Miracle-dz/UniFAR-FTUs huggingface dataset:
eNEUTRAL IoNT: Energy-Neutral Event Monitoring for Internet of Nano Things

Advancements in nanotechnology promise new capabilities for the Internet of Things (IoT) to monitor extremely fine-grained events with sensors as small as a hundred nanometers. Researchers predict that such tiny sensors can be connected to the Internet using graphene-based nano-antenna radiating in the terahertz band, giving rise to the so-called Internet of Nano-Things (IoNT). Powering such wireless communications with nanoscale energy supply, however, is a major challenge to overcome. Since in many application domains, different types of events discharge different amounts of energy to the environment, we propose an energy-neutral event monitoring framework, called eNEUTRAL IoNT, that allows the sensors to transmit event information using only the amount of energy harvested from the events. We design and analyze two implementation options for this framework. The first option uses a single pulse containing the entire energy harvested from the event but manipulates its time duration to create a unique pulse amplitude for a given combination of event type and its location. In the second option, the harvested event energy is divided into two pulses so that the energy of the first pulse uniquely defines a location and the second pulse uses the remaining energy to identify event types. To minimize classification error at the receiver, we optimize pulse durations in the single-pulse option and pulse energies in the dual-pulse option. Feasibility of eNEUTRAL IoNT is demonstrated using extensive numerical experiments involving terahertz channels. We find that the dual-pulse approach significantly outperforms the single-pulse approach achieving 99% accuracy for detecting both location and event type in 10-node network monitoring two different event types for a radius of 28 mm.
Low Threshold Acquisition Controller for Skipper Charge Coupled Devices

In this work, the design and preliminary results of the first specially design Skipper-CCD controller, aimed for low threshold particle and astronomy experiments, is presented. The system integrates on a single board: the generation of the clock and bias signals for the sensor; four high-speed analog-to-digital converters to digitize the video channels of the CCD; Artix-7 FPGA to perform pixel calculation and board control; a front panel connector for all the signal inspection; and Ethernet port for full operation and data collection through standard network connection to a PC. The system has been fully tested and sample images were taken for both conventional and Skipper-CCDs.
Survival of Korean Huntington’s Disease Patients

Objective The survival of Huntington’s disease (HD) patients is reported to be 15–20 years. However, most studies on the survival of HD have been conducted in patients without genetic confirmation with the possible inclusion of non-HD patients, and all studies have been conducted in Western countries. The survival of patients with HD in East Asia, where its prevalence is 10–50-fold lower compared with Western populations, has not yet been reported. Methods Forty-seven genetically confirmed Korean HD patients from independent families were included in this retrospective medical record review study. Results The mean age at onset among the 47 patients was 46.1 ± 14.0 years. At the time of data collection, 25 patients had died, and these patients had a mean age at death of 57.8 ± 13.7 years. The Kaplan-Meier estimate of the median survival from onset in the 47 patients was 14.5 years (95% confidence interval: 12.3–16.6). None of the following factors were associated with the survival time in the univariate Cox regression analysis: gender, age at onset, normal CAG repeat size, mutant CAG repeat size, and the absence or presence of non-motor symptoms at onset. Conclusion This is the first Asian study on survival in HD patients. Survival in Korean HD patients may be shorter than that reported for Western populations, or at least is in the lower range of expected survival. A larger longitudinal observation study is needed to confirm the results found in this study.
Learning Abstract Concepts from Multi-Modal Data: Since You Probably Can’t See What I Mean

Models that acquire semantic representations from both linguistic and perceptual input outperform linguistic-only models on various NLP tasks. However, this superiority has only been established when learning concrete concepts, which are usually domain specific and also comparatively rare in everyday language. We extend the scope to more widely applicable abstract representations, and present a multi-modal probabilistic language architecture for learning semantic representations for both concrete and abstract concepts. Our model outperforms alternative approaches in combining input from two modalities and also in propagating perceptual information on concrete to more abstract concepts. We discuss the implications of our results both for optimizing the performance of multi-modal models and for theories of abstract conceptual representation.
A distributed cellular approach of large scale SOM models for hardware implementation

Self-organization is a powerful bio-inspired feature that has been poorly investigated in the context of hardware computing architectures. The neural foundations of brain self-organization enable structural plasticity, continuous learning, and tolerance to lesions but at the cost of massive lateral synaptic connections. A cellular formulation of the related neural models would be able to tackle this limitation by iterating the propagation of the information in the network. This paper introduces the SOMA hardware architecture, which relies on this principle to develop a Self-Organizing Machine Architecture. We show the benefits of our cellular implementation compared to the existing neural models. This contribution leads to a hardware compliant algorithm thanks to decentralized operations and communications. A generalization of the technique is proposed to implement different Kohonen-like self-organizing models. This particular cellular implementation reaches the same behavior as the centralized models but drastically reduces their computing complexity. We present the performance results and discuss the benefits of cellular computing approaches in the perspective of designing hardware neural processors for embedded applications.
Image co-segmentation based on image complexity analysis and pixel-correspondences

Image co-segmentation refers to the task of segmenting jointly a set of images sharing objects of a specific class. Recent methods can co-segment simple images well, however their performance may degrade significantly on more cluttered images. In this paper a novel method is presented to jointly segment out common objects from a set of images using image complexity analysis and dense correspondences. The images are first ranked based of their complexity scores. Then a set of simple images is selected and segmented. Finally the segmentation results of simple images are propagated to more cluttered images via multi-scale dense correspondences in order to guide their segmentation. The comparison experiments conducted on iCoseg dataset demonstrate the performance of the proposed method.
Two-Stage Convolutional Neural Network Architecture for Lung Nodule Detection

Early detection of lung cancer is an effective way to improve the survival rate of patients. It is a critical step to have accurate detection of lung nodules in computed tomography (CT) images for the diagnosis of lung cancer. However, due to the heterogeneity of the lung nodules and the complexity of the surrounding environment, robust nodule detection has been a challenging task. In this study, we propose a two-stage convolutional neural network (TSCNN) architecture for lung nodule detection. The CNN architecture in the first stage is based on the improved UNet segmentation network to establish an initial detection of lung nodules. Simultaneously, in order to obtain a high recall rate without introducing excessive false positive nodules, we propose a novel sampling strategy, and use the offline hard mining idea for training and prediction according to the proposed cascaded prediction method. The CNN architecture in the second stage is based on the proposed dual pooling structure, which is built into three 3D CNN classification networks for false positive reduction. Since the network training requires a significant amount of training data, we adopt a data augmentation method based on random mask. Furthermore, we have improved the generalization ability of the false positive reduction model by means of ensemble learning. The proposed method has been experimentally verified on the LUNA dataset. Experimental results show that the proposed TSCNN architecture can obtain competitive detection performance.
A Groebner basis for the secant ideal of the second hypersimplex

We determine a Groebner basis for the secant ideal of the toric ideal associated to the second hypersimplex, with respect to any circular term order. The Groebner basis of the secant ideal requires polynomials of odd degree up to n. This shows that the circular term order is 2-delightful, resolving a conjecture of Drton, Sturmfels, and the author. The proof uses Groebner degenerations for secant ideals, combinatorial characterizations of the secant ideals of monomial ideals, and the relations between secant ideals and prolongations.
Read rate profile monitoring for defect detection in RFID Systems

RFID technologies are sometimes used into critical domains where the on-line detection of RFID system defects is a must. Moreover, as RFID systems are based on low cost tags which are often used into harsh environment, they do not always ensure robust functioning. This article proposes a new on-line monitoring approach allowing the detection of system defects to enhance system reliability and availability. This approach is based on the characterization of a statistical system parameter - the tag read rate profile - to perform the on-line detection of faulty RFID components. This monitoring approach is compared to classical monitoring approaches for a real case study and through several fault simulations. Results show that the proposed read rate profile monitoring is more efficient than the existing approaches. In addition, results show that this approach should be combined with an existing approach to maximize the fault detection.
Aspects of a Functional Grammar for Machine Translation 1

This paper discusses aspects of a functional grammar for Machine Translation (MT). We present a restricted approach to some central issues of "diathesis/ alternations" of predicate-argument structures on a level of "Interface Structure" (IS) for multi-lingual machine translation. The approach generalizes a set of morphosyntactic phenomena into a set of "diathesis" features with specified interactions with other features at IS. The aim of conforming with the overriding goal of simple transfer is discussed. We suggest extensions to the restricted approach to make it more compatible with ongoing work in knowledge-based MT and in text generation. 1 I am grateful to my EUROTRA colleagues Nuria Bel, Mounira Loughraieb, Lee Humphreys, Ursula Reuther, Sibylle Rieder, Anne Becker, and Randy Sharp for critical support in various ways. Recently, I have also profited through critical feedback by John Bateman, Bob Kasper, Leo Wanner, Elke Teich and Elisabeth Maier. I am alone responsible for any remaining weaknesses.
Differential glutamate‐dependent and glutamate‐independent adenosine A1 receptor‐mediated modulation of dopamine release in different striatal compartments

Adenosine and dopamine are two important modulators of glutamatergic neurotransmission in the striatum. However, conflicting reports exist about the role of adenosine and adenosine receptors in the modulation of striatal dopamine release. It has been previously suggested that adenosine A1 receptors localized in glutamatergic nerve terminals indirectly modulate dopamine release, by their ability to modulate glutamate release. In the present study, using in vivo microdialysis, we provide evidence for the existence of a significant glutamate‐independent tonic modulation of dopamine release in most of the analyzed striatal compartments. In the dorsal, but not in the ventral, part of the shell of the nucleus accumbens (NAc), blockade of A1 receptors by local perfusion with the selective A1 receptor antagonist 8‐cyclopentyl‐1,3‐dimethyl‐xanthine or by systemic administration of the non‐selective adenosine antagonist caffeine induced a glutamate‐dependent release of dopamine. On the contrary, A1 receptor blockade induced a glutamate‐independent dopamine release in the core of the NAc and the nucleus caudate–putamen. Furthermore, using immunocytochemical and functional studies in rat striatal synaptosomes, we demonstrate that a fraction of striatal dopaminergic terminals contains adenosine A1 receptors, which directly inhibit dopamine release independently of glutamatergic transmission.
Evolution of function in protein superfamilies, from a structural perspective.

The recent growth in protein databases has revealed the functional diversity of many protein superfamilies. We have assessed the functional variation of homologous enzyme superfamilies containing two or more enzymes, as defined by the CATH protein structure classification, by way of the Enzyme Commission (EC) scheme. Combining sequence and structure information to identify relatives, the majority of superfamilies display variation in enzyme function, with 25 % of superfamilies in the PDB having members of different enzyme types. We determined the extent of functional similarity at different levels of sequence identity for 486,000 homologous pairs (enzyme/enzyme and enzyme/non-enzyme), with structural and sequence relatives included. For single and multi-domain proteins, variation in EC number is rare above 40 % sequence identity, and above 30 %, the first three digits may be predicted with an accuracy of at least 90 %. For more distantly related proteins sharing less than 30 % sequence identity, functional variation is significant, and below this threshold, structural data are essential for understanding the molecular basis of observed functional differences. To explore the mechanisms for generating functional diversity during evolution, we have studied in detail 31 diverse structural enzyme superfamilies for which structural data are available. A large number of variations and peculiarities are observed, at the atomic level through to gross structural rearrangements. Almost all superfamilies exhibit functional diversity generated by local sequence variation and domain shuffling. Commonly, substrate specificity is diverse across a superfamily, whilst the reaction chemistry is maintained. In many superfamilies, the position of catalytic residues may vary despite playing equivalent functional roles in related proteins. The implications of functional diversity within supefamilies for the structural genomics projects are discussed. More detailed information on these superfamilies is available at http://www.biochem. ucl.ac. uk/bsm/FAM-EC/.
The Impact of Sound Systems on the Perception of Cinematic Content in Immersive Audiovisual Productions

With fast technological developments, traditional perceptual environments disappear and new ones emerge. These changes make the human senses adapt to new ways of perceptual understanding, for example, regarding the perceptual integration of sound and vision. Proceeding from the fact that hearing cooperates with visual attention processes, the aim of this study is to investigate the effect of different sound design conditions on the perception of cinematic content in immersive audiovisual reproductions. Here we introduce the results of a visual selective attention task (counting objects) performed by participants watching a 270-degree immersive audiovisual display, on which a movie ("Ego Cure") was shown. Four sound conditions were used, which employed an increasing number of loudspeakers, i.e., mono, stereo, 5.1 and 7.1. 4. Eye tracking was used to record the participant’s eye gaze during the task. The eye tracking data showed that an increased number of speakers and a wider spatial audio distribution diffused the participants’ attention from the task-related part of the display to non-task-related directions. The number of participants looking at the task-irrelevant display in the 7.1. 4 condition was significantly higher than in the mono audio condition. This implies that additional spatial cues in the auditory modality automatically influence human visual attention (involuntary eye movements) and human analysis of visual information. Sound engineers should consider this when mixing educational or any other information-oriented productions.
On a Counting Theorem for Weakly Admissible Lattices

We give a precise estimate for the number of lattice points in certain bounded subsets of $\mathbb{R}^{n}$ that involve “hyperbolic spikes” and occur naturally in multiplicative Diophantine approximation. We use Wilkie’s o-minimal structure $\mathbb{R}_{\exp }$ and expansions thereof to formulate our counting result in a general setting. We give two different applications of our counting result. The 1st one establishes nearly sharp upper bounds for sums of reciprocals of fractional parts and thereby sheds light on a question raised by Lê and Vaaler, extending previous work of Widmer and of the author. The 2nd application establishes new examples of linear subspaces of Khintchine type thereby refining a theorem by Huang and Liu. For the proof of our counting result, we develop a sophisticated partition method that is crucial for further upcoming work on sums of reciprocals of fractional parts over distorted boxes.
Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies

Testing scenario library generation (TSLG) is a critical step for the development and deployment of connected and automated vehicles (CAVs). In Part I of this study, a general method for TSLG is proposed, and theoretical properties are investigated regarding the accuracy and efficiency of CAV evaluation. This paper aims to provide implementation examples and guidelines, and to enhance the proposed methodology under high-dimensional scenarios. Three typical cases, including cut-in, highway-exit, and car-following, are designed and studied in this paper. For each case, the process of library generation and CAV evaluation is elaborated. To address the challenges brought by high dimensionality, the proposed method is further enhanced by reinforcement learning technique. For all three cases, results show that the proposed method can accelerate the CAV evaluation process by multiple magnitudes with same evaluation accuracy, if compared with the on-road test method.
Re-Mind the Gap! Insertion – Deletion Data Reveal Neglected Phylogenetic Potential of the Nuclear Ribosomal Internal Transcribed Spacer (ITS) of Fungi

Rapidly evolving, indel-rich phylogenetic markers play a pivotal role in our understanding of the relationships at multiple levels of the tree of life. There is extensive evidence that indels provide conserved phylogenetic signal, however, the range of phylogenetic depths for which gaps retain tree signal has not been investigated in detail. Here we address this question using the fungal internal transcribed spacer (ITS), which is central in many phylogenetic studies, molecular ecology, detection and identification of pathogenic and non-pathogenic species. ITS is repeatedly criticized for indel-induced alignment problems and the lack of phylogenetic resolution above species level, although these have not been critically investigated. In this study, we examined whether the inclusion of gap characters in the analyses shifts the phylogenetic utility of ITS alignments towards earlier divergences. By re-analyzing 115 published fungal ITS alignments, we found that indels are slightly more conserved than nucleotide substitutions, and when included in phylogenetic analyses, improved the resolution and branch support of phylogenies across an array of taxonomic ranges and extended the resolving power of ITS towards earlier nodes of phylogenetic trees. Our results reconcile previous contradicting evidence for the effects of data exclusion: in the case of more sophisticated indel placement, the exclusion of indel-rich regions from the analyses results in a loss of tree resolution, whereas in the case of simpler alignment methods, the exclusion of gapped sites improves it. Although the empirical datasets do not provide to measure alignment accuracy objectively, our results for the ITS region are consistent with previous simulations studies alignment algorithms. We suggest that sophisticated alignment algorithms and the inclusion of indels make the ITS region and potentially other rapidly evolving indel-rich loci valuable sources of phylogenetic information, which can be exploited at multiple taxonomic levels.
How to interpret algorithmically constructed topical structures of research specialties? A case study comparing an internal and an external mapping of the topical structure of invasion biology

In our paper we seek to address a shortcoming in the scientometric literature, namely that, given the proliferation of algorithmic approaches to topic detection from bibliometric data, there is a relative lack of studies that validate and create a deeper understanding of the topical structures these algorithmic approaches generate. To take a closer look at this issue, we investigate the results of the new Leiden algorithm when applied to the direct citation network of a field-level data set. We compare this internal perspective which is constructed from the citation links within a data set of 30,000 publications in invasion biology, with an external perspective onto the topic structures in this research specialty, which is based on a global science map in form of the CWTS microfield classification underlying the Leiden Ranking. We present an initial comparative analysis of the results and lay out our next steps that will involve engaging with domain experts to examine how the algorithmically identified topics relate to understandings of topics and topical perspectives that operate within this research specialty.
Leader–Follower Bipartite Consensus of Uncertain Linear Multiagent Systems With External Bounded Disturbances Over Signed Directed Graph

In this letter, leader–follower bipartite consensus of linear multiagent systems (MASs) has been studied over a signed directed graph. The agents in the MAS are subjected to system parametric uncertainties and unknown external bounded disturbances. A distributed observer-based relative output feedback control law is designed for each follower to achieve bipartite consensus. It has been shown that if the underlying directed communication graph is structurally balanced and contains a directed spanning tree rooted at the leader, then the appropriate design of scalar coupling gains, and controller feedback gain and observer gain obtained from solving two suitable linear matrix inequalities (LMIs) stabilizes both the bipartite consensus error and observer estimation error to a bound. The simulation results verify the effectiveness of the proposed methods.