pwc-archive/papers-with-abstracts huggingface dataset:
Computing Functions Over-the-Air Using Digital Modulations
Authors: Saeed Razavikia, Jose Mairton Barros da Silva Jr, Carlo Fischione
Date: 2023-03-01 00:00:00

Over-the-air computation (AirComp) is a known technique in which wireless devices transmit values by analog amplitude modulation so that a function of these values is computed over the communication channel at a common receiver. The physical reason is the superposition properties of the electromagnetic waves, which naturally return sums of analog values. Consequently, the applications of AirComp are almost entirely restricted to analog communication systems. However, the use of digital communications for over-the-air computations would have several benefits, such as error correction, synchronization, acquisition of channel state information, and easier adoption by current digital communication systems. Nevertheless, a common belief is that digital modulations are generally unfeasible for computation tasks because the overlapping of digitally modulated signals returns signals that seem to be meaningless for these tasks. This paper breaks through such a belief and proposes a fundamentally new computing method, named ChannelComp, for performing over-the-air computations by any digital modulation. In particular, we propose digital modulation formats that allow us to compute a wider class of functions than AirComp can compute, and we propose a feasibility optimization problem that ascertains the optimal digital modulation for computing functions over-the-air. The simulation results verify the superior performance of ChannelComp in comparison to AirComp, particularly for the product functions, with around 10 dB improvement of the computation error.
Planning and Control of Uncertain Cooperative Mobile Manipulator-Endowed Systems under Temporal-Logic Tasks
Authors: Christos Verginis
Date: 2023-03-02 00:00:00
Tasks: continuous-control, Continuous Control, Motion Planning, Object

Control and planning of multi-agent systems is an active and increasingly studied topic of research, with many practical applications such as rescue missions, security, surveillance, and transportation. This thesis addresses the planning and control of multi-agent systems under temporal logic tasks. The considered systems concern complex, robotic, manipulator-endowed systems, which can coordinate in order to execute complicated tasks, including object manipulation/transportation. Motivated by real-life scenarios, we take into account high-order dynamics subject to model uncertainties and unknown disturbances. Our approach is based on the integration of tools from the areas of multi-agent systems, intelligent control theory, cooperative object manipulation, discrete abstraction design of multi-agent-object systems, and formal verification. The first part of the thesis is devoted to the design of continuous control protocols for cooperative object manipulation/transportation by multiple robotic agents, and the relation of rigid cooperative manipulation schemes to multi-agent formation. In the second part of the thesis, we develop control schemes for the continuous coordination of multi-agent complex systems with uncertain dynamics, focusing on multi-agent navigation with collision specifications in obstacle-cluttered environments. The third part of the thesis is focused on the planning and control of multi-agent and multi-agent-object systems subject to complex tasks expressed as temporal logic formulas. The fourth and final part of the thesis focuses on several extension schemes for single-agent setups, such as motion planning under timed temporal tasks and asymptotic reference tracking for unknown systems while respecting funnel constraints.
Improving Transformer-based End-to-End Speaker Diarization by Assigning Auxiliary Losses to Attention Heads
Authors: Ye-Rin Jeoung, Joon-Young Yang, Jeong-Hwan Choi, Joon-Hyuk Chang
Date: 2023-03-02 00:00:00
Tasks: Action Detection, Activity Detection, speaker-diarization, Speaker Diarization

Transformer-based end-to-end neural speaker diarization (EEND) models utilize the multi-head self-attention (SA) mechanism to enable accurate speaker label prediction in overlapped speech regions. In this study, to enhance the training effectiveness of SA-EEND models, we propose the use of auxiliary losses for the SA heads of the transformer layers. Specifically, we assume that the attention weight matrices of an SA layer are redundant if their patterns are similar to those of the identity matrix. We then explicitly constrain such matrices to exhibit specific speaker activity patterns relevant to voice activity detection or overlapped speech detection tasks. Consequently, we expect the proposed auxiliary losses to guide the transformer layers to exhibit more diverse patterns in the attention weights, thereby reducing the assumed redundancies in the SA heads. The effectiveness of the proposed method is demonstrated using the simulated and CALLHOME datasets for two-speaker diarization tasks, reducing the diarization error rate of the conventional SA-EEND model by 32.58% and 17.11%, respectively.
DISPLACE Challenge: DIarization of SPeaker and LAnguage in Conversational Environments
Authors: Shikha Baghel, Shreyas Ramoji, Sidharth, Ranjana H, Prachi Singh, Somil Jain, Pratik Roy Chowdhuri, Kaustubh Kulkarni, Swapnil Padhi, Deepu Vijayasenan, Sriram Ganapathy
Date: 2023-03-01 00:00:00
Tasks: speaker-diarization, Speaker Diarization

In multilingual societies, social conversations often involve code-mixed speech. The current speech technology may not be well equipped to extract information from multi-lingual multi-speaker conversations. The DISPLACE challenge entails a first-of-kind task to benchmark speaker and language diarization on the same data, as the data contains multi-speaker conversations in multilingual code-mixed speech. The challenge attempts to highlight outstanding issues in speaker diarization (SD) in multilingual settings with code-mixing. Further, language diarization (LD) in multi-speaker settings also introduces new challenges, where the system has to disambiguate speaker switches with code switches. For this challenge, a natural multilingual, multi-speaker conversational dataset is distributed for development and evaluation purposes. The systems are evaluated on single-channel far-field recordings. We also release a baseline system and report the highlights of the system submissions.
audb -- Sharing and Versioning of Audio and Annotation Data in Python
Authors: Hagen Wierstorf, Johannes Wagner, Florian Eyben, Felix Burkhardt, Björn W. Schuller
Date: 2023-03-01 00:00:00
Tasks: Management

Driven by the need for larger and more diverse datasets to pre-train and fine-tune increasingly complex machine learning models, the number of datasets is rapidly growing. audb is an open-source Python library that supports versioning and documentation of audio datasets. It aims to provide a standardized and simple user-interface to publish, maintain, and access the annotations and audio files of a dataset. To efficiently store the data on a server, audb automatically resolves dependencies between versions of a dataset and only uploads newly added or altered files when a new version is published. The library supports partial loading of a dataset and local caching for fast access. audb is a lightweight library and can be interfaced from any machine learning library. It supports the management of datasets on a single PC, within a university or company, or within a whole research community.
Towards domain generalisation in ASR with elitist sampling and ensemble knowledge distillation
Authors: Rehan Ahmad, Md Asif Jalal, Muhammad Umar Farooq, Anna Ollerenshaw, Thomas Hain
Date: 2023-03-01 00:00:00
Tasks: Domain Adaptation, Knowledge Distillation, Model Compression

Knowledge distillation has widely been used for model compression and domain adaptation for speech applications. In the presence of multiple teachers, knowledge can easily be transferred to the student by averaging the models output. However, previous research shows that the student do not adapt well with such combination. This paper propose to use an elitist sampling strategy at the output of ensemble teacher models to select the best-decoded utterance generated by completely out-of-domain teacher models for generalizing unseen domain. The teacher models are trained on AMI, LibriSpeech and WSJ while the student is adapted for the Switchboard data. The results show that with the selection strategy based on the individual models posteriors the student model achieves a better WER compared to all the teachers and baselines with a minimum absolute improvement of about 8.4 percent. Furthermore, an insights on the model adaptation with out-of-domain data has also been studied via correlation analysis.
First-shot anomaly sound detection for machine condition monitoring: A domain generalization baseline
Authors: Noboru Harada, Daisuke Niizumi, Yasunori Ohishi, Daiki Takeuchi, Masahiro Yasuda
Date: 2023-03-01 00:00:00
Tasks: Anomaly Detection, Domain Generalization, Task 2, Unsupervised Anomaly Detection

This paper provides a baseline system for First-shot-compliant unsupervised anomaly detection (ASD) for machine condition monitoring. First-shot ASD does not allow systems to do machine-type dependent hyperparameter tuning or tool ensembling based on the performance metric calculated with the grand truth. To show benchmark performance for First-shot ASD, this paper proposes an anomaly sound detection system that works on the domain generalization task in the Detection and Classification of Acoustic Scenes and Events (DCASE) 2022 Challenge Task 2: "Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Technique" while complying with the First-shot requirements introduced in the DCASE 2023 Challenge Task 2 (DCASE2023T2). A simple autoencoder based implementation combined with selective Mahalanobis metric is implemented as a baseline system. The performance evaluation is conducted to set the target benchmark for the forthcoming DCASE2023T2. Source code of the baseline system will be available on GitHub: https://github.com/nttcslab/dcase2023_task2_baseline_ae .
Co-Design of Topology, Scheduling, and Path Planning in Automated Warehouses
Authors: Christopher Leet, Chanwook Oh, Michele Lora, Sven Koenig, Pierluigi Nuzzo
Date: 2023-03-02 00:00:00
Tasks: Scheduling

We address the warehouse servicing problem (WSP) in automated warehouses, which use teams of mobile agents to bring products from shelves to packing stations. Given a list of products, the WSP amounts to finding a plan for a team of agents which brings every product on the list to a station within a given timeframe. The WSP consists of four subproblems, concerning what tasks to perform (task formulation), who will perform them (task allocation), and when (scheduling) and how (path planning) to perform them. These subproblems are NP-hard individually and become more challenging in combination. The difficulty of the WSP is compounded by the scale of automated warehouses, which frequently use teams of hundreds of agents. In this paper, we present a methodology that can solve the WSP at such scales. We introduce a novel, contract-based design framework which decomposes an automated warehouse into traffic system components. By assigning each of these components a contract describing the traffic flows it can support, we can synthesize a traffic flow satisfying a given WSP instance. Component-wise search-based path planning is then used to transform this traffic flow into a plan for discrete agents in a modular way. Evaluation shows that this methodology can solve WSP instances on real automated warehouses.
Switched Lyapunov Function based Controller Synthesis for Networked Control Systems: A Computationally Inexpensive Approach
Authors: Katarina Stanojevic, Martin Steinberger, Martin Horn
Date: 2023-03-02 00:00:00
Tasks: Computational Efficiency

This paper presents a Lyapunov function based control strategy for networked control systems (NCS) affected by variable time delays and data loss. A special focus is put on the reduction of the computational complexity. A specific buffering mechanism is defined first, such that it adds an additional delay up to one sampling period. The resulting buffered NCS can then be formulated as a switched system which leads to the significant simplification of the NCS model and subsequent controller synthesis. The novel approach does not only circumvent the need for any over-approximation technique, since the switched NCS model can be used for stability analysis directly, but also reduces the infinite set of allowable values of the dynamic matrix to a small finite set. The proposed strategy leads hereby to a strongly decreased number of optimization variables and linear matrix inequalities (LMIs) which allows greater flexibility with respect to additional degrees of freedom affecting the transient behavior. The performance and computational efficiency of the control strategy are demonstrated by means of simulation example.
Co-Optimization of Adaptive Cruise Control and Hybrid Electric Vehicle Energy Management via Model Predictive Mixed Integer Control
Authors: Qitao Li, Changfu Gong, Yuan Lin
Date: 2023-03-02 00:00:00
Tasks: energy management, Management, Model Predictive Control

In this paper, a model predictive mixed integer control method for BYD Qin Plus DM-i (Dual Model intelligent) plug-in hybrid electric vehicle (PHEV) is proposed for co-optimization to reduce fuel consumption during car following. First, the adaptive cruise control (ACC) model for energy-saving driving is established. Then, a control-oriented energy management strategy (EMS) model considering the clutch engagement and disengagement is constructed. Finally, the co-optimization structure by integrating ACC model and EMS model is created and is converted to the mixed integer nonlinear programming (MINLP). The results show that this modeling method can be applied to EMS based on the model predictive control (MPC) framework and verify that co-optimization can achieve a 5.1$\%$ reduction in fuel consumption compared to sequential optimization with the guarantee of ACC performance.
Distribution grid power flexibility aggregation at multiple interconnections between the high and extra high voltage grid levels
Authors: Neelotpal Majumdar, Marcel Sarstedt, Lutz Hofmann
Date: 2023-03-02 00:00:00
Tasks: Management, Vocal Bursts Intensity Prediction

The energy transition towards renewable based power provision requires improved monitoring and control of distributed energy resources (DERs), installed predominantly at the distribution grid level. Due to the gradual phase out of thermal generation, a shift of ancillary services provision like voltage control, congestion management and dynamic support from DERs is underway. Increased planning for procurement of ancillary services from underlying grid levels is required. Therefore, provision of flexible active and reactive power potentials from distribution system operators to transmission system operators at the vertical system interface is a subject of current research. At present, provision of active and reactive power flexibilities (PQ-flexibilities) across radial system interconnections are investigated, which involves a single transformer branch interconnection across two different voltage levels. Inclusion of multiple interconnections in a meshed grid structure increases the complexity as proximal interdependencies of interconnections to PQ-flexibilities require consideration. The objective of this paper is to address the flexibility aggregation across multiple vertical interconnections. Alternating current power transfer distribution factors (AC-PTDFs) are used to determine the power flow across the interconnections. Subsequent integration in a linear optimization environment controls the interconnection power flows (IPF) using a weighted objective function. Therefore, power flow regulation is enabled according to the requirements and specifications of both the underlying and overlaying grid level. The results show interdependent concentric flexibility regions or Feasible Operating Regions (FORs) in accordance with the manipulation of the weighted objective function.
Fast Randomized Subspace System Identification for Large I/O Data
Authors: Vatsal Kedia, Debraj Chakraborty
Date: 2023-03-02 00:00:00
Tasks: State Space Models, subspace methods

In this article, a novel fast randomized subspace system identification method for estimating combined deterministic-stochastic LTI state-space models, is proposed. The algorithm is especially well-suited to identify high-order and multi-scale systems with both fast and slow dynamics, which typically require a large number of input-output data samples for accurate identification using traditional subspace methods. Instead of working with such large matrices, the dataset is compressed using randomized methods, which preserve the range-spaces of these matrices almost surely. A novel identification algorithm using this compressed dataset, is proposed. This method enables the handling of extremely large datasets, which often make conventional algorithms like N4SID, MOESP, etc. run out of computer memory. Moreover the proposed method outperforms these algorithms in terms of memory-cost, data-movement, flop count and computation time for cases where these algorithms still work in-spite of large data sizes. The effectiveness of the proposed algorithm is established by theoretical analysis and various real and simulated case studies.
Challenges and Opportunities for Beyond-5G Wireless Security
Authors: Eric Ruzomberka, David J. Love, Christopher G. Brinton, Arpit Gupta, Chih-Chun Wang, H. Vincent Poor
Date: 2023-03-01 00:00:00

The demand for broadband wireless access is driving research and standardization of 5G and beyond-5G wireless systems. In this paper, we aim to identify emerging security challenges for these wireless systems and pose multiple research areas to address these challenges.
Highly Efficient Year-Round Energy and Comfort Optimization of HVAC Systems in Electric City Buses
Authors: Fabio Widmer, Andreas Ritter, Mathias Achermann, Fabian Büeler, Joshua Bagajo, Christopher H. Onder
Date: 2023-03-01 00:00:00

In this paper, we present a novel approach to perform highly efficient numerical simulations of the heating, ventilation, and air-conditioning (HVAC) system of an electric city bus. The models for this simulation are based on the assumption of a steady-state operation. We show two approaches to obtain the minimum energy requirement for a certain thermal comfort criterion under specific ambient conditions. Due to the computationally efficient approach developed, we can evaluate the model on a large dataset of 7500 scenarios in various ambient conditions to estimate the year-round performance of the system subject to different comfort requirements. Compared to a heating strategy based on positive temperature coefficient (PTC) elements, we can thus show that a heat pump (HP) can reduce the annual mean power consumption by up to 60%. Ceiling-mounted radiant heating elements complementing a PTC heating system can reduce the annual mean power consumption by up to 10%, while they cannot improve the energy efficiency when used in conjunction with a HP. Finally, a broad sensitivity study reveals the fact that improving the HP's heating-mode coefficient of performance (COP) manifests the largest leverage in terms of mean annual power consumption. Moreover, the annual energy expenditure for cooling are around eight times smaller than those for heating. The case study considered thus reveals that the advantages of improving the COP of the cooling mode are significantly lower.
LQR-trees with Sampling Based Exploration of the State Space
Authors: Jiří Fejlek, Stefan Ratschan
Date: 2023-03-01 00:00:00
Tasks: Motion Planning, valid

This paper introduces an extension of the LQR-tree algorithm, which is a feedback-motion-planning algorithm for stabilizing a system of ordinary differential equations from a bounded set of initial conditions to a goal. The constructed policies are represented by a tree of exemplary system trajectories, so called demonstrations, and linear-quadratic regulator (LQR) feedback controllers. Consequently, the crucial component of any LQR-tree algorithm is a demonstrator that provides suitable demonstrations. In previous work, such a demonstrator was given by a local trajectory optimizer. However, these require appropriate initial guesses of solutions to provide valid results, which was pointed out, but largely unresolved in previous implementations. In this paper, we augment the LQR-tree algorithm with a randomized motion-planning procedure to discover new valid demonstration candidates to initialize the demonstrator in parts of state space not yet covered by the LQR-tree. In comparison to the previous versions of the LQR-tree algorithm, the resulting exploring LQR-tree algorithm reliably synthesizes feedback control laws for a far more general set of problems.
Optimal Placement of Electric Vehicle Charging Stations in Populated Regions of Tehran for Various Demands Distribution
Authors: Armin Mosavi
Date: 2023-03-01 00:00:00

Development of clean transportation using electric vehicles is one of the best ways to deal with environmental pollution and global warming. However, there are many challenges, one of the main of which is the development of charging infrastructure and their optimal location. For this purpose, in this article, the problem of finding the optimal location of charging stations in the densely populated areas of Tehran, considering the parking lots and gas stations has been addressed. First, a mathematical model based on Integer Linear Programming (ILP) is developed for this problem. In this model, we tried to add various constraints and statistical distributions to make the problem as realistic as possible. Then, by using the discrete Genetic Algorithm (GA), the optimal solution has been obtained from the point of view of the least amount of investment required for the construction of stations, as well as the greatest comfort for passengers in their daily life. In other words, the drivers are able to reach the first charging station by traveling the shortest possible distance while the government spends the least amount of investment to set them up.
Optimal Application of Trajectory Optimization by Travel Profile for Electric Train in an Electrical Transportation System
Authors: Armin Mosavi
Date: 2023-03-01 00:00:00
Tasks: energy management, Management

Increasing greenhouse gas (GHG) emission is one of the most important concerns of world decision-makers. The considerable part of it belongs to the transportation systems. As a solution, the world is going to use urban electrical transportation systems, and thus designing optimal methods and novel strategies is essential nowadays. Indeed, reducing the amount of consumed electrical energy and having optimum and efficient energy management for electrical means of transport requires several vital elements. Designing a travel profile is one of the most crucial parts of these methods. A travel profile for the fully electric train ET245 with three stations is designed and analyzed in Milan, Italy, as a case study in this paper. As the first step, route data are acquired by the Google Maps tool. Then, the train data is collected from the manufacturer's catalogues and datasheets. Finally, computations were performed by Excel spreadsheets. The results indicate that the designed path in the proposed travel profile is covered by a commercial speed and requires reasonable electrical energy, which is considered economical and advantageous.