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We propose a novel resource efficient analog over-the-air (OTA) computation framework to address the demanding requirements of the uplink (UL) fronthaul between the access points (APs) and the central processing unit (CPU) in cell-free massive multiple-input multiple-output (MIMO) systems. We discuss the drawbacks of the wired and wireless fronthaul solutions, and show that our proposed mechanism is efficient and scalable as the number of APs increases. We present the transmit precoding and two-phase power assignment strategies at the APs to coherently combine the signals OTA in a spectrally efficient manner. We derive the statistics of the APs locally available signals which enable us to to obtain the analytical expressions for the Bayesian and classical estimators of the OTA combined signals. We empirically evaluate the normalized mean square error (NMSE), symbol error rate (SER), and the coded bit error rate (BER) of our developed solution and benchmark against the state-of-the-art wired fronthaul based system

Compositional generalisation (CG), in NLP and in machine learning more generally, has been assessed mostly using artificial datasets. It is important to develop benchmarks to assess CG also in real-world natural language tasks in order to understand the abilities and limitations of systems deployed in the wild. To this end, our GenBench Collaborative Benchmarking Task submission utilises the distribution-based compositionality assessment (DBCA) framework to split the Europarl translation corpus into a training and a test set in such a way that the test set requires compositional generalisation capacity. Specifically, the training and test sets have divergent distributions of dependency relations, testing NMT systems' capability of translating dependencies that they have not been trained on. This is a fully-automated procedure to create natural language compositionality benchmarks, making it simple and inexpensive to apply it further to other datasets and languages. The code and data for the experiments is available at //github.com/aalto-speech/dbca.

Dynamically scheduled high-level synthesis (HLS) enables the use of load-store queues (LSQs) which can disambiguate data hazards at circuit runtime, increasing throughput in codes with unpredictable memory accesses. However, the increased throughput comes at the price of lower clock frequency and higher resource usage compared to statically scheduled circuits without LSQs. The lower frequency often nullifies any throughput improvements over static scheduling, while the resource usage becomes prohibitively expensive with large queue sizes. This paper presents a method for achieving dynamically scheduled memory operations in HLS without significant clock period and resource usage increase. We present a novel LSQ based on shift-registers enabled by the opportunity to specialize queue sizes to a target code in HLS. We show a method to speculatively allocate addresses to our LSQ, significantly increasing pipeline parallelism in codes that could not benefit from an LSQ before. In stark contrast to traditional load value speculation, we do not require pipeline replays and have no overhead on misspeculation. On a set of benchmarks with data hazards, our approach achieves an average speedup of 11$\times$ against static HLS and 5$\times$ against dynamic HLS that uses a state of the art LSQ from previous work. Our LSQ also uses several times fewer resources, scaling to queues with hundreds of entries, and supports both on-chip and off-chip memory.

Simultaneous sequence generation is a pivotal task for real-time scenarios, such as streaming speech recognition, simultaneous machine translation and simultaneous speech translation, where the target sequence is generated while receiving the source sequence. The crux of achieving high-quality generation with low latency lies in identifying the optimal moments for generating, accomplished by learning a mapping between the source and target sequences. However, existing methods often rely on task-specific heuristics for different sequence types, limiting the model's capacity to adaptively learn the source-target mapping and hindering the exploration of multi-task learning for various simultaneous tasks. In this paper, we propose a unified segment-to-segment framework (Seg2Seg) for simultaneous sequence generation, which learns the mapping in an adaptive and unified manner. During the process of simultaneous generation, the model alternates between waiting for a source segment and generating a target segment, making the segment serve as the natural bridge between the source and target. To accomplish this, Seg2Seg introduces a latent segment as the pivot between source to target and explores all potential source-target mappings via the proposed expectation training, thereby learning the optimal moments for generating. Experiments on multiple simultaneous generation tasks demonstrate that Seg2Seg achieves state-of-the-art performance and exhibits better generality across various tasks.

A digital twin (DT) is a virtual representation of physical process, products and/or systems that requires a high-fidelity computational model for continuous update through the integration of sensor data and user input. In the context of laser powder bed fusion (LPBF) additive manufacturing, a digital twin of the manufacturing process can offer predictions for the produced parts, diagnostics for manufacturing defects, as well as control capabilities. This paper introduces a parameterized physics-based digital twin (PPB-DT) for the statistical predictions of LPBF metal additive manufacturing process. We accomplish this by creating a high-fidelity computational model that accurately represents the melt pool phenomena and subsequently calibrating and validating it through controlled experiments. In PPB-DT, a mechanistic reduced-order method-driven stochastic calibration process is introduced, which enables the statistical predictions of the melt pool geometries and the identification of defects such as lack-of-fusion porosity and surface roughness, specifically for diagnostic applications. Leveraging data derived from this physics-based model and experiments, we have trained a machine learning-based digital twin (PPB-ML-DT) model for predicting, monitoring, and controlling melt pool geometries. These proposed digital twin models can be employed for predictions, control, optimization, and quality assurance within the LPBF process, ultimately expediting product development and certification in LPBF-based metal additive manufacturing.

Deep learning has shown promise in decoding brain signals, such as electroencephalogram (EEG), in the field of brain-computer interfaces (BCIs). However, the non-stationary characteristics of EEG signals pose challenges for training neural networks to acquire appropriate knowledge. Inconsistent EEG signals resulting from these non-stationary characteristics can lead to poor performance. Therefore, it is crucial to investigate and address sample inconsistency to ensure robust performance in spontaneous BCIs. In this study, we introduce the concept of sample dominance as a measure of EEG signal inconsistency and propose a method to modulate its effect on network training. We present a two-stage dominance score estimation technique that compensates for performance degradation caused by sample inconsistencies. Our proposed method utilizes non-parametric estimation to infer sample inconsistency and assigns each sample a dominance score. This score is then aggregated with the loss function during training to modulate the impact of sample inconsistency. Furthermore, we design a curriculum learning approach that gradually increases the influence of inconsistent signals during training to improve overall performance. We evaluate our proposed method using public spontaneous BCI dataset. The experimental results confirm that our findings highlight the importance of addressing sample dominance for achieving robust performance in spontaneous BCIs.

In this paper we present a non-local numerical scheme based on the Local Discontinuous Galerkin method for a non-local diffusive partial differential equation with application to traffic flow. In this model, the velocity is determined by both the average of the traffic density as well as the changes in the traffic density at a neighborhood of each point. We discuss nonphysical behaviors that can arise when including diffusion, and our measures to prevent them in our model. The numerical results suggest that this is an accurate method for solving this type of equation and that the model can capture desired traffic flow behavior. We show that computation of the non-local convolution results in $\mathcal{O}(n^2)$ complexity, but the increased computation time can be mitigated with high-order schemes like the one proposed.

The current body of research on terahertz (THz) wireless communications predominantly focuses on its application for single-user backhaul/fronthaul connectivity at sub-THz frequencies. First, we develop a generalized statistical model for signal propagation at THz frequencies encompassing physical layer impairments, including random path-loss with Gamma distribution for the molecular absorption coefficient, short-term fading characterized by the $\alpha$-$\eta$-$\kappa$-$\mu$ distribution, antenna misalignment errors, and transceiver hardware impairments. Next, we propose random access protocols for a cell-free wireless network, ensuring successful transmission for multiple users with limited delay and energy loss, exploiting the combined effect of random atmospheric absorption, non-linearity of fading, hardware impairments, and antenna misalignment errors. We consider two schemes: a fixed transmission probability (FTP) scheme where the transmission probability (TP) of each user is updated at the beginning of the data transmission and an adaptive transmission probability (ATP) scheme where the TP is updated with each successful reception of the data. We analyze the performance of both protocols using delay, energy consumption, and outage probability with scaling laws for the transmission of a data frame consisting of a single packet from users at a predefined quality of service (QoS).

A peculiarity of conversational search systems is that they involve mixed-initiatives such as system-generated query clarifying questions. Evaluating those systems at a large scale on the end task of IR is very challenging, requiring adequate datasets containing such interactions. However, current datasets only focus on either traditional ad-hoc IR tasks or query clarification tasks, the latter being usually seen as a reformulation task from the initial query. The only two datasets known to us that contain both document relevance judgments and the associated clarification interactions are Qulac and ClariQ. Both are based on the TREC Web Track 2009-12 collection, but cover a very limited number of topics (237 topics), far from being enough for training and testing conversational IR models. To fill the gap, we propose a methodology to automatically build large-scale conversational IR datasets from ad-hoc IR datasets in order to facilitate explorations on conversational IR. Our methodology is based on two processes: 1) generating query clarification interactions through query clarification and answer generators, and 2) augmenting ad-hoc IR datasets with simulated interactions. In this paper, we focus on MsMarco and augment it with query clarification and answer simulations. We perform a thorough evaluation showing the quality and the relevance of the generated interactions for each initial query. This paper shows the feasibility and utility of augmenting ad-hoc IR datasets for conversational IR.

Multi-relation Question Answering is a challenging task, due to the requirement of elaborated analysis on questions and reasoning over multiple fact triples in knowledge base. In this paper, we present a novel model called Interpretable Reasoning Network that employs an interpretable, hop-by-hop reasoning process for question answering. The model dynamically decides which part of an input question should be analyzed at each hop; predicts a relation that corresponds to the current parsed results; utilizes the predicted relation to update the question representation and the state of the reasoning process; and then drives the next-hop reasoning. Experiments show that our model yields state-of-the-art results on two datasets. More interestingly, the model can offer traceable and observable intermediate predictions for reasoning analysis and failure diagnosis, thereby allowing manual manipulation in predicting the final answer.