Miracle-dz/UniFAR-FTUs huggingface dataset:
Line Spectrum Estimation and Detection with Few-bit ADCs: Theoretical Analysis and Generalized NOMP Algorithm

As radar systems will be equipped with thousands of antenna elements and wide bandwidth, the associated costs and power consumption become exceedingly high, primarily attributed to the high-precision (e.g., 10-12 bits) analog-to-digital converters (ADCs). To address this challenge, a potential solution is to adopt low-resolution quantization technology, which not only reduces data storage needs but also lowers power and hardware costs. In this context, the focus is on studying line spectral estimation and detection (LSE\&D) with few-bit ADCs, typically using 1-4 bits. This paper investigates the signal-to-noise ratio (SNR) loss, establishing a framework to understand the impact of intersinusoidal interference, the bit-depth of the quantizer, and the noise variance on weak signal detection in scenarios involving multiple sinusoids under low-resolution quantization. Additionally, a low-complexity, super-resolution, and constant false alarm rate (CFAR) algorithm, named generalized Newtonized orthogonal matching pursuit (GNOMP), is proposed. Extensive numerical simulations are conducted to validate the theoretical findings, particularly in terms of the detection probability bound. The effectiveness of GNOMP is demonstrated through comparisons with state-of-the-art algorithms, the Cram\'{e}r Rao bound, and the detection probability bound. Real data acquired by mmWave radar further substantiates the effectiveness of GNOMP in practical applications.
Transport, Variational Inference and Diffusions: with Applications to Annealed Flows and Schrödinger Bridges

This paper explores the connections between optimal transport and variational inference, with a focus on forward and reverse time stochastic differential equations and Girsanov transformations.We present a principled and systematic framework for sampling and generative modelling centred around divergences on path space. Our work culminates in the development of a novel score-based annealed flow technique (with connections to Jarzynski and Crooks identities from statistical physics) and a regularised iterative proportional fitting (IPF)-type objective, departing from the sequential nature of standard IPF. Through a series of generative modelling examples and a double-well-based rare event task, we showcase the potential of the proposed methods.
Advantages of Multimodal versus Verbal-Only Robot-to-Human Communication with an Anthropomorphic Robotic Mock Driver

Robots are increasingly used in shared environments with humans, making effective communication a necessity for successful human-robot interaction. In our work, we study a crucial component: active communication of robot intent. Here, we present an anthropomorphic solution where a humanoid robot communicates the intent of its host robot acting as an “Anthropomorphic Robotic Mock Driver” (ARMoD). We evaluate this approach in two experiments in which participants work alongside a mobile robot on various tasks, while the ARMoD communicates a need for human attention, when required, or gives instructions to collaborate on a joint task. The experiments feature two interaction styles of the ARMoD: a verbal-only mode using only speech and a multimodal mode, additionally including robotic gaze and pointing gestures to support communication and register intent in space. Our results show that the multimodal interaction style, including head movements and eye gaze as well as pointing gestures, leads to more natural fixation behavior. Participants naturally identified and fixated longer on the areas relevant for intent communication, and reacted faster to instructions in collaborative tasks. Our research further indicates that the ARMoD intent communication improves engagement and social interaction with mobile robots in workplace settings.
Influence maximization on temporal networks: a review

Influence maximization (IM) is an important topic in network science where a small seed set is chosen to maximize the spread of influence on a network. Recently, this problem has attracted attention on temporal networks where the network structure changes with time. IM on such dynamically varying networks is the topic of this review. We first categorize methods into two main paradigms: single and multiple seeding. In single seeding, nodes activate at the beginning of the diffusion process, and most methods either efficiently estimate the influence spread and select nodes with a greedy algorithm, or use a node-ranking heuristic. Nodes activate at different time points in the multiple seeding problem, via either sequential seeding, maintenance seeding or node probing paradigms. Throughout this review, we give special attention to deploying these algorithms in practice while also discussing existing solutions for real-world applications. We conclude by sharing important future research directions and challenges.
On a Relation Between the Rate-Distortion Function and Optimal Transport

We discuss a relationship between rate-distortion and optimal transport (OT) theory, even though they seem to be unrelated at first glance. In particular, we show that a function defined via an extremal entropic OT distance is equivalent to the rate-distortion function. We numerically verify this result as well as previous results that connect the Monge and Kantorovich problems to optimal scalar quantization. Thus, we unify solving scalar quantization and rate-distortion functions in an alternative fashion by using their respective optimal transport solvers.
Don't Stop Self-Supervision: Accent Adaptation of Speech Representations via Residual Adapters

Speech representations learned in a self-supervised fashion from massive unlabeled speech corpora have been adapted successfully toward several downstream tasks. However, such representations may be skewed toward canonical data characteristics of such corpora and perform poorly on atypical, non-native accented speaker populations. With the state-of-the-art HuBERT model as a baseline, we propose and investigate self-supervised adaptation of speech representations to such populations in a parameter-efficient way via training accent-specific residual adapters. We experiment with 4 accents and choose automatic speech recognition (ASR) as the downstream task of interest. We obtain strong word error rate reductions (WERR) over HuBERT-large for all 4 accents, with a mean WERR of 22.7% with accent-specific adapters and a mean WERR of 25.1% if the entire encoder is accent-adapted. While our experiments utilize HuBERT and ASR as the downstream task, our proposed approach is both model and task-agnostic.
ChatGPT vs. Google: A Comparative Study of Search Performance and User Experience

The advent of ChatGPT, a large language model-powered chatbot, has prompted questions about its potential implications for traditional search engines. In this study, we investigate the differences in user behavior when employing search engines and chatbot tools for information-seeking tasks. We carry out a randomized online experiment, dividing participants into two groups: one using a ChatGPT-like tool and the other using a Google Search-like tool. Our findings reveal that the ChatGPT group consistently spends less time on all tasks, with no significant difference in overall task performance between the groups. Notably, ChatGPT levels user search performance across different education levels and excels in answering straightforward questions and providing general solutions but falls short in fact-checking tasks. Users perceive ChatGPT's responses as having higher information quality compared to Google Search, despite displaying a similar level of trust in both tools. Furthermore, participants using ChatGPT report significantly better user experiences in terms of usefulness, enjoyment, and satisfaction, while perceived ease of use remains comparable between the two tools. However, ChatGPT may also lead to overreliance and generate or replicate misinformation, yielding inconsistent results. Our study offers valuable insights for search engine management and highlights opportunities for integrating chatbot technologies into search engine designs.
Non-Uniqueness and Inadmissibility of the Vanishing Viscosity Limit of the Passive Scalar Transport Equation

We study the vanishing viscosity/diffusivity limit for the transport equation of a passive scalar $f(x,t)\in\mathbb{R}$ along a divergence-free vector field $u(x,t)\in\mathbb{R}^2$, given by $\frac{\partial f}{\partial t} + \nabla\cdot (u f) = 0$; and the associated advection-diffusion equation of $f$ along $u$ for positive viscosity/diffusivity parameter $\nu>0$, expressed by $\frac{\partial f}{\partial t} + \nabla\cdot (u f) -\nu\Delta f = 0$. We demonstrate failure of the vanishing viscosity limit of the advection-diffusion equation to select unique solutions, or to select entropy-admissible solutions, to transport along $u$. First, we construct a bounded divergence-free vector field $u$ which admits, for each (non-constant) initial datum, two weak solutions to the initial value problem for the transport equation. Moreover, we show that both these solutions are renormalised weak solutions, and are strong limits along different subsequences of vanishing viscosity of solutions to the corresponding advection-diffusion equation. Second, we construct a second bounded divergence-free vector field $u$ admitting, for any initial datum, a weak solution to the transport equation which is perfectly mixed to its spatial average, and after some delay in time, it unmixes to its initial state. Moreover, we show that this entropy-inadmissible unmixing is the unique weak vanishing viscosity limit of the corresponding advection-diffusion equation.
Scenario-Based Motion Planning with Bounded Probability of Collision

Robots will increasingly operate near humans that introduce uncertainties in the motion planning problem due to their complex nature. Typically, chance constraints are introduced in the planner to optimize performance while guaranteeing probabilistic safety. However, existing methods do not consider the actual probability of collision for the planned trajectory, but rather its marginalization, that is, the independent collision probabilities for each planning step and/or dynamic obstacle, resulting in conservative trajectories. To address this issue, we introduce a novel real-time capable method termed Safe Horizon MPC, that explicitly constrains the joint probability of collision with all obstacles over the duration of the motion plan. This is achieved by reformulating the chance-constrained planning problem using scenario optimization and predictive control. Our method is less conservative than state-of-the-art approaches, applicable to arbitrary probability distributions of the obstacles' trajectories, computationally tractable and scalable. We demonstrate our proposed approach using a mobile robot and an autonomous vehicle in an environment shared with humans.
Optimisation of a propagation-based x-ray phase-contrast micro-CT system

Micro-CT scanners find applications in many areas ranging from biomedical research to material sciences. In order to provide spatial resolution on a micron scale, these scanners are usually equipped with micro-focus, low-power x-ray sources and hence require long scanning times to produce high resolution 3D images of the object with acceptable contrast-to-noise. Propagation-based phase-contrast tomography (PB-PCT) has the potential to significantly improve the contrast-to-noise ratio (CNR) or, alternatively, reduce the image acquisition time while preserving the CNR and the spatial resolution. We propose a general approach for the optimisation of the PB-PCT imaging system. When applied to an imaging system with fixed parameters of the source and detector this approach requires optimisation of only two independent geometrical parameters of the imaging system, i.e. the source-to-object distance R1 and geometrical magnification M, in order to produce the best spatial resolution and CNR. If, in addition to R1 and M, the system parameter space also includes the source size and the anode potential this approach allows one to find a unique configuration of the imaging system that produces the required spatial resolution and the best CNR.
BatGPT: A Bidirectional Autoregessive Talker from Generative Pre-trained Transformer

BatGPT is a large-scale language model designed and trained jointly by Wuhan University and Shanghai Jiao Tong University. It is capable of generating highly natural and fluent text in response to various types of input, including text prompts, images, and audio. In the modeling level, we employ a bidirectional autoregressive architecture that allows the model to efficiently capture the complex dependencies of natural language, making it highly effective in tasks such as language generation, dialog systems, and question answering. Moreover, the bidirectional autoregressive modeling not only operates from left to right but also from right to left, effectively reducing fixed memory effects and alleviating model hallucinations. In the training aspect, we propose a novel parameter expansion method for leveraging the pre-training of smaller models and employ reinforcement learning from both AI and human feedback, aimed at improving the model's alignment performance. Overall, these approaches significantly improve the effectiveness of BatGPT, and the model can be utilized for a wide range of natural language applications.
Fixing confirmation bias in feature attribution methods via semantic match

Feature attribution methods have become a staple method to disentangle the complex behavior of black box models. Despite their success, some scholars have argued that such methods suffer from a serious flaw: they do not allow a reliable interpretation in terms of human concepts. Simply put, visualizing an array of feature contributions is not enough for humans to conclude something about a model's internal representations, and confirmation bias can trick users into false beliefs about model behavior. We argue that a structured approach is required to test whether our hypotheses on the model are confirmed by the feature attributions. This is what we call the"semantic match"between human concepts and (sub-symbolic) explanations. Building on the conceptual framework put forward in Cin\`a et al. [2023], we propose a structured approach to evaluate semantic match in practice. We showcase the procedure in a suite of experiments spanning tabular and image data, and show how the assessment of semantic match can give insight into both desirable (e.g., focusing on an object relevant for prediction) and undesirable model behaviors (e.g., focusing on a spurious correlation). We couple our experimental results with an analysis on the metrics to measure semantic match, and argue that this approach constitutes the first step towards resolving the issue of confirmation bias in XAI.
The influence of different offset modes on the drainage characteristics of a double stack drainage system in a high-rise building

To meet the functional requirements of high-rise buildings, drainage stacks usually employ an offset pipe in the structural transfer floor. At the offset point, the flow direction changes and the air pressure fluctuates, which significantly influences the drainage system capacity and water seal effectiveness. Different offset modes have different degrees of influence on drainage characteristics. To better understand the influence of different offset modes on drainage characteristics, five different test conditions in double stack building drainage systems were established and investigated. DS1 was a standard double-stack drainage system with anti-reflux H-tube joints for every two floor levels. The remaining four systems were developed from DS1 by adopting various offset pipes (or pipe joints) on the fourth floor. DS2 adopted an S-shape offset pipe joint at the offset point. DS3 adopted double S-shape offset pipe joints. DS4 adopted a 1.0-m-long horizontal offset pipe. DS5 adopted a 1.5-m-long horizontal offset pipe. The pressure fluctuations and water seal losses were chosen as the experimental parameters to determine the maximum discharge capacity according to the Standard for Capacity Test of Vertical Pipe of the Domestic Residential Drainage System CJJ/T 245. The experimental results demonstrated that the installation of offset pipes (or pipe joints) contributed to the abrupt positive pressure and severe water seal losses at the position where flow direction shifted. As the length of the offset pipe increased, the positive pressure was more severe, and maximum discharge capacity was minor. The relationships among the maximum discharge rate, pressure fluctuation limits, and water seal losses are discussed. Lastly, a nondimensional analysis was adopted to understand the relationship between water seal losses and pressure limit values under different discharge rates for current test facilities. Practical application: Pipe offset has the potential to influence a drainage system discharge capacity. As the length of the horizontal offset pipe increases, the discharge capacity will decrease. In the actual design, the length of the offset pipe section should be reduced and this study demonstrates that the application of S-shape offset pipe joints is an efficient solution.
Comparison of step‐by‐step kinematics and muscle activation of resisted, assisted, and unloaded 30‐m sprints in sprinters

The aim of this study was to compare step‐by‐step kinematics and muscle activation during 30‐m sprint efforts under resisted, normal, and assisted conditions in experienced sprinters. Sixteen subjects performed resisted, normal, and assisted 30‐m sprint in one session. Almost all measured kinematics were affected by resisted and assisted sprint conditions. The exception was that step frequency did not show any marked differences between the assisted sprint condition and normal sprints. Maximal angular velocity of hip flexion and knee extension increased the first four strides during all conditions, and muscle activation of the hamstrings muscles similarly increased with each of the first four strides. The only difference between conditions was that maximal knee extension velocity was reduced during the resisted condition. Step‐by‐step changes in muscle activation during the sprints also did not differ between conditions. It was concluded that the subjects were simply not able to increase limb velocity to the degree required to achieve greater step frequency under the assisted sprint condition as shown by similar maximal angular hip flexion, knee extension, and muscle activations. Furthermore, that the hamstrings muscles during acceleration are getting more important for propulsion than the other muscles, which are not changing much in activation.
Performance Optimization of Reconfigurable Real-Time Wireless Sensor Networks

Wireless sensor networks (WSNs) can be seriously impacted by several changes in behavior. As a measure of optimality, a network should react in real-time. However, even if some reactions can make the system flexible, they can cause significant damages when they are not well-structured. Moreover, the scenarios of reconfigurations can affect several parameters within a wireless sensor network. Among these parameters it is possible to name the energy efficiency, the memory limitation within a node, the real-time constraints at the level of the nodes and the network in general. Either through the application or the transmission of the reconfiguration scenarios, some parameters are severely altered. A metamorphosis of the internal architecture of the nodes is proposed in this research work as well as a policy of communication between several nodes aiming to adapt the network to any change. The resulting proposition offers general efficiency in energy and real-time constraints. The efficiency is realized by the application of a pipelined approach, dealing with the incoming reconfiguration scenarios, and a communication protocol, based on a priority energy and deadline aware scheduling algorithm (a multicriteria scheduling algorithm). This protocol is capable of optimizing the transmission of both reconfiguration scenarios and sensed data simultaneously. The solution is tested on a network connecting several cars during an automobile journey and the results validate the efficiency of this proposition.
Identifying climate models based on their daily output using machine learning

Abstract Climate models are primary tools for investigating processes in the climate system, projecting future changes, and informing decision makers. The latest generation of models provides increasingly complex and realistic representations of the real climate system, while there is also growing awareness that not all models produce equally plausible or independent simulations. Therefore, many recent studies have investigated how models differ from observed climate and how model dependence affects model output similarity, typically drawing on climatological averages over several decades. Here, we show that temperature maps of individual days drawn from datasets never used in training can be robustly identified as “model” or “observation” using the CMIP6 model archive and four observational products. An important exception is a prototype storm-resolving simulation from ICON-Sapphire which cannot be unambiguously assigned to either category. These results highlight that persistent differences between simulated and observed climate emerge at short timescales already, but very high-resolution modeling efforts may be able to overcome some of these shortcomings. Moreover, temporally out-of-sample test days can be assigned their dataset name with up to 83% accuracy. Misclassifications occur mostly between models developed at the same institution, suggesting that effects of shared code, previously documented only for climatological timescales, already emerge at the level of individual days. Our results thus demonstrate that the use of machine learning classifiers, once trained, can overcome the need for several decades of data to evaluate a given model. This opens up new avenues to test model performance and independence on much shorter timescales.
Evaluation of oxidative stress biomarkers together with myeloperoxidase/paraoxonase-1 and myeloperoxidase/high density lipoprotein cholesterol in ST-elevation myocardial infarction

Abstract Objectives Oxidative stress is closely associated with atherosclerosis and acute coronary syndromes. The purpose of this study was to evaluate well-known and proportional oxidative stress biomarkers in ST-Elevation Myocardial Infarction (STEMI) patients. Methods In this single center, prospective and cross-sectional study, 107 individuals (63 patients) were studied. Total oxidative status (TOS), total antioxidant status (TAS), oxidative stress index (OSI), ischemia modified albumin (IMA), myeloperoxidase (MPO), paraoxonase-1 (PON-1) and arylesterase (AREase) enzyme activities as well as MPO/PON-1, MPO/AREase and MPO/HDL-C ratios were studied. As short-term in-hospital prognosis biomarkers; in-hospital mortality, early systolic dysfunction and spontaneous complete revascularization were investigated. Results Our results indicated that TOS, OSI, IMA, MPO, MPO/PON-1 and MPO/HDL ratios were significantly higher, PON-1 and AREase were significantly lower in STEMI patients compared to the control group. However, in the regression analysis performed by adjusting the differences between the groups, only IMA was found as an independent risk factor (OR=2.711, 95 % CI=1.094–6.719, p=0.031). In terms of in-hospital short-term prognostic biomarkers, a significant relationship was found only between OSI and spontaneous complete revascularization. The OSI value was higher in the group with TIMI grade 3 flow than in the group with TIMI grade 0–2 flow (2.42 [0.81–4.49] vs. 1.63 [0.33–6.07], p=0.016). Conclusions In STEMI patients, both the well-known (TOS, OSI, and MPO) and proportional (MPO/PON-1 and MPO/HDL cholesterol ratios) oxidative stress markers were elevated and can be considered as having a role in the pathogenesis of STEMI.