Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-Aided Fractional Delay-Doppler Channel Estimation with Embedded Pilot Frames in DZT-Based OTFS

Orthogonal time frequency space (OTFS) implementation using discrete Zak transform (DZT) has performance and complexity advantages in channels with high Doppler spreads. In this paper, we propose a data-aided approach to channel estimation in DZT-based OTFS systems with fractional delays and Dopplers. The proposed method employs an embedded pilot frame (consisting of pilot and data symbols) for channel estimation and leverages detected data to enhance channel estimation accuracy. The algorithm adopts a path-by-path approach where the channel parameters for one path are estimated before proceeding to the next, without assuming the knowledge of the number of paths. Considering the embedded nature of the pilot and data symbols in a frame, the effect of data signal-to-noise ratio (SNR) on the channel estimation performance and the effect of pilot SNR on the data detection performance are evaluated, leading to the data-aided approach to achieve improved performance. Simulation results show that the proposed estimation algorithm achieves significantly better performance compared to threshold based estimation algorithm.
LEMLEM W ORKING P APER S ERIES

In the centennial of the March on Rome, this paper contributes to the political economy of Italian Fascism by addressing in quantitative terms the fortunes of Italian economic elites during the interwar period. Macro-economic indicators indicate capital accumulation and high profits, in a period characterised by international economic turmoil, alongside increasing concentration. New fiscal evidence is adopted to show the increasing relative position of the rich, including new series of top income shares. A discussion of taxation at the top and its evasion makes it possible to place these developments within the regressive economic policies of the Fascist regime.
The effect of trophic level on individual amino acid δ15N values in a terrestrial ruminant food web

ABSTRACT Bulk collagen δ15N analysis is widely used to investigate past diet and trophic positions, but these values average the δ15N values of the constituent amino acids. Compound–specific isotope analysis of amino acids (AAs) can help elucidate the complex metabolic effects underpinning bulk δ15N values. Although trophic level effects on individual AA δ15N values have been investigated in aquatic and terrestrial invertebrate food webs, most archaeological applications involve terrestrial herbivores, hence a greater understanding of these effects between diet and consumer in this food chain is required. The North Wyke Farm Platform provided baseline nitrogen isotope information for cattle grazing on a Lolium perenne-dominated pasture. Bulk dentine δ15N values show a shift expected for a one trophic level increase, but obscure insight into the underlying metabolic processes that cause this change in value. However, determination of AA δ15N values of hydrolysable plant protein and cattle tooth dentine clarifies the trophic effect on consumer AA δ15N values. The observed trophic shift in the studied system is different from previously studied food webs, with a trophic enrichment factor, based on the δ15N values of glutamate and phenylalanine, of 4.0‰ compared to 7.6‰ commonly used in ecological and archaeological studies. This emphasises the need to understand the trophic shifts in the particular food web being investigated in order to apply isotopic investigations in archaeological contexts.
Does Justice Need to be in the Eyes of Both Beholders? Examining Face-to-Face and Virtual Negotiators’ Interactional Justice Congruence

We build from justice and negotiation scholars’ historical interest in interpersonal interactions and reciprocity to develop the notion of interactional justice congruence, which refers to the degree of reciprocity in negotiation dyad members’ respectful and truthful treatment of one another. Yet, media richness theory holds that communication media differ in their provision of social cues and ability to interpret subjective information such as justice perceptions. Integrating social exchange theory tenets with media richness theory, we examine how communication medium influences the effects of negotiators’ interactional justice congruence on their dyadic economic and social-psychological outcomes. Moderated polynomial regression and response surface analyses of data from 199 face-to-face and virtual negotiation dyads revealed that face to-face dyads’ relationship conflict and outcome inequality were minimized when negotiators’ interpersonal and informational justice perceptions were congruent—even if both negotiators perceived one another be disrespectful or deceptive. Virtual negotiation dyads did not experience this benefit. This suggests justice functions differently at the dyadic level in negotiations, such that justice needs to be similarly perceived by both face-to-face
MAD-TI: Meta-Path Aggregated-Graph Attention Network for Drug Target Interaction Prediction

Computational identification of unknown drug-target interactions (DTI) is crucial in locating new drug treat-ments for proteins, viruses, and diseases. This work proposes MAD-TI a meta-path-based, GAT-oriented method to predict DTIs. Our proposed method uses a heterogeneous graph of drugs, targets, diseases, and side effects as the input graph. Then, it applies two graph attention networks to generate the embeddings of drugs and targets. Using the embeddings, it predicts the unknown DTIs. The results show that MAD-TI outperforms the state-of-the-art methods.
Nematode-Inspired Cable Routing Method for Cable Driven Redundant Manipulator*

Cable driven redundant manipulator (CDRM) can provide complex movements with high dexterity and singularity reduction. However, traditional CDRMs with universal joints have the disadvantages of requiring a high number of motors and having a narrow joint workspace. Furthermore, there is a limitation in terms of stiffness and payload. Recently, CDRMs composed of Quaternion joints have been developed to address these disadvantages. They require fewer motors and have larger joint workspace due to the Quaternion joints. Yet, their cable routing method is the same as the traditional CDRMs. In this paper, we propose a novel nematode-inspired cable routing method to achieve complex movements and stiffness increase. To achieve the stiffness increase of CDRM, the proposed cable routing method was inspired by the alternately arranged muscle structure of nematodes. Moreover, moving pulley structure was selected to amplify the stiffness and force of CDRM. An 8-DOF CDRM prototype composed of four Quaternion joints was developed to show the effectiveness of the cable routing method. Kinematics simulation was conducted and then, verified by trajectory through experiments. Finally, a joint stiffness simulation was conducted and verified with the developed prototype by stiffness experiments.
Masked Language Modeling for Resource Constrained Biological Natural Language Processing

Recent advances in Natural Language Processing (NLP) have produced state of the art results on several sequence to sequence (seq2seq) tasks. Enhancements in embedders and their training methodologies have shown significant improvement on downstream tasks. Word vector models like Word2Vec, FastText & Glove were widely used over one-hot encoded vectors for years until the advent of deep contextualized embedders. Protein sequences consist of 20 naturally occurring amino acids that can be treated as the language of nature. These amino acids in combinations with each other makeup the biological functions. The choice of vector representation and architecture design for a biological task is highly dependent upon the nature of the task. We utilize unlabelled protein sequences to train a Convolution and Gated Recurrent Network (CGRN) embedder using Masked Language Modeling (MLM) technique that shows significant performance boost under resource constraint setting on two downstream tasks i.e., F1-score(Q8) of 73.1% on Secondary Structure Prediction (SSP) & F1-score of 84% on Intrinsically Disordered Region Prediction (IDRP). We also compare different architectures on downstream tasks to show the impact of the nature of biological task on the performance of the model.
Classification of patients with cardiac amyloidosis using machine learning models on Italian electronic clinical health records*

Amyloidosis refers to a range of medical conditions in which misshapen proteins accumulate in various organs and tissues, forming insoluble fibrils. Cardiac amyloidosis is frequently linked to the buildup of misfolded transthyretin (TTR) or immunoglobulin light chains (AL). Delayed diagnosis, due to lack of disease awareness, results in a poor prognosis, especially in patients with AL amyloidosis. Early identification is therefore a key factor to improve patient outcomes. This study investigates the use of supervised machine-learning algorithms to support clinicians in classifying amyloidosis and control subjects. The aim of this work is to foster model interpretability reporting the most important risk factors in predicting the presence of cardiac amyloidosis. We analyzed electronic health records (EHRs) of 418 participants acquired in a time window of 12 years as part of a case-control study conducted in Fondazione Toscana Gabriele Monasterio (Italy) clinical practice. This work paves the way for the creation of digital health solutions that can aid in amyloidosis screening. The effective handling, analysis, and interpretation of these solutions can have a transformative effect on modern healthcare, offering new opportunities for improved patient care.
Design of an experimental setup for delivering intracortical microstimulation in vivo via Spiking Neural Network

Electroceutical approaches for the treatment of neurological disorders, such as stroke, can take advantage of neuromorphic engineering, to develop devices able to achieve a seamless interaction with the neural system. This paper illustrates the development and test of a hardware-based Spiking Neural Network (SNN) to deliver neural-like stimulation patterns in an open-loop fashion. Neurons in the SNN have been designed by following the Hodgkin-Huxley formalism, with parameters taken from neuroscientific literature. We then built the set-up to deliver the SNN-driven stimulation in vivo. We used deeply anesthetized healthy rats to test the potential effect of the SNN-driven stimulation. We analyzed the neuronal firing activity pre- and post-stimulation in both the primary somatosensory and the rostral forelimb area. Our results showed that the SNN-based neurostimulation was able increase the spontaneous level of neuronal firing at both monitored locations, as found in the literature only for closed-loop stimulation. This study represents the first step towards translating the use of neuromorphic-based devices into clinical applications.Clinical Relevance— Stroke represents one of the leading causes of long-term disability and death worldwide. Intracortical microstimulation is an effective approach for restoring lost sensory motor integration by promoting plasticity among the affected brain areas. Stimulation delivered via neuromorphic-based open-loop systems (i.e. neuromorphic prostheses) can pave the way to novel electroceutical strategies for brain repair.
Christian parent controlled schools in Australia - A study of the relationship between functional values and prevailing practices

Christian Parent Controlled (CPC) Schools, which commenced in the 1960s, are a relatively small, but growing component of the non-government schooling sector in Australia. In 2001, they enrolled over 22 000 students in 85 schools. Very little research has been conducted on the values and practices of CPC schools and while these schools frequently assert that they promote explicitly Christian values, their foundational values have not previously been identified or recorded. This research identifies the key foundational values which are characteristic of these schools and examines the extent to which these values continue to influence the prevailing practices of these schools. Consideration is given to the implications of the relationship between foundational values and prevailing practices for the identity, development and leadership of CPC schools in Australia. The ability of these schools to articulate their foundational values and consider their prevailing practices in the light of these values should enhance their ability to understand their heritage, assess their current situation and plan their future. The research found that in general, prevailing practices in these schools give faithful expression to the foundational values; however, the research also identified a number of areas where CPC schools struggle to engage consistently with these values. As a result of this study, recommendations are proposed to assist national and school-based leadership in their strategic planning for the maintenance of these values and the future of these schools.
Pseudo-Stereo++: Cycled Generative Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving

Recently, the feature-level generation has demonstrated the effectiveness of pseudo-stereo synthesis in Monocular 3D Detection (M3D). In this paper, we aim to further bridge the gap between the stereo and the monocular 3D object detectors in autonomous driving through direct image-level pseudo-stereo generation. We propose a novel Cycled Generative Pseudo-Stereo (CGPS) architecture to generate the right-view image from the left-view for constructing a pseudo-stereo pair to stereo 3D object detectors while maintaining the natural of M3D with the left-view image as the only input. Moreover, we use a triplet consistency loss to focus on the detected objects in the pseudo-stereo generation. Besides, we demonstrate that the proposed CGPS is an ad-hoc module to adapt top stereo 3D object detectors into monocular 3D object detectors. The proposed framework with CGPS achieves 74.80%, 55.28%, and 46.70% 3DAP for easy, moderate, and hard difficulty levels in monocular 3D detection on the KITTI benchmark with comparable performance to the stereo 3D object detectors but using a monocular image as the only input. Till the submission, the proposed M3D framework ranks 1st with dramatic improvements against the existing monocular 3D detectors on the KITTI benchmark.
Experimental Evaluation of a Transparent Operation Mode for a Lower-Limb Exoskeleton Designed for Children with Cerebral Palsy

Robot-assisted rehabilitation is expected to reduce locomotor limitations of children and young adults with Cerebral Palsy (CP). However, to achieve this result, it is essential that the robot is transparent, allowing the user to move freely, and generate joint torques only when the exoskeleton joints significantly deviate from physiological gait patterns. Nevertheless, the development of transparent operation in lower-limb exoskeletons is still an open problem with several implementation challenges. In this study, we implemented a transparent operation strategy on the ExoRoboWalker, an overground exoskeleton designed for children and young adults with CP. The approach employs a feedback zero-torque controller with feedforward compensations for the exoskeleton's dynamics and actuators' impedance. We experimented the proposed system in five healthy subjects walking overground with the exoskeleton in transparent mode (ExoTransp) and non-transparent mode (ExoOff). The proposed transparent controller reduced the user-robot interaction torque and improved user gait kinematics relative to ExoOff. This is a significant step toward an overground gait training exoskeleton for CP population.
Dynamic Functional Connectivity Analysis Using Network-Based Brain State Identification, Application on Temporal Lobe Epilepsy

Epilepsy is a brain network disorder caused by discharges of interconnected groups of neurons and resulting brain dysfunction. The brain network can be characterized by intra- and inter-regional functional connectivity (FC). However, since the BOLD signal is inherently non-stationary, the FC is evidenced to be varying over time. Considering the dynamic characteristics of the functional network, we aimed to obtain dynamic brain states and their properties using network-based analyses for the comparison of healthy control and temporal lobe epilepsy (TLE) groups and also lateralization of TLE patients. We used dwelling time, transition time, and brain network connection in each state as the dynamic features for this purpose. Results showed a significant difference in dwelling time and transition time between the healthy control group and both left TLE and right TLE groups and also a significant difference in brain network connections between the left and right TLE groups.
Anticipatory distractor suppression elicited by statistical regularities in visual search

Salient yet irrelevant objects often capture our attention and interfere with our daily tasks. Distraction by salient objects can be reduced by suppressing the location where they are likely to appear. The question we addressed here was whether suppression of frequent distractors is already implemented beforehand, in anticipation of the stimulus. Using electroencephalography (EEG), we recorded cortical activity of human participants searching for a target while ignoring a salient distractor. The distractor was presented more often at one location than at any other location. We found reduced capture for distractors at frequent locations, indicating that participants learned to avoid distraction. Critically, we found evidence for proactive suppression as already prior to display onset, there was enhanced power in parieto-occipital alpha oscillations contralateral to the frequent distractor location – a signal known to occur in anticipation of irrelevant information. Locked to display onset, event-related potentials analysis showed a distractor-suppression-related PD component for this location. Importantly, this PD was found regardless of whether distracting information was presented at the frequent location. In addition, there was an early PD component representing an early attentional index of the frequent distractor location. Our results are show anticipatory (proactive) suppression of frequent distractor locations in visual search already starting prior to display onset.
An Attention-based Bidirectional LSTM Model for Continuous Cross-Subject Estimation of Knee Joint Angle during Running from sEMG Signals

Accurate and robust estimation of joint kinematics via surface electromyogram (sEMG) signals provides a human-machine interaction (HMI)-based method that can be used to adequately control rehabilitation robots while performing complex movements, such as running, for motor function restoration in affected individuals. To this end, this paper proposes a deep learning-based model (AM-BiLSTM) that integrates a bidirectional long short-term memory (BiLSTM) network and an attention mechanism (AM) for robust estimation of joint kinematics. The proposed model was appraised using knee joint kinematic and sEMG signals collected from fourteen subjects who performed running at the speed of 2 m/s. The proposed model's generalizability was tested for both within- and cross-subject scenarios and compared with long short-term memory (LSTM) and multi-layer perceptron (MLP) networks in terms of normalized root-mean-square error and correlation coefficient metrics. Based on the statistical tests, the proposed AM-BiLSTM model significantly outperformed the LSTM and MLP methods in both within- and cross-subject scenarios (p<0.05) and achieved state-of-the-art performance.Clinical Relevance— The promising results of this study suggest that the AM-BiLSTM model has the potential for continuous cross-subject estimation of lower limb kinematics during running, which can be used to control sEMG-driven exoskeleton robots oriented towards rehabilitation training.
Variabilidad genética en coipos, Myocastor coypus, y su relación con la presión de caza

Genetic variability in coypus, Myocastor coypus, and its relation with hunting pressure. Genetic variability of coypu in sites that support different hunting pressures was analysed through PCR amplification of RAPD markers. DNA was extracted from tissue samples obtained in five freshwater sites located in Lujan zone, north-east of Buenos Aires province, Argentina, between October 1999 and September 2000. Fifty-one RAPD markers were obtained. Forty-nine of these were polymorphic between individuals. The analysis of molecular variance (AMOVA) showed a high genetic variability between sites, which would indicate that individuals from these sites are genetically differentiated. Genetic diversity within each site was negatively correlated with hunting pressure, while a positive correlation between genetic diversity and population size was observed. Genetic distance between immigrants, residents and immigrants-residents were significantly different, showing that immigrant individuals are more related between them than with the rest. It is proposed that in the Pampas Region, where the landscape is fragmented into parcels having agricultural-cattle use, a source-sink dynamic between parcels protected and not protected from hunting pressure is established for the coypu where protected parcels would act as a source of individuals for the not protected ones
LEMLEM W ORKING P APER S ERIES A non-Normal framework for price discovery: The independent component based information shares measure

I propose a new measure of price discovery, which I will refer to as the Independent Component based Information Share (IC-IS). This measure constitutes a variant of the widespread Information Share, with the main difference being it does not suffer the same identification issues. Under the assumptions of non-normality and independence of the shocks, a rather general theoretical framework leading to the estimation of the IC-IS is illustrated. After testing the robustness of the proposed measures to different non-Normal distributions in a simulated environment, an empirical exercise encompassing different price discovery applications will follow.
Evaluation of the effects of glucose and oxygen on the vascular endothelial cell migration

Glucose is essential as the main energy source for living organisms. However, excessive elevation of blood sugar levels can lead to diabetes and serious complications such as arteriosclerosis. Even though blood sugar levels as well as hypoxia associated with hyperglycemia are known to be closely related to diabetes complications, the responses of vascular endothelial cells to glucose and oxygen have not been fully investigated. In this study, using a microfluidic device that can control the oxygen concentration, we observed the behavior of vascular endothelial cell monolayers while simultaneously controlling glucose and oxygen levels. Results showed that the cell migration speed was increased by high-glucose exposure in an oxygen-rich environment, but was decreased in a hypoxic environment regardless of glucose condition. The expression of vascular endothelial-cadherin at the cell periphery, which plays a role in cell-cell adhesion, was increased by hypoxic exposure, but was largely independent of glucose condition. This suggested that cell-cell adhesion is less involved in the increase in migration caused by high glucose. Furthermore, stabilization and nuclear translocation of hypoxia-inducible factor-1α, which is involved in cellular hypoxia sensing, increased 5 h after exposure to high glucose, but decreased 3 days after the exposure. This indicated that intracellular hypoxia was generated by increased oxygen consumption in mitochondria just after the high-glucose exposure, but it was moderated within 3 days.