A novel distributed control law for consensus of networked double integrator systems with biased measurements is developed in this article. The agents measure relative positions over a time-varying, undirected graph with an unknown and constant sensor bias corrupting the measurements. An adaptive control law is derived using Lyapunov methods to estimate the individual sensor biases accurately. The proposed algorithm ensures that position consensus is achieved exponentially in addition to bias estimation. The results leverage recent advances in collective initial excitation based results in adaptive estimation. Conditions connecting bipartite graphs and collective initial excitation are also developed. The algorithms are illustrated via simulation studies on a network of double integrators with local communication and biased measurements.
Starting from the global financial crisis to the more recent disruptions brought about by geopolitical tensions and public health crises, the volatility of risk in financial markets has increased significantly. This underscores the necessity for comprehensive risk measures capable of capturing the complexity and heightened fluctuations in market volatility. This need is crucial not only for new financial assets but also for the traditional financial market in the face of a rapidly changing financial environment and global landscape. In this paper, we consider the risk measures on a special space $L^{p(\cdot)}$, where the variable exponent $p(\cdot)$ is no longer a given real number as in the conventional risk measure space $L^{p}$, but rather a random variable reflecting potential fluctuations in volatility within financial markets. Through further development of axioms related to this class of risk measures, we also establish dual representations for them.
Recurrent models have been dominating the field of neural machine translation (NMT) for the past few years. Transformers \citep{vaswani2017attention}, have radically changed it by proposing a novel architecture that relies on a feed-forward backbone and self-attention mechanism. Although Transformers are powerful, they could fail to properly encode sequential/positional information due to their non-recurrent nature. To solve this problem, position embeddings are defined exclusively for each time step to enrich word information. However, such embeddings are fixed after training regardless of the task and the word ordering system of the source or target language. In this paper, we propose a novel architecture with new position embeddings depending on the input text to address this shortcoming by taking the order of target words into consideration. Instead of using predefined position embeddings, our solution generates new embeddings to refine each word's position information. Since we do not dictate the position of source tokens and learn them in an end-to-end fashion, we refer to our method as dynamic position encoding (DPE). We evaluated the impact of our model on multiple datasets to translate from English into German, French, and Italian and observed meaningful improvements in comparison to the original Transformer.
Reconstructions of Lagrangian drift, for example for objects lost at sea, are often uncertain due to unresolved physical phenomena within the data. Uncertainty is usually overcome by introducing stochasticity into the drift, but this approach requires specific assumptions for modelling uncertainty. We remove this constraint by presenting a purely data-driven framework for modelling probabilistic drift in flexible environments. Using ocean circulation model simulations, we generate probabilistic trajectories of object location by simulating uncertainty in the initial object position. We train an emulator of probabilistic drift over one day given perfectly known velocities and observe good agreement with numerical simulations. Several loss functions are tested. Then, we strain our framework by training models where the input information is imperfect. On these harder scenarios, we observe reasonable predictions although the effects of data drift become noticeable when evaluating the models against unseen flow scenarios.
The key to successful grounding for video surveillance is to understand a semantic phrase corresponding to important actors and objects. Conventional methods ignore comprehensive contexts for the phrase or require heavy computation for multiple phrases. To understand comprehensive contexts with only one semantic phrase, we propose Position-aware Location Regression Network (PLRN) which exploits position-aware features of a query and a video. Specifically, PLRN first encodes both the video and query using positional information of words and video segments. Then, a semantic phrase feature is extracted from an encoded query with attention. The semantic phrase feature and encoded video are merged and made into a context-aware feature by reflecting local and global contexts. Finally, PLRN predicts start, end, center, and width values of a grounding boundary. Our experiments show that PLRN achieves competitive performance over existing methods with less computation time and memory.
The human nervous system utilizes synaptic plasticity to solve optimization problems. Previous studies have tried to add the plasticity factor to the training process of artificial neural networks, but most of those models require complex external control over the network or complex novel rules. In this manuscript, a novel nature-inspired optimization algorithm is introduced that imitates biological neural plasticity. Furthermore, the model is tested on three datasets and the results are compared with gradient descent optimization.
Accurate and ubiquitous localization is crucial for a variety of applications such as logistics, navigation, intelligent transport, monitoring, control, and also for the benefit of communications. Exploiting millimeter-wave (mmWave) signals in 5G and Beyond 5G systems can provide accurate localization with limited infrastructure. We consider the single base station (BS) localization problem and extend it to 3D position and 3D orientation estimation of an unsynchronized multi-antenna user equipment (UE), using downlink multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) signals. Through a Fisher information analysis, we show that the problem is often identifiable, provided that there is at least one multipath component in addition to the line-of-sight (LoS), even if the position of corresponding incidence point (IP) is a priori unknown. Subsequently, we pose a maximum likelihood (ML) estimation problem, to jointly estimate the 3D position and 3D orientation of the UE as well as several nuisance parameters (the UE clock offset and the positions of IPs corresponding to the multipath). The ML problem is a high-dimensional non-convex optimization problem over a product of Euclidean and non-Euclidean manifolds. To avoid complex exhaustive search procedures, we propose a geometric initial estimate of all parameters, which reduces the problem to a 1-dimensional search over a finite interval. Numerical results show the efficiency of the proposed ad-hoc estimation, whose gap to the Cram\'er-Rao bound (CRB) is tightened using the ML estimation.
In social choice theory, Sen's value restriction condition is a sufficiency condition restricted to individuals' ordinal preferences so as to obtain a transitive social preference under the majority decision rule. In this article, Sen's transitivity condition is described by use of inequality and equation. First, for a triple of alternatives, an individual's preference is represented by a preference map, whose entries are sets containing the ranking position or positions derived from the individual's preference over that triple of those alternatives. Second, by using the union operation of sets and the cardinality concept, Sen's transitivity condition is described by inequalities. Finally, by using the membership function of sets, Sen's transitivity condition is further described by equations.
Concurrent TMS-fMRI involves administrating TMS while subjects are inside an MRI scanner and allows the study of the effects of neurostimulation on simultaneous brain activity. Despite its high promise, the technique has proven challenging to implement for at least three reasons. First, it is difficult to position and stabilize the TMS coil inside the MRI scanner in a way that precisely targets a pre-specified brain region. Second, standard task-presentation software suffers from imprecise timing, which can lead to TMS causing large image artifacts. Third, it is difficult to verify the exact TMS coil position during scanning. In this paper, we describe solutions to all three of these challenges. First, we develop a 3D-printed TMS stand that is fully adjustable and can reach most areas of the scalp. The stand is compatible with various MR coils and features an adjustable mirror holder. Second, we create an interface that can precisely time the TMS pulses with respect to the fMRI image acquisition with a variance of under 1 ms. Third, we develop software for precisely determining the TMS coil position inside the MRI scanner and computing the location of maximal stimulation. All three tools are either free or inexpensive. We provide detailed instructions for building and implementing these tools to facilitate an efficient and reliable concurrent TMS-fMRI setup.
This paper presents a technique for finding the surface normal of an object from a set of images obtained under different lighting positions. The method presented is based on the principles of Photometric Stereo (PS) combined with Optimum Experimental Design (OED) and Parameter Estimation (PE). Unclear by the approach of photometric stereo, and many models based thereon, is how to position the light sources. So far, this is done by using heuristic approaches this leads to suboptimal and non-data driven positioning of the light sources. But what if the optimal positions of the light sources are calculated for photometric stereo? To this end, in this contribution, the effect of positioning the light sources on the quality of the normal vector for PS is evaluated. Furthermore, a new approach in this direction is derived and formulated. For the calculation of the surface normal of a Lambertian surface, the approach based on calibrated photometric stereo; for the estimation the optimal position of the light sources the approach is premised on parameter estimation and optimum experimental design. The approach is tested using synthetic and real-data. Based on results it can be seen that the surface normal estimated with the new method is more detailed than with conventional methods.
Past works that investigate out-of-domain performance of QA systems have mainly focused on general domains (e.g. news domain, wikipedia domain), underestimating the importance of subdomains defined by the internal characteristics of QA datasets. In this paper, we extend the scope of "out-of-domain" by splitting QA examples into different subdomains according to their several internal characteristics including question type, text length, answer position. We then examine the performance of QA systems trained on the data from different subdomains. Experimental results show that the performance of QA systems can be significantly reduced when the train data and test data come from different subdomains. These results question the generalizability of current QA systems in multiple subdomains, suggesting the need to combat the bias introduced by the internal characteristics of QA datasets.
All the existing source-seeking algorithms for unicycle models in GPS-denied settings guarantee at best an exponential rate of convergence over an infinite interval. Using the recently introduced time-varying feedback tools for prescribed-time stabilization, we achieve source seeking in prescribed time, i.e., the convergence to a small but bounded neighborhood of the source, without the measurements of the position and velocity of the unicycle, in as short a time as the user desires, starting from an arbitrary distance from the source. The practical convergence is established using a singularly perturbed version of the Lie bracket averaging method, combined with time dilation and time contraction operations. The algorithm is robust, provably, even to bounded nonvanishing perturbations and an arbitrarily strong gradient-dependent repulsive velocity drift emanating from the source.
Probing Pre-trained Language Models (PLMs) using prompts has indirectly implied that language models (LMs) can be treated as knowledge bases. To this end, this phenomena has been effective especially when these LMs are fine-tuned towards not just data of a specific domain, but also to the style or linguistic pattern of the prompts themselves. We observe that, satisfying a particular linguistic pattern in prompts is an unsustainable constraint that unnecessarily lengthens the probing task, especially because, they are often manually designed and the range of possible prompt template patterns can vary depending on the prompting objective and domain. We therefore explore an idea of using a position-attention mechanism to capture positional information of each word in a prompt relative to the mask to be filled, hence avoiding the need to re-construct prompts when the prompts linguistic pattern changes. Using our approach, we demonstrate the ability of eliciting answers to rare prompt templates (in a case study on health outcome generation) such as Postfix and Mixed patterns whose missing information is respectively at the start and in multiple random places of the prompt. More so, using various biomedical PLMs, our approach consistently outperforms a baseline in which the default mask language model (MLM) representation is used to predict masked tokens.
This paper studies the optimal state estimation for a dynamic system, whose transfer function can be nonlinear and the input noise can be of arbitrary distribution. Our algorithm differs from the conventional extended Kalman filter (EKF) and the particle filter (PF) in that it estimates not only the state vector but also the Cramer-Rao bound (CRB), which serves as an accuracy indicator. Combining the state estimation, the CRB, and the incoming new measurement, the algorithm updates the state estimation according to the maximum likelihood (ML) criterion. To illustrate the effectiveness of the proposed method for autonomous driving, we apply it to estimate the position and velocity of a vehicle based on the noisy measurements of distance and Doppler offset. Simulation results show that the proposed algorithm can achieve estimation significantly more accurate than the standard EKF and the PF.
Compressive approaches provide a means of effective channel high resolution channel estimates in millimeter wave MIMO systems, despite the use of analog and hybrid architectures. Such estimates can also be used as part of a joint channel estimation and localization solution. Achieving good localization performance, though, requires high resolution channel estimates and better methods to exploit those channels. In this paper, we propose a low complexity multidimensional orthogonal matching pursuit strategy for compressive channel estimation based by operating with a product of independent dictionaries for the angular and delay domains, instead of a global large dictionary. This leads to higher quality channel estimates but with lower complexity than generalizations of conventional solutions. We couple this new algorithm with a novel localization formulation that does not rely on the absolute time of arrival of the LoS path and exploits the structure of reflections in indoor channels. We show how the new approach is able to operate in realistic 3D scenarios to estimate the communication channel and locate devices in an indoor simulation setting.
Estimating Head-Related Transfer Functions (HRTFs) of arbitrary source points is essential in immersive binaural audio rendering. Computing each individual's HRTFs is challenging, as traditional approaches require expensive time and computational resources, while modern data-driven approaches are data-hungry. Especially for the data-driven approaches, existing HRTF datasets differ in spatial sampling distributions of source positions, posing a major problem when generalizing the method across multiple datasets. To alleviate this, we propose a deep learning method based on a novel conditioning architecture. The proposed method can predict an HRTF of any position by interpolating the HRTFs of known distributions. Experimental results show that the proposed architecture improves the model's generalizability across datasets with various coordinate systems. Additional demonstrations show that the model robustly reconstructs the target HRTFs from the spatially downsampled HRTFs in both quantitative and perceptual measures.
Session-based recommendation is a practical recommendation task that predicts the next item based on an anonymous behavior sequence, and its performance relies heavily on the transition information between items in the sequence. The SOTA methods in SBR employ GNN to model neighboring item transitions from global (i.e, other sessions) and local (i.e, current session) contexts. However, most existing methods treat neighbors from different sessions equally without considering that the neighbor items from different sessions may share similar features with the target item on different aspects and may have different contributions. In other words, they have not explored finer-granularity transition information between items in the global context, leading to sub-optimal performance. In this paper, we fill this gap by proposing a novel Transition Information Enhanced Disentangled Graph Neural Network (TIE-DGNN) model to capture finer-granular transition information between items and try to interpret the reason of the transition by modeling the various factors of the item. Specifically, we propose a position-aware global graph, which utilizes the relative position information to model the neighboring item transition. Then, we slice item embeddings into blocks, each of which represents a factor, and use disentangling module to separately learn the factor embeddings over the global graph. For local context, we train item embeddings by using attention mechanisms to capture transition information from the current session. To this end, our model considers two levels of transition information. Especially in global text, we not only consider finer-granularity transition information between items but also take user intents at factor-level into account to interpret the key reason for the transition. Extensive experiments on three datasets demonstrate the superiority of our method over the SOTA methods.
Myoelectric pattern recognition is one of the important aspects in the design of the control strategy for various applications including upper-limb prostheses and bio-robotic hand movement systems. The current work has proposed an approach to design an energy-efficient EMG-based controller by considering a kernelized SVM classifier for decoding the information of surface electromyography (sEMG) signals to infer the underlying muscle movements. In order to achieve the optimized performance of the EMG-based controller, our main strategy of classifier design is to reduce the false movements of the overall system (when the EMG-based controller is at the `Rest' position). To this end, we have formulated the training algorithm of the proposed supervised learning system as a general constrained multi-objective optimization problem. An elitist multi-objective evolutionary algorithm $-$ the non-dominated sorting genetic algorithm II (NSGA-II) has been used to tune the hyperparameters of SVM. We have presented the experimental results by performing the experiments on a dataset consisting of the sEMG signals collected from eleven subjects at five different upper limb positions. Furthermore, the performance of the trained models based on the two-objective metrics, namely classification accuracy, and false-negative have been evaluated on two different test sets to examine the generalization capability of the proposed training approach while implementing limb-position invariant EMG classification. It is evident from the presented result that the proposed approach provides much more flexibility to the designer in selecting the parameters of the classifier to optimize the energy efficiency of the EMG-based controller.
Millimeter Waves (mmW) and sub-THz frequencies are the candidate bands for the upcoming Sixth Generation (6G) of communication systems. The use of collimated beams at mmW/sub-THz to compensate for the increased path and penetration loss arises the need for a seamless Beam Management (BM), especially for high mobility scenarios such as the Vehicle-to-Infrastructure (V2I) one. Recent research advances in Integrated Sensing and Communication (ISAC) indicate that equipping the network infrastructure, e.g., the Base Station (BS), with either a stand-alone radar or sensing capabilities using optimized waveforms, represents the killer technology to facilitate the BM. However, radio sensing should accurately localize the Vehicular Equipment (VE)'s antenna, which is not guaranteed in general. Differently, employing side information from VE's onboard positioning sensors might overcome this limitation at the price of an increased control signaling between VE and BS. This paper provides a pragmatic comparison between radar-assisted and position-assisted BM for mmW V2I systems in a typical urban scenario in terms of BM training time and beamforming gain loss due to a wrong BM decision. Simulation results, supported by experimental evidence, show that the point target approximation of a traveling VE does not hold in practical V2I scenarios with radar-equipped BS. Therefore, the true antenna position has a residual uncertainty that is independent of radar's resolution and implies 50\,\% more BM training time on average. Moreover, there is not a winning technology for BM between BS-mounted radar and VE's onboard positioning systems. They provide complementary performance, depending on position, although outperforming blind BM techniques compared to conventional blind methods. Thus, we propose to optimally combine radar and positioning information in a multi-technology integrated BM solution.
Unbiased learning to rank has been proposed to alleviate the biases in the search ranking, making it possible to train ranking models with user interaction data. In real applications, search engines are designed to display only the most relevant k documents from the retrieved candidate set. The rest candidates are discarded. As a consequence, position bias and sample selection bias usually occur simultaneously. Existing unbiased learning to rank approaches either focus on one type of bias (e.g., position bias) or mitigate the position bias and sample selection bias with separate components, overlooking their associations. In this study, we first analyze the mechanisms and associations of position bias and sample selection bias from the viewpoint of a causal graph. Based on the analysis, we propose Causal Likelihood Decomposition (CLD), a unified approach to simultaneously mitigating these two biases in top-k learning to rank. By decomposing the log-likelihood of the biased data as an unbiased term that only related to relevance, plus other terms related to biases, CLD successfully detaches the relevance from position bias and sample selection bias. An unbiased ranking model can be obtained from the unbiased term, via maximizing the whole likelihood. An extension to the pairwise neural ranking is also developed. Advantages of CLD include theoretical soundness and a unified framework for pointwise and pairwise unbiased top-k learning to rank. Extensive experimental results verified that CLD, including its pairwise neural extension, outperformed the baselines by mitigating both the position bias and the sample selection bias. Empirical studies also showed that CLD is robust to the variation of bias severity and the click noise.