretation by visualization of feature perturbations
Venue: arXiv cs.LG 2021
Year: 2021

Interpretation of machine learning models has become one of the most important research topics due to the necessity of maintaining control and avoiding bias in these algorithms. Since many machine learning algorithms are published every day, there is a need for novel model-agnostic interpretation approaches that could be used to interpret a great variety of algorithms. Thus, one advantageous way to interpret machine learning models is to feed different input data to understand the changes in the prediction. Using such an approach, practitioners can define relations among data patterns and a model's decision. This work proposes a model-agnostic interpretation approach that uses visualization of feature perturbations induced by the PSO algorithm. We validate our approach on publicly available datasets, showing the capability to enhance the interpretation of different classifiers while yielding very stable results compared with state-of-the-art algorithms.
Human Centric Accessibility Graph For Environment Analysis
Venue: arXiv cs.GR 2021
Year: 2021

Understanding design decisions in relation to the future occupants of a building is a crucial part of good design. However, limitations in tools and expertise hinder meaningful human-centric decisions during the design process. In this paper, a novel Spatial Human Accessibility graph for Planning and Environment Analysis (SHAPE) is introduced that brings together the technical challenges of discrete representations of digital models, with human-based metrics for evaluating the environment. SHAPE: does not need labeled geometry as input, works with multi-level buildings, captures surface variations (e.g., slopes in a terrain), and can be used with existing graph theory (e.g., gravity, centrality) techniques. SHAPE uses ray-casting to perform a search, generating a dense graph of all accessible locations within the environment and storing the type of travel required in a graph (e.g., up a slope, down a step). The ability to simultaneously evaluate and plan paths from multiple human factors is shown to work on digital models across room, building, and topography scales. The results enable designers and planners to evaluate options of the built environment in new ways, and at higher fidelity, that will lead to more human-friendly and accessible environments.
On the Evaluation of Vision-and-Language Navigation Instructions
Venue: arXiv cs.AI 2021
Year: 2021

Vision-and-Language Navigation wayfinding agents can be enhanced by exploiting automatically generated navigation instructions. However, existing instruction generators have not been comprehensively evaluated, and the automatic evaluation metrics used to develop them have not been validated. Using human wayfinders, we show that these generators perform on par with or only slightly better than a template-based generator and far worse than human instructors. Furthermore, we discover that BLEU, ROUGE, METEOR and CIDEr are ineffective for evaluating grounded navigation instructions. To improve instruction evaluation, we propose an instruction-trajectory compatibility model that operates without reference instructions. Our model shows the highest correlation with human wayfinding outcomes when scoring individual instructions. For ranking instruction generation systems, if reference instructions are available we recommend using SPICE.
Ear Recognition
Venue: arXiv cs.CV 2021
Year: 2021

Ear recognition can be described as a revived scientific field. Ear biometrics were long believed to not be accurate enough and held a secondary place in scientific research, being seen as only complementary to other types of biometrics, due to difficulties in measuring correctly the ear characteristics and the potential occlusion of the ear by hair, clothes and ear jewellery. However, recent research has reinstated them as a vivid research field, after having addressed these problems and proven that ear biometrics can provide really accurate identification and verification results. Several 2D and 3D imaging techniques, as well as acoustical techniques using sound emission and reflection, have been developed and studied for ear recognition, while there have also been significant advances towards a fully automated recognition of the ear. Furthermore, ear biometrics have been proven to be mostly non-invasive, adequately permanent and accurate, and hard to spoof and counterfeit. Moreover, different ear recognition techniques have proven to be as effective as face recognition ones, thus providing the opportunity for ear recognition to be used in identification and verification applications. Finally, even though some issues still remain open and require further research, the scientific field of ear biometrics has proven to be not only viable, but really thriving.
Routing Packet Traffic via Enhanced Access Control List for Network Congestion Avoidance
Venue: arXiv cs.NI 2021
Year: 2021

Filtering packet traffic and rules of permit/denial of data packets into network nodes are granted by facilitating Access Control Lists (ACL). This paper proposes a procedure of adding a link load threshold value to the access control list rules option, which acts on the basis of threshold value. The ultimate goal of this enhanced ACL is to avoid congestion in targeted subnetworks. The link load threshold value allows to decide that packet traffic is rerouted by the router to avoid congestion, or packet drop happens on the basis of packet priorities. The packet rerouting in case of high traffic loads, based on new packet filtering procedure for congestion avoidance, will result in the reduction of the overall packet drop ratio, and of over-subscription in congested subnetworks.
Coloring the Black Box: What Synesthesia Tells Us about Character Embeddings
Venue: arXiv cs.CL 2021
Year: 2021

In contrast to their word- or sentence-level counterparts, character embeddings are still poorly understood. We aim at closing this gap with an in-depth study of English character embeddings. For this, we use resources from research on grapheme-color synesthesia -- a neuropsychological phenomenon where letters are associated with colors, which give us insight into which characters are similar for synesthetes and how characters are organized in color space. Comparing 10 different character embeddings, we ask: How similar are character embeddings to a synesthete's perception of characters? And how similar are character embeddings extracted from different models? We find that LSTMs agree with humans more than transformers. Comparing across tasks, grapheme-to-phoneme conversion results in the most human-like character embeddings. Finally, ELMo embeddings differ from both humans and other models.
Low Resource Recognition and Linking of Biomedical Concepts from a Large Ontology
Venue: arXiv cs.CL 2021
Year: 2021

Tools to explore scientific literature are essential for scientists, especially in biomedicine, where about a million new papers are published every year. Many such tools provide users the ability to search for specific entities (e.g. proteins, diseases) by tracking their mentions in papers. PubMed, the most well known database of biomedical papers, relies on human curators to add these annotations. This can take several weeks for new papers, and not all papers get tagged. Machine learning models have been developed to facilitate the semantic indexing of scientific papers. However their performance on the more comprehensive ontologies of biomedical concepts does not reach the levels of typical entity recognition problems studied in NLP. In large part this is due to their low resources, where the ontologies are large, there is a lack of descriptive text defining most entities, and labeled data can only cover a small portion of the ontology. In this paper, we develop a new model that overcomes these challenges by (1) generalizing to entities unseen at training time, and (2) incorporating linking predictions into the mention segmentation decisions. Our approach achieves new state-of-the-art results for the UMLS ontology in both traditional recognition/linking (+8 F1 pts) as well as semantic indexing-based evaluation (+10 F1 pts).
Design, analysis and control of the series-parallel hybrid RH5 humanoid robot
Venue: arXiv cs.RO 2021
Year: 2021

Last decades of humanoid research has shown that humanoids developed for high dynamic performance require a stiff structure and optimal distribution of mass--inertial properties. Humanoid robots built with a purely tree type architecture tend to be bulky and usually suffer from velocity and force/torque limitations. This paper presents a novel series-parallel hybrid humanoid called RH5 which is 2 m tall and weighs only 62.5 kg capable of performing heavy-duty dynamic tasks with 5 kg payloads in each hand. The analysis and control of this humanoid is performed with whole-body trajectory optimization technique based on differential dynamic programming (DDP). Additionally, we present an improved contact stability soft-constrained DDP algorithm which is able to generate physically consistent walking trajectories for the humanoid that can be tracked via a simple PD position control in a physics simulator. Finally, we showcase preliminary experimental results on the RH5 humanoid robot.
The Granularity Gap Problem: A Hurdle for Applying Approximate Memory to Complex Data Layout
Venue: arXiv cs.ET 2021
Year: 2021

The main memory access latency has not much improved for more than two decades while the CPU performance had been exponentially increasing until recently. Approximate memory is a technique to reduce the DRAM access latency in return of losing data integrity. It is beneficial for applications that are robust to noisy input and intermediate data such as artificial intelligence, multimedia processing, and graph processing. To obtain reasonable outputs from applications on approximate memory, it is crucial to protect critical data while accelerating accesses to non-critical data. We refer the minimum size of a continuous memory region that the same error rate is applied in approximate memory to as the approximation granularity. A fundamental limitation of approximate memory is that the approximation granularity is as large as a few kilo bytes. However, applications may have critical and non-critical data interleaved with smaller granularity. For example, a data structure for graph nodes can have pointers (critical) to neighboring nodes and its score (non-critical, depending on the use-case). This data structure cannot be directly mapped to approximate memory due to the gap between the approximation granularity and the granularity of data criticality. We refer to this issue as the granularity gap problem. In this paper, we first show that many applications potentially suffer from this problem. Then we propose a framework to quantitatively evaluate the performance overhead of a possible method to avoid this problem using known techniques. The evaluation results show that the performance overhead is non-negligible compared to expected benefit from approximate memory, suggesting that the granularity gap problem is a significant concern.
Spreading dynamics of a 2SIH2R, rumor spreading model in the homogeneous network
Venue: arXiv cs.SI 2021
Year: 2021

In the era of the rapid development of the Internet, the threshold for information spreading has become lower. Most of the time, rumors, as a special kind of information, are harmful to society. And once the rumor appears, the truth will follow. Considering that the rumor and truth compete with each other like light and darkness in reality, in this paper, we study a rumor spreading model in the homogeneous network called 2SIH2R, in which there are both spreader1(people who spread the rumor) and spreader2(people who spread the truth). In this model, we introduced discernible mechanism and confrontation mechanism to quantify the level of people's cognitive abilities and the competition between the rumor and truth. By mean-field equations, steady-state analysis and numerical simulations in a generated network which is closed and homogeneous, some significant results can be given: the higher discernible rate of the rumor, the smaller influence of the rumor; the stronger confrontation degree of the rumor, the smaller influence of the rumor; the large average degree of the network, the greater influence of the rumor but the shorter duration. The model and simulation results provide a quantitative reference for revealing and controlling the spread of the rumor.