Miracle-dz/UniFAR-FTUs huggingface dataset:
Human-Robot Team Performance Compared to Full Robot Autonomy in 16 Real-World Search and Rescue Missions: Adaptation of the DARPA Subterranean Challenge

Human operators in human-robot teams are commonly perceived to be critical for mission success. To explore the direct and perceived impact of operator input on task success and team performance, 16 real-world missions (10 hrs) were conducted based on the DARPA Subterranean Challenge. These missions were to deploy a heterogeneous team of robots for a search task to locate and identify artifacts such as climbing rope, drills and mannequins representing human survivors. Two conditions were evaluated: human operators that could control the robot team with state-of-the-art autonomy (Human-Robot Team) compared to autonomous missions without human operator input (Robot-Autonomy). Human-Robot Teams were often in directed autonomy mode (70% of mission time), found more items, traversed more distance, covered more unique ground, and had a higher time between safety-related events. Human-Robot Teams were faster at finding the first artifact, but slower to respond to information from the robot team. In routine conditions, scores were comparable for artifacts, distance, and coverage. Reasons for intervention included creating waypoints to prioritise high-yield areas, and to navigate through error-prone spaces. After observing robot autonomy, operators reported increases in robot competency and trust, but that robot behaviour was not always transparent and understandable, even after high mission performance.
IRS-aided UAV for Future Wireless Communications: A Survey and Research Opportunities

— Both unmanned aerial vehicles (UAVs) and intelligent reflecting surfaces (IRS) are gaining traction as transformative technologies for upcoming wireless networks. The IRS-aided UAV communication, which introduces IRSs into UAV communications, has emerged in an effort to improve the system performance while also overcoming UAV communication constraints and issues. The purpose of this paper is to provide a comprehensive overview of IRS-assisted UAV communications. First, we provide five examples of how IRSs and UAVs can be combined to achieve unrivaled potential in difficult situations. The technological features of the most recent relevant researches on IRS-aided UAV communications from the perspective of the main performance criteria, i.e., energy efficiency, security, spectral efficiency , etc. Additionally, previous research studies on technology adoption as machine learning algorithms. Lastly, some promising research directions and open challenges for IRS-aided UAV communication are presented.
Hand-Breathe: Noncontact Monitoring of Breathing Abnormalities From Hand Palm

In the post-COVID-19 world, radio frequency (RF)-based noncontact methods, for example, software-defined radios (SDRs)-based methods, have emerged as promising candidates for intelligent remote sensing of human vitals and could help in the containment of contagious viruses like COVID-19. To this end, this work utilizes the universal software radio peripherals (USRPs)-based SDRs along with classical machine-learning (ML) methods to design a noncontact method to monitor different breathing abnormalities. Under our proposed method, a subject rests his/her hand on a table in between the transmit and receive antennas, while an orthogonal frequency division multiplexing (OFDM) signal passes through the hand. Subsequently, the receiver extracts the channel frequency response (CFR) [basically, fine-grained wireless channel state information (WCSI)] and feeds it to various ML algorithms that eventually classify between different breathing abnormalities. Among all classifiers, the linear support vector machine (SVM) classifier resulted in a maximum accuracy of 88.1%. To train the ML classifiers in a supervised manner, data were collected by doing real-time experiments on four subjects in a laboratory environment. For the label-generation purpose, the breathing of the subjects was classified into three classes: normal, fast, and slow breathing. Furthermore, in addition to our proposed method (where only a hand is exposed to RF signals), we also implemented and tested the state-of-the-art method (where a full chest is exposed to RF radiation). The performance comparison of the two methods reveals a tradeoff, that is, the accuracy of our proposed method is slightly inferior but our method results in minimal body exposure to (nonionizing) RF radiation, compared to the benchmark method.
Wake-Based Locomotion Gait Design for Aerobat

—Flying animals, such as bats, ﬂy through their ﬂuidic environment as they create air jets and form wake structures downstream of their ﬂight path. Bats, in particular, dynamically morph their highly ﬂexible and dexterous armwing to manipulate their ﬂuidic environment which is key to their agility and ﬂight efﬁciency. This paper presents the theoretical and numerical analysis of the wake-structure-based gait design inspired by bat ﬂight for ﬂapping robots using the notion of reduced-order models and unsteady aerodynamic model incorporating Wagner function. The objective of this paper is to introduce the notion of gait design for ﬂapping robots by systematically searching the design space in the context of optimization. The solution found using our gait design framework was used to design and test a ﬂapping robot.
Lambretta: Learning to Rank for Twitter Soft Moderation

To curb the problem of false information, social media platforms like Twitter started adding warning labels to content discussing debunked narratives, with the goal of providing more context to their audiences. Unfortunately, these labels are not applied uniformly and leave large amounts of false content unmoderated. This paper presents LAMBRETTA, a system that automatically identifies tweets that are candidates for soft moderation using Learning To Rank (LTR). We run Lambretta on Twitter data to moderate false claims related to the 2020 US Election and find that it flags over 20 times more tweets than Twitter, with only 3.93% false positives and 18.81% false negatives, outperforming alternative state-of-the-art methods based on keyword extraction and semantic search. Overall, LAMBRETTA assists human moderators in identifying and flagging false information on social media.
GT-CausIn: a novel causal-based insight for traffic prediction

Traffic forecasting is an important application of spatiotemporal series prediction. Among different methods, graph neural networks have achieved so far the most promising results, learning relations between graph nodes then becomes a crucial task. However, improvement space is very limited when these relations are learned in a node-to-node manner. The challenge stems from (1) obscure temporal dependencies between different stations, (2) difficulties in defining variables beyond the node level, and (3) no ready-made method to validate the learned relations. To confront these challenges, we define legitimate traffic causal variables to discover the causal relation inside the traffic network, which is carefully checked with statistic tools and case analysis. We then present a novel model named Graph Spatial-Temporal Network Based on Causal Insight (GT-CausIn), where prior learned causal information is integrated with graph diffusion layers and temporal convolutional network (TCN) layers. Experiments are carried out on two real-world traffic datasets: PEMS-BAY and METR-LA, which show that GT-CausIn significantly outperforms the state-of-the-art models on mid-term and long-term prediction.
Co-simulation domain decomposition algorithm for hybrid EMT-Dynamic Phasor modeling

An iterative coupling algorithm based on restricted additive Schwarz domain decomposition is investigated to co-simulate electrical circuits with hybrid electromagnetic (EMT) and transient stability (TS) modeled using dynamic phasors. This co-simulation algorithm does not introduce any delay between the data exchanged at the co-simulation step. The pure linear convergence property of the iterative method allows it to be accelerated towards the true solution by a non-intrusive Aitken's acceleration of the convergence post-processing, even if the domain decomposition interface conditions make the iterative method divergent. This provides a method less sensitive to the splitting. Numerical tests on a linear RLC circuit combining EMT and TS modeling are provided.
HoloBeam: Paper-Thin Near-Eye Displays

An emerging alternative to conventional Augmented Reality (AR) glasses designs, Beaming displays promise slim AR glasses free from challenging design trade-offs, including battery-related limits or computational budget-related issues. These beaming displays remove active components such as batteries and electronics from AR glasses and move them to a projector that projects images to a user from a distance (1–2 meters), where users wear only passive optical eyepieces. However, earlier implementations of these displays delivered poor resolutions (7 cycles per degree) without any optical focus cues and were introduced with a bulky form-factor eyepiece ($\sim 50\ mm$ thick). This paper introduces a new milestone for beaming displays, which we call HoloBeam. In this new design, a custom holographic projector populates a micro-volume located at some distance (1–2 meters) with multiple planes of images. Users view magnified copies of these images from this small volume with the help of an eyepiece that is either a Holographic Optical Element (HOE) or a set of lenses. Our HoloBeam prototypes demonstrate the thinnest AR glasses to date with submillimeter thickness (e.g., HOE film is only $120\ \mu m$ thick). In addition, HoloBeam prototypes demonstrate near retinal resolutions (24 cycles per degree) with a 70 degrees-wide field of view.
Optimized Sparse Matrix Operations for Reverse Mode Automatic Differentiation

Sparse matrix representations are ubiquitous in computational science and machine learning, leading to significant reductions in compute time, in comparison to dense representation, for problems that have local connectivity. The adoption of sparse representation in leading ML frameworks such as PyTorch is incomplete, however, with support for both automatic differentiation and GPU acceleration missing. In this work, we present an implementation of a CSR-based sparse matrix wrapper for PyTorch with CUDA acceleration for basic matrix operations, as well as automatic differentiability. We also present several applications of the resulting sparse kernels to optimization problems, demonstrating ease of implementation and performance measurements versus their dense counterparts.
Two-Dimensional Sloshing: Domains with Interior “High Spots”

Considering the two-dimensional sloshing problem, our main focus is to construct domains with interior high spots; that is, points, where the free surface elevation for the fundamental eigenmode attains its critical values. The so-called semi-inverse procedure is applied for this purpose. The existence of high spots is proved rigorously for some domains. Many of the constructed domains have multiple interior high spots and all of them are bulbous at least on one side.
Collective Vector Clocks: Low-Overhead Transparent Checkpointing for MPI

Taking snapshots of the state of a distributed computation is useful for off-line analysis of the computational state, for later restarting from the saved snapshot, for cloning a copy of the computation, and for migration to a new cluster. The problem is made more difficult when supporting collective operations across processes, such as barrier, reduce operations, scatter and gather, etc. Some processes may have reached the barrier or other collective operation, while other processes wait a long time to reach that same barrier or collective operation. At least two solutions are well-known in the literature: (I) draining in-flight network messages and then freezing the network at checkpoint time; and (ii) adding a barrier prior to the collective operation, and either completing the operation or aborting the barrier if not all processes are present. Both solutions suffer important drawbacks. The code in the first solution must be updated whenever one ports to a newer network. The second solution implies additional barrier-related network traffic prior to each collective operation. This work presents a third solution that avoids both drawbacks. There is no additional barrier-related traffic, and the solution is implemented entirely above the network layer. The work is demonstrated in the context of transparent checkpointing of MPI libraries for parallel computation, where each of the first two solutions have already been used in prior systems, and then abandoned due to the aforementioned drawbacks. Experiments demonstrate the low runtime overhead of this new, network-agnostic approach. The approach is also extended to non-blocking, collective operations in order to handle overlapping of computation and communication.
Punctuation Restoration for Singaporean Spoken Languages: English, Malay, and Mandarin

This paper presents the work of restoring punctuation for ASR transcripts generated by multilingual ASR systems. The focus languages are English, Mandarin, and Malay which are three of the most popular languages in Singapore. To the best of our knowledge, this is the first system that can tackle punctuation restoration for these three languages simultaneously. Traditional approaches usually treat the task as a sequential labeling task, however, this work adopts a slot-filling approach that predicts the presence and type of punctuation marks at each word boundary. The approach is similar to the Masked-Language Model approach employed during the pre-training stages of BERT, but instead of predicting the masked word, our model predicts masked punctuation. Additionally, we find that using Jieba11https://github.com/fxsjy/jieba instead of only using the built-in SentencePiece tokenizer of XLM-R can significantly improve the performance of punctuating Mandarin transcripts. Experimental results on English and Mandarin IWSLT2022 datasets and Malay News show that the proposed approach achieved state-of-the-art results for Mandarin with 73.8% F1-score while maintaining a reasonable F1-score for English and Malay, i.e. 74.7% and 78% respectively. Our source code that allows reproducing the results and building a simple web-based application for demonstration purposes is available on Github22https://github.com/AetherPrior/Multilingual-Sentence-Boundary-detection.
Symmetries and asymmetries in the neural encoding of 3D space

The neural coding of space centres on three foundational cell types: place cells, head direction cells and grid cells. One notable characteristic of these neurons is the symmetry properties of their spatial firing patterns. In symmetric environments, firing patterns are often also symmetric: for example, place cells show translational symmetry in aligned sub-compartments of a multi-compartment environment. A single head direction cell has a mirror-symmetric firing pattern, while a sub-class of head direction cells can show multi-fold rotational symmetries in multi-compartment environments, matching the symmetry of the recently experienced environment. The entorhinal grid cells are notable for the symmetry of their firing patterns in both rotational and translational domains. However, these symmetries are broken in a variety of situations. These symmetry-making and -breaking observations shed light on the underlying computations that generate these firing patterns, and also invite speculation as to whether they may have a functional role. This article outlines these findings and speculates on the consequences of the resultant firing symmetries and asymmetries for spatial coding and cognition. This article is part of a discussion meeting issue ‘New approaches to 3D vision’.
Learning robotic navigation from experience: principles, methods and recent results

Navigation is one of the most heavily studied problems in robotics and is conventionally approached as a geometric mapping and planning problem. However, real-world navigation presents a complex set of physical challenges that defies simple geometric abstractions. Machine learning offers a promising way to go beyond geometry and conventional planning, allowing for navigational systems that make decisions based on actual prior experience. Such systems can reason about traversability in ways that go beyond geometry, accounting for the physical outcomes of their actions and exploiting patterns in real-world environments. They can also improve as more data is collected, potentially providing a powerful network effect. In this article, we present a general toolkit for experiential learning of robotic navigation skills that unifies several recent approaches, describe the underlying design principles, summarize experimental results from several of our recent papers, and discuss open problems and directions for future work. This article is part of the theme issue ‘New approaches to 3D vision’.
Do you hear what I see? How do early blind individuals experience object motion?

One of the most important tasks for 3D vision is tracking the movement of objects in space. The ability of early blind individuals to understand motion in the environment from noisy and unreliable auditory information is an impressive example of cortical adaptation that is only just beginning to be understood. Here, we compare visual and auditory motion processing, and discuss the effect of early blindness on the perception of auditory motion. Blindness leads to cross-modal recruitment of the visual motion area hMT+ for auditory motion processing. Meanwhile, the planum temporale, associated with auditory motion in sighted individuals, shows reduced selectivity for auditory motion. We discuss how this dramatic shift in the cortical basis of motion processing might influence the perceptual experience of motion in early blind individuals. This article is part of a discussion meeting issue ‘New approaches to 3D vision’.
Research on Forest Phenology Prediction Based on LSTM and GRU Model

Abstract: Research on forest phenology is an important parameter related to climate and environmental changes. An optical camera was used as a near-earth remote sensing satellite device to obtain forest images, and the data of Green excess index (GEI) in the images were calculated, which was fitted with the seasonal variation curve of GEI data by double Logistic method and normalization method. LSTM and GRU deep learning models were introduced to train and test the GEI data. Moreover, the rationality and performance evaluation of the deep learning model were verified, and finally the model predicted the trend of GEI data in the next 60 days. Results showed: In the aspects of forest phenology training and prediction, GRU and LSTM models were verified by histograms and autocorrelation graphs, indicating that the distribution of predicted data was consistent with the trend of real data, LSTM and GRU model data were feasible and the model was stable. The differences of MSE, RMSE, MAE and MAPE between LSTM model and GRU model were 0.0014, 0.013, 0.008 and 5.26%, respectively. GRU had higher performance than LSTM. The prediction of LSTM and GRU models about GEI data for the next 60 days both showed a trend chart consistent with the change trend of GEI data in the first half of the year. GRU and LSTM were used to predict GEI data by deep learning model, and the response of LSTM and GRU deep learning models in forest phenology prediction was realized, and the performance of GRU was better than that of LSTM model. It could further reveal the growth and climate change of forest phenology in the future, and provide a theoretical basis for the application of forest phenology prediction.
Der Mensch und seine Schmerzen – Möglichkeiten und Grenzen der Erfahrbarkeit der Schmerzen des anderen

Zusammenfassung Der Schmerz nimmt als primum movens für den Arztbesuch, durch seinen diagnostischen Stellenwert und als Indikator für die Therapiewirksamkeit eine zentrale Stellung im Arzt-Patienten-Verhältnis ein. Die Frage, ob und ggf. wie der Schmerz des anderen erfahrbar ist, hat epistemische Bedeutung. Der Schmerz als Sinneszustand muss vom Schmerz als Bewusstseinsphänomen unterschieden werden. Qualia werden von Nagel als „ what is it like “- Zustände beschrieben, die im phänomenalen Bewusstsein erlebt werden. Sie sind phänomenale Zustände und charakterisiert durch den qualitativen Charakter eines unmittelbar gegebenen subjektiven Gefühlsinhaltes. Der Schmerz als Quale ist subjektiv und an die Erste-Person-Perspektive gebunden. Phänomenale Zustände entziehen sich der begrifflichen Beschreibbarkeit. Wissenschaftler und Ärzte untersuchen den Schmerz objektiv aus der Dritte-Person-Perspektive. Abstraktion und Objektivierung werden dem subjektiven phänomenalen Schmerzerleben nicht gerecht. Die Erklärungslücke bei Levine besteht, weil es keinen Nexus zwischen der physischen Reizung einer C-Schmerzfaser und dem Auftreten der phänomenalen Eigenschaft des Schmerzes (Bewusstseinsphänomen) gibt. Nagels Thesen zeigen, dass Tatsachen, die phänomenale Zustände betreffen, keine physikalischen Tatsachen sind. Erstere sind nur Wesen zugänglich, die die geeignete Erfahrungsperspektive einnehmen können. Der Schmerz des anderen als Quale ist für den Arzt nicht erfahrbar. Medizinische Untersuchungsverfahren in der Medizin sind Surrogate für das Schmerzverständnis, die zugleich für die ärztliche Diagnostik und Therapie unverzichtbar sind Wiehl postuliert (in der Auseinandersetzung mit Viktor von Weizsäckers Aufsatz „Die Schmerzen“), aufgrund der polaren Struktur des Schmerzes, dass der Schmerz, indem er individuell und spezifisch sei, gerade auch ein allgemeiner Schmerz sei. Wenn in der empathischen Begegnung von Patient und Arzt der Schmerz des anderen mitteilbar wird, könnte durch diesen sprachlichen Annäherungsversuch auf der Seite des Therapeuten modifiziert etwas vom phänomenalen Erleben des Patienten erfahrbar werden.
A Switching NMOS Based Single Ended Sense Amplifier for High Density SRAM Applications

The demand for single ended static random access memory is growing, driven by the decreasing technology node and increasing processing load. This mandates the need for a single ended sense amplifier to be used along with the memory. Consequently, a single ended latch based sense amplifier is proposed in this article for high-speed, low power application. The sense amplifier is designed at a 32 nm technology node and its functioning is analyzed at 1 V supply voltage, while the environment temperature is maintained at 27 °C. It is analyzed for its delay, temperature tolerance, variability tolerance, and area occupancy. The delay requirement of 0.2 ns for the proposed scheme is significantly lower in comparison to its other counter parts. While, its false read time is 0. 3μs. In terms of power consumption, the proposed sensing topology is marginally higher than SPSS, but its leakage power is 1.4 times less than SPSS. The major advantage of the proposed SA is its reduced area footprint of 7.65 μm2, which is 1.78 times better than the best pre-existing topology in terms of area.
Invariants of Universal Enveloping Algebras of Relatively Free Lie Algebras

Let FmW be the relatively free algebra of rank m ≥ 2 in the nonlocally nilpotent variety W of Lie algebras over an infinite field of any characteristic. We study the problem of finite generation of the algebra of invariants of a cyclic linear group G = 〈g〉 of finite order invertible in the base field, acting on the universal enveloping algebra U(FmW). If the matrix g has eigenvalues of different multiplicative orders, then we show that the algebra of invariants U(FmW)G is not finitely generated. If all eigenvalues of g ate of the same order and W is a subvariety of the variety RcU of all nilpotent of class c-by-abelian algebras for some c ≥ 1, then the algebra of invariants is finitely generated. On the other hand, for every g which is not a scalar matrix, there exists a variety of Lie algebras W such that the algebra U(FmW)G is not finitely generated.