Miracle-dz/UniFAR-FTUs huggingface dataset:
End-to-End Deep Learning-Based Adaptation Control for Linear Acoustic Echo Cancellation

The attenuation of acoustic loudspeaker echoes remains to be one of the open challenges to achieve pleasant full-duplex hands free speech communication. In many modern signal enhancement interfaces, this problem is addressed by a linear acoustic echo canceler which subtracts a loudspeaker echo estimate from the recorded microphone signal. To obtain precise echo estimates, the parameters of the echo canceler, i.e., the filter coefficients, need to be estimated quickly and precisely from the observed loudspeaker and microphone signals. For this a sophisticated adaptation control is required to deal with high-power double-talk and rapidly track time-varying acoustic environments which are often faced with portable devices. In this paper, we address this problem by end-to-end deep learning. In particular, we suggest to infer the step-size for a least mean squares frequency-domain adaptive filter update by a Deep Neural Network (DNN). Two different step-size inference approaches are investigated. On the one hand broadband approaches, which use a single DNN to jointly infer step-sizes for all frequency bands, and on the other hand narrowband methods, which exploit individual DNNs per frequency band. The discussion of benefits and disadvantages of both approaches leads to a novel hybrid approach which shows improved echo cancellation while requiring only small DNN architectures. Furthermore, we investigate the effect of different loss functions, signal feature vectors, and DNN output layer architectures on the echo cancellation performance from which we obtain valuable insights into the general design and functionality of DNN-based adaptation control algorithms.
KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations

While recent works have been considerably improving the quality of the natural language explanations (NLEs) generated by a model to justify its predictions, there is very limited research in detecting and alleviating inconsistencies among generated NLEs. In this work, we leverage external knowledge bases to significantly improve on an existing adversarial attack for detecting inconsistent NLEs. We apply our attack to high-performing NLE models and show that models with higher NLE quality do not necessarily generate fewer inconsistencies. Moreover, we propose an off-the-shelf mitigation method to alleviate inconsistencies by grounding the model into external background knowledge. Our method decreases the inconsistencies of previous high-performing NLE models as detected by our attack.
In symptomatic PAH, adding sotatercept to stable background therapy improved 6-min walk distance at 24 wk

SOURCE CITATION Hoeper MM, Badesch DB, Ghofrani HA, et al; STELLAR Trial Investigators. Phase 3 trial of sotatercept for treatment of pulmonary arterial hypertension. N Engl J Med. 2023;388:1478-1490. 36877098.
Cetacean diversity of the eastern South Atlantic Ocean and Vema Seamount detected during a visual and passive acoustic survey, 2019

Abstract Cetaceans in the eastern South Atlantic Ocean are poorly studied. We present results from a 2 week ship-based survey from Cape Town to Vema Seamount (980 km to the west) during October–November 2019, including visual and towed-hydrophone observations from the vessel, and 10 days of acoustic monitoring on the seamount. Fifty-two hours of visual surveys resulted in 39 encounters of whale groups including seven of humpback, six of fin and one sei whale, as well as four unidentified baleen whales, 18 unidentified balaenopterid whales and four unidentified odontocetes. Two humpback whales at the seamount were engaged in possible feeding behaviour. A large aggregation of mostly fin whales was observed near the continental shelf edge (22 encounters over a 70 × 50 km2 area, six fin, one sei whale, 15 not confirmed to species), an historic whaling ground for both fin and sei whales. Towed-hydrophone data (78.7 h) detected five groups of sperm whales, 45 of delphinids, one beaked whale and no Kogiids. Acoustic data from the seamount detected calls from several baleen whale species including humpback whale non-song calls, Antarctic minke ‘bioduck’ calls, sei whale down-sweep calls and a likely Bryde's whale call. Two call types could not be assigned to species, including the most detected – a simple frequency-modulated call with peak power around 130 Hz. This study contributes to an improved understanding of cetacean occurrence in the eastern South Atlantic Ocean and highlights the need for more research to improve identification of cetacean vocalizations in the region.
Towards a feature-based didactic framework for generating individualized programming tasks for an e-learning environment

Adaptive programming tasks are a promising approach for personalized learning that adapts to each student’s unique needs and abilities. However, developing effective adaptive programming tasks can be challenging, particularly when it comes to selecting the appropriate changes and adapting the difficulty of the exercise. In this paper, we propose a model for tracking student knowledge and adapting programming exercises to guide the selection and implementation of task features. Our model combines aspects of cognitive load, computational thinking and feature-oriented software product line engineering to identify core and optional features, so that they can be used in conjunction to adapt to the specific needs and abilities of each student. We provide an overview over the insights gained from an exploratory study with students. To support the creation process of feature-based programming tasks, we present an approach using a template-based generator.
Compiling a functional array language with non-semantic memory information

We present a technique for introducing a notion of memory in the compiler representation for a parallel functional array language, in a way that allows for transformation and optimization of memory access patterns and uses, while preserving value-based semantics. Functional languages expose no notion of memory to the user. There is no explicit allocation or deallocation of memory, nor any mapping from arrays to memory locations. Instead, the compiler must infer when and where to perform allocations as well as how values should be represented in memory. Because this affects performance, an optimizing compiler will need freedom to express various memory optimizations. Typically this is done by lowering the functional language to an imperative intermediate representation with a direct notion of memory, losing the ability to use high-level functional reasoning while performing said optimizations. We present a compiler representation where memory information is non-semantic, in the sense that it does not affect the program result, but only the operational details. We start by defining a simple functional language without memory, Fun, and give it static and dynamic semantics. Next, we define an extended language, FunMem, which is Fun with non-semantic memory information in the form of lmads and an allocation statement. We give the extended language static and dynamic semantics and we provide an algorithm for transforming a Fun program into FunMem. We likewise introduce a simple imperative language Imp, which we use to illustrate how one might translate FunMem into lower-level code. Finally, we show an example of a useful transformation in FunMem, memory expansion, which is used to hoist allocations from parallel GPU kernels.
First-Class Data Types in Shallow Embedded Domain-Specific Languages using Metaprogramming

Functional programming languages are excellent for hosting embedded domain specific languages (eDSLs) because of their rich type systems, minimal syntax, and referential transparency. However, data types defined in the host language are not automatically available in the embedded language. To do so, all the operations on the data type must be ported to the eDSL resulting in a lot of boilerplate. This paper shows that by using metaprogramming, all first-order user-defined data types can be automatically made first class in shallow embedded DSLs. We show this by providing an implementation in Template Haskell for a typical DSL with two different semantics. Furthermore, we show that by utilising quasiquotation, there is hardly any burden on the syntax. Finally, the paper also serves as a gentle introduction to Template Haskell.
Fairness in Ranking: From Values to Technical Choices and Back

In the past few years, there has been much work on incorporating fairness requirements into the design of algorithmic rankers, with contributions from the data management, algorithms, information retrieval, and recommender systems communities. In this tutorial, we give a systematic overview of this work, offering a broad perspective that connects formalizations and algorithmic approaches across subfields. During the first part of the tutorial, we present a classification framework for fairness-enhancing interventions, along which we will then relate the technical methods. This framework allows us to unify the presentation of mitigation objectives and of algorithmic techniques to help meet those objectives or identify trade-offs. Next, we discuss fairness in score-based ranking and in supervised learning-to-rank. We conclude with recommendations for practitioners, to help them select a fair ranking method based on the requirements of their specific application domain.
This Doesn’t Work! – An Explorative Study on Student Modeling Skills in the Context of Developing Hybrid Methods

For large-scale processes as implemented in organizations that develop software in regulated domains, comprehensive software process models are implemented, e.g., for compliance requirements. Creating and evolving such processes is demanding and requires software engineers having substantial modeling skills to create consistent and certifiable processes. While teaching process engineering to students, we observed issues in providing and explaining models. In this paper, we present an exploratory study in which we aim to shed light on the challenges students face when it comes to modeling. Our findings show that students are capable of doing basic modeling tasks, yet, fail in utilizing models correctly. We conclude that the required skills, notably abstraction and solution development, are underdeveloped due to missing practice and routine. Since modeling is key to many software engineering disciplines, we advocate for intensifying modeling activities in teaching.
SLR: From Saltzer and Schroeder to 2021…47 Years of Research on the Development and Validation of Security API Recommendations

Producing secure software is challenging. The poor usability of security Application Programming Interfaces (APIs) makes this even harder. Many recommendations have been proposed to support developers by improving the usability of cryptography libraries—rooted in wider best practice guidance in software engineering and API design. In this SLR, we systematize knowledge regarding these recommendations. We identify and analyze 65 papers, offering 883 recommendations. Through thematic analysis, we identify seven core ways to improve usability of APIs. Most of the recommendations focus on helping API developers to construct and structure their code and make it more usable and easier for programmers to understand. There is less focus, however, on documentation, writing requirements, code quality assessment, and the impact of organizational software development practices. By tracing and analyzing paper ancestry, we map how this knowledge becomes validated and translated over time. We find that very few API usability recommendations are empirically validated, and that recommendations specific to usable security APIs lag even further behind.
Modelling of the Monolithic Stiffener Forming Process from a Paek Thermoplastic Composite Matrix Using Pam-Form Software

Abstract The growing use of thermoplastic composites in aviation, automotive, sports and medical industries is forcing the development of processing technology. Due to the properties of thermoplastic composite materials, their shaping is subject to many restrictions. For this reason, it is not always possible to obtain components with complex geometry and adequate quality. By using numerical analysis and experience in the production of parts using hot pressing technology, we are able to predict the course of the process and the behaviour of the material during formation. The article describes how to build a model for numerical analysis of the process of thermoforming monolithic inspection door stiffening for an ILX-34 aircraft using a Toray Cetex® TC1225 carbon composite material with a thermoplastic polyaryletherketone (PAEK) matrix. Pam-Form v2019.0 software version V1.9. N was used for modelling. The results of the analysis were compared with those of the part produced by the Institute of Aviation, Łukasiewicz Research Network.
Effect of tip clearance on impeller discharge flow and vaneless diffuser performance of a centrifugal compressor

To see the tip clearance effect on a centrifugal compressor, time-resolved flow measurements are carried out at the discharge of a centrifugal compressor impeller with a diameter of 280 mm. The impeller is 30° back swept and is operated at a blade tip speed of 260 m/s, which is a rather high speed for measuring time-resolved flow. Six levels of tip clearance ratio ranging from 0.06 to 0.21 were tested. A fast response aerodynamic probe equipped with a single pressure sensor is used for the flow measurements. The correlation between the impeller efficiency and the tip clearance is presented. The mean flow variation from hub to shroud is obtained and its dependence on the tip clearance is discussed. The measured time-resolved flow at impeller exit differs from the classical jet-wake flow model. Four kinds of flow patterns (blade wake, jet, channel wake, and tip clearance flow) are observed at the impeller discharge plane. By increasing the tip clearance, the total pressure loss in the vaneless diffuser decreases, while the impeller loss increases. One-dimensional flow calculations of the vaneless diffuser show that the matching constant of a skin friction coefficient is decreased by increasing the tip clearance.
Finite-time stochastic stabilization for uncertain Markov jump systems subject to input constraint

In this paper, the problem of finite-time stochastic stabilization for a class of uncertain Markov jump systems subject to partly known transition probabilities and disturbances is investigated. The constraint on the control input sent to the actuator is also considered. A finite-time stochastic stabilization criterion is given, to ensure that the state trajectory remains in a bounded region of the state space over a fixed time interval in mean square sense. An optimization algorithm is proposed to find the optimal performance and the corresponding feedback controller where the undesirable high-gain phenomenon can be avoided. Finally, numerical examples are given to illustrate the effectiveness of the proposed method.
“Sex is sex, marriage is marriage”: infidelity amongst married women in Shamva, Zimbabwe

Many African societies adhere to a strong patriarchal norm as regards sexual relations, one that is unforgiving of female infidelity yet tolerates men’s extramarital affairs and even incorporates these into culture. This study reveals that, contrary to traditional assumptions defining women as a passive and powerless second sex, married women in a specific African township redefine their sexuality in ways that defy accepted cultural norms. In-depth, qualitative interviews with a sample of six Zimbabwean women — all of whom were involved in extramarital affairs at the time of the study — suggest that these women devised their own means of resistance to defy socially accepted norms and values and to become active sexual actors outside the marital domain. Some of the women also gain much-needed self-esteem from their extramarital affairs in that they experience these relationships as more egalitarian than the situations prevailing within their marriages.
Robust and Transferable Log-based Anomaly Detection

Log messages provide a valuable source of runtime information for ensuring the safety and consistency of systems. Recently, many machine learning and deep learning methods have been proposed to automatically detect anomalous log messages, obviating the need for manual detection by experts. However, we find that in practice, the effectiveness of existing learning-based methods is severely affected by incomplete information and distribution shift. Specifically, each log message can actually be parsed into a fixed number of key information fields, while existing methods analyze log messages using only the log event information and ignore other useful information fields that can be critical to anomaly detection. Further, the distribution of real-world log messages changes continuously due to the dynamic nature of the runtime environment and thus, a detection model conventionally trained based on the unrealistic i.i. d. assumption may not provide the expected and consistent performance. In this paper, we present a robust and transferable anomaly detection framework RT-Log to address the above problems. To perform a comprehensive analysis of log messages, we introduce an adaptive relation modeling technique, which captures feature interactions among log information fields selectively and dynamically for effective and interpretable log representations. To establish its robustness and transferability, we propose a general environment generalization technique for learning the environment invariant representations that can generalize across different runtime environments. We evaluate the anomaly detection performance of RT-Log on large real-world datasets. Extensive experimental results demonstrate that RT-Log consistently outperforms state-of-the-art methods by a significant margin under different settings.
Life cycle of Sahelian mesoscale convective cloud systems

This paper provides an eight‐year high‐resolution climatology of Sahelian mesoscale convective systems (MCSs) during the summer. MCSs are defined as convective cloud clusters larger than 5000 km2. They are extensively tracked from METEOSAT full‐resolution infrared images (time resolution 0.5 h and spatial resolution about 5 km). The method enables every MCS to be tracked throughout its entire lifetime. For each time step, the MCS location and its morphological and radiative characteristics are computed for three different brightness temperature thresholds. The methodology is presented, evaluated and compared with previous studies using low‐resolution data.
When Tree Meets Hash: Reducing Random Reads for Index Structures on Persistent Memories

Indexing structures are widely used in modern data-processing applications to support high-performance queries, and there are a variety of recent designs specifically optimized for the newly available persistent memory (PM). The primary focus of previous PM indexes is on reducing the expensive PM writes for persisting data. However, we find that in tree-based PM indexes, because of the smaller performance gap between writes and random reads on real PM devices, the read-intensive tree traversal phase dominates the overall latency. This observation calls for further optimizations on existing indexing structures for PM. In this paper, we propose Extendible Radix Tree (ERT), an efficient indexing structure for PM that significantly reduces tree heights to minimize random reads, while still maintaining fast in-node search speed. The key idea is to use extendible hashing for each node in a radix tree. This design allows us to have a relatively large fanout of the radix tree to keep the tree height small, and also to realize constant-time lookups within a node. Using extendible hashing also allows for incremental node modification without excessive writes during inserts and updates. Range queries are efficiently and robustly handled by enforcing partial ordering among the keys in the hash table of each node without introducing more hash collisions. Our experiments on both synthetic and real-world data sets demonstrate that ERT achieves up to 2.65×, 4.41×, and 2.43× speedups for search, insert, and range queries over the respectively state-of-the-art PM index.
Grouping Time Series for Efficient Columnar Storage

Columnar storage is now an industry standard design in most open-source or commercial time series database products, making them HTAP systems. The time column of a time series serves as the key for identifying the other value column, namely single-column storage scheme. When multiple time series share a similar set of timestamps, very likely in a module of multiple sensors, it is natural to group them together, i.e., one time column identifies multiple value columns in a single-group storage scheme. While multiple value columns sharing the same time column reduce the space cost of repeating timestamps, it may introduce extra space cost for recording null values. The reason is that time series may not be exactly aligned on each timestamp, owing to missing values, distinct data collection frequencies, unsynchronized clocks and so on. The columngroups storage scheme is thus to divide columns into multiple groups, within which the value columns share the same time column. Unfortunately, the problem of finding the optimal column groups for the minimum space cost is highly challenging, NP-hard according to our analysis. Thereby, we propose a heuristic algorithm for automatically grouping time series for efficient columnar storage. The column groups storage has been deployed in Apache IoTDB, an open-source time series database. The extensive performance analysis, over real-world data from our industrial partners, demonstrates that the proposed column groups achieve near optimal storage, more concise than the storage of single-column or single-group schemes. Interestingly, both the flushing and querying time costs of column groups are comparable to those of single-column or singlegroup, i.e., without incurring extra time cost.
Efficient Estimation of Pairwise Effective Resistance

Given an undirected graph G, the effective resistance r(s,t) measures the dissimilarity of node pair s,t in G, which finds numerous applications in real-world problems, such as recommender systems, combinatorial optimization, molecular chemistry, and electric power networks. Existing techniques towards pairwise effective resistance estimation either trade approximation guarantees for practical efficiency, or vice versa. In particular, the state-of-the-art solution is based on a multitude of Monte Carlo random walks, rendering it rather inefficient in practice, especially on large graphs. Motivated by this, this paper first presents an improved Monte Carlo approach, AMC, which reduces both the length and amount of random walks required without degrading the theoretical accuracy guarantee, through careful theoretical analysis and an adaptive sampling scheme. Further, we develop a greedy approach, GEER, which combines AMC with sparse matrix-vector multiplications in an optimized and non-trivial way. GEER offers significantly improved practical efficiency over AMC without compromising its asymptotic performance and accuracy guarantees. Extensive experiments on multiple benchmark datasets reveal that GEER is orders of magnitude faster than the state of the art in terms of computational time when achieving the same accuracy.