Miracle-dz/UniFAR-FTUs huggingface dataset:
Production and Comprehension of Prosodic Boundary Marking in Persons With Unilateral Brain Lesions.

PURPOSE Persons with unilateral brain damage in the right hemisphere (RH) or left hemisphere (LH) show limitations in processing linguistic prosody, with yet inconclusive results on their ability to process prosodically marked structural boundaries for syntactic ambiguity resolution. We aimed at systematically investigating production and comprehension of three prosodic cues (f 0 range, final lengthening, and pause) at structural boundaries in coordinate sequences in participants with right hemisphere brain damage (RHDP) and participants with left hemisphere brain damage (LHDP). METHOD Twenty RHDP and 15 LHDP participated in our study. Comprehension experiment: Participants and a control group listened to coordinate name sequences with internal grouping by a prosodically marked structural boundary (grouped condition, e.g., "(Gabi und Leni) # und Nina") or without internal grouping (ungrouped condition, e.g., "Gabi und Leni und Nina") and had to identify the target condition. The strength and combinations of prosodic cues in the stimuli were manipulated. Production experiment: Participants were asked to produce coordinate sequences in the two conditions (grouped, ungrouped) in two different tasks: a Reading Aloud and a Repetition experiment. Accuracy of participants' productions was subsequently assessed in a rating study and productions were analyzed with respect to use of prosodic cues. RESULTS In the Comprehension experiment, RHDP and LHDP had overall lower identification accuracies than unimpaired control participants and LHDP were found to have particular problems with boundary identification when the pause cue was reduced. In production, LHDP and RHDP employed all three prosodic cues for boundary marking, but struggled to clearly mark prosodic boundaries in 28% of all productions. Both groups showed better performance in reading aloud than in repetition. LHDP relied more on using f 0 range and pause duration to prosodically mark structural boundaries, whereas RHDP employed final lengthening more vigorously than LHDP in reading aloud. CONCLUSIONS We conclude that processing of linguistic prosody is affected in RHDP and LHDP, but not completely impaired. Therefore, prosody can serve as a relevant communicative resource. However, it should also be considered as a target area for assessment and treatment in both groups. SUPPLEMENTAL MATERIAL https://doi.org/10.23641/asha.21637505.
Sensitivity loss of real-time DFT-based frequency excision with direct sequence spread-spectrum communication

The capacity of direct sequence spread-spectrum modulation to reject narrow-band interference can be significantly improved by eliminating narrowband energy at the receiver (frequency excision) using algorithms that operate on the real time discrete Fourier transform of the received signal (RT-DFT-Based). These algorithms have the potential to adapt very quickly to a change in interference spectrum and eliminate multiple interferers, but to do this the decision of which frequency bins to excise must be made based on a very short observation time. Under these circumstances, the number of bins excised can be much larger than the number of bins containing narrow-band interference. The receive signal strength loss due to "over-excision" can be very significant and limits receive sensitivity. This paper shows theoretical over-excision losses of several heuristic algorithms. The sensitivity loss due to time weighting (or windowing) is presented for variable overlap and several different windows. These theoretical results are confirmed with simulation results and can be used to project sensitivity of PN spread spectrum systems that are located in a band that is also used by narrow-band systems. <<ETX>>
Learning Sequence Descriptor Based on Spatio-Temporal Attention for Visual Place Recognition

Visual Place Recognition (VPR) aims to retrieve frames from a geotagged database that are located at the same place as the query frame. To improve the robustness of VPR in perceptually aliasing scenarios, sequence-based VPR methods are proposed. These methods are either based on matching between frame sequences or extracting sequence descriptors for direct retrieval. However, the former is usually based on the assumption of constant velocity, which is difficult to hold in practice, and is computationally expensive and subject to sequence length. Although the latter overcomes these problems, existing sequence descriptors are constructed by aggregating features of multiple frames only, without interaction on temporal information, and thus cannot obtain descriptors with spatio-temporal discrimination. In this letter, we propose a sequence descriptor that effectively incorporates spatio-temporal information. Specifically, spatial attention within the same frame is utilized to learn spatial feature patterns, while attention in corresponding local regions of different frames is utilized to learn the persistence or change of features over time. We use a sliding window to control the temporal range of attention and use relative positional encoding to construct sequential relationships between different features. This allows our descriptors to capture the intrinsic dynamics in a sequence of frames. Comprehensive experiments on challenging benchmark datasets show that the proposed approach outperforms recent state-of-the-art methods.
IMPROVING LONG ACTING AND REVERSIBLE CONTRACEPTIVES UPTAKE USING QUALITY IMPROVEMENT STRATEGY AT A SUB-COUNTY HOSPITAL KENYA.

Beatrice Mukabana, Nancy Khayeli and Dr. W. Adegu. 1. School of Nursing and Midwifery, Masinde Muliro University. 2. Iguhu County Hospital. 3. Kakamega County Referal Hospital. Manuscript Info Abstract . Manuscript History
A mixed-signal oscillatory neural network for scalable analog computations in phase domain

Digital electronics based on von Neumann’s architecture is reaching its limits to solve large-scale problems essentially due to the memory fetching. Instead, recent efforts to bring the memory near the computation have enabled highly parallel computations at low energy costs. Oscillatory neural network (ONN) is one example of in-memory analog computing paradigm consisting of coupled oscillating neurons. When implemented in hardware, ONNs naturally perform gradient descent of an energy landscape which makes them particularly suited for solving optimization problems. Although the ONN computational capability and its link with the Ising model are known for decades, implementing a large-scale ONN remains difficult. Beyond the oscillators’ variations, there are still design challenges such as having compact, programmable synapses and a modular architecture for solving large problem instances. In this paper, we propose a mixed-signal architecture named Saturated Kuramoto ONN (SKONN) that leverages both analog and digital domains for efficient ONN hardware implementation. SKONN computes in the analog phase domain while propagating the information digitally to facilitate scaling up the ONN size. SKONN’s separation between computation and propagation enhances the robustness and enables a feed-forward phase propagation that is showcased for the first time. Moreover, the SKONN architecture leads to unique binarizing dynamics that are particularly suitable for solving NP-hard combinatorial optimization problems such as finding the weighted Max-cut of a graph. We find that SKONN’s accuracy is as good as the Goemans–Williamson 0.878-approximation algorithm for Max-cut; whereas SKONN’s computation time only grows logarithmically. We report on Weighted Max-cut experiments using a 9-neuron SKONN proof-of-concept on a printed circuit board (PCB). Finally, we present a low-power 16-neuron SKONN integrated circuit and illustrate SKONN’s feed-forward ability while computing the XOR function.
Island morphology, reef resources, and development paths in the Maldives

Maldivian paths for economic development have historically been constrained by the morphology of atoll islands and the availability of material resources. These constraints are most evident when examining the development of Male’, the Maldives’ capital and most populous island. Before the 1970s, Male’ was a rather typical atoll island, consisting of accumulated rubble and sand with an underlying lagoonal reef (faro) structure. Rising population and standard-of-living expectations in Male’ led to accelerated coral mining of Male’s reefs in the 1970s and 80s for both landfill and construction material, extending the island’s land surface across Male’s lagoon and reef flats, close to the edge of its underlying faro. This combination of mining and fill degraded the island’s natural defenses against wave events, resulting in disastrous floods in April 1987 and the fortification of the coastline with seawalls shortly thereafter. The degree of degradation to natural defenses and amount of investment in urban building stock have jointly locked Male’ into a “hard path” for coastal resilience engineering, and both the damage patterns and response to the 2004 Indian Ocean tsunami demonstrate that the Male’ model of development and degradation has spread to smaller Maldivian islands. While hard measures have proven successful in preventing further damage, their expense has led to greater interest in “soft path,” ecosystem-based resilience measures. The degree of local ecosystem damage, combined with high vulnerability to climate change and Male’s continued growth, means that such measures can only be seen as supplements to heavier fortification in the future, including raised (Th. Vilufushi) or artificial islands (Hulhumale’). The intersecting role hydrological stress on Maldivian groundwater has played in Male’s development path is also discussed.
Variation of Radon Levels in U.S. Homes with Various Factors

A program combining 70,000 purchased and 3500 random selection-no charge measurements with extensive questionnaires is described. Methods for reducing biases in purchased measurements have been reasonably successful. Distributions are roughly lognormal with sigma/sub g/ approx. = 3.0 but with an excess of very high measurements. Winter and spring-fall measurements average about 60 percent and 40 percent, respectively, higher than summer measurements. Basements average 2.5 times higher radon levels than upper floors. Tightening of homes since 1974 has increased radon levels by less than 10 percent, but well-weatherized houses have approx. 40 percent higher levels than poorly weatherized houses. Open windows reduce levels by a factor of about 2.5. Low-cost newer houses have much lower levels than more expensive newer houses, but among older houses this is reversed. Low income families have substantially lower radon levels than average. There is little variation with education level. Nonsmokers have higher radon levels than smokers, but this seems to apply mainly to living rooms and dining rooms. Suburban houses have higher levels than urban, and rural houses have still higher levels. Owner-occupied houses have much higher levels than rented houses. Variations of radon levels with weatherization do not seem to be due to costmore » factors. A strategy for locating areas with high mean radon levels is described. « less
Knowledge Fusion of Large Language Models

While training large language models (LLMs) from scratch can generate models with distinct functionalities and strengths, it comes at significant costs and may result in redundant capabilities. Alternatively, a cost-effective and compelling approach is to merge existing pre-trained LLMs into a more potent model. However, due to the varying architectures of these LLMs, directly blending their weights is impractical. In this paper, we introduce the notion of knowledge fusion for LLMs, aimed at combining the capabilities of existing LLMs and transferring them into a single LLM. By leveraging the generative distributions of source LLMs, we externalize their collective knowledge and unique strengths, thereby potentially elevating the capabilities of the target model beyond those of any individual source LLM. We validate our approach using three popular LLMs with different architectures--Llama-2, MPT, and OpenLLaMA--across various benchmarks and tasks. Our findings confirm that the fusion of LLMs can improve the performance of the target model across a range of capabilities such as reasoning, commonsense, and code generation. Our code, model weights, and data are public at \url{https://github.com/fanqiwan/FuseLLM}.
Dynamic matrix factorization-based collaborative filtering in movie recommendation services

Movies are a primary source of entertainment, but finding specific content can be challenging given the exponentially increasing number of movies produced each year. Recommendation systems are extremely useful for solving this problem. While various approaches exist, Collaborative Filtering (CF) is the most straightforward. CF leverages user input and historical preferences to determine user similarity and suggest movies. Matrix Factorization (MF) is one of the most popular Collaborative Filtering (CF) techniques. It maps users and items into a joint latent space, using a vector of latent features to represent each user or item. However, traditional MF techniques are static, while user cognition and product variety are constantly evolving. As a result, traditional MF approaches struggle to accommodate the dynamic nature of user-item interactions. To address this challenge, we propose a Dynamic Matrix Factorization CF model for movie recommendation systems (DMF-CF) that considers the dynamic changes in user interactions. To validate our approach, we conducted evaluations using the standard MovieLens dataset and compared it to state-of-the-art models. Our preliminary findings highlight the substantial benefits of DMF-CF, which outperforms recent models on the MovieLens-100K and MovieLens-1M datasets in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics.
A Primer on Learning in Bayesian Networks for Computational Biology

Bayesian networks (BNs) provide a neat and compact representation for expressing joint probability distributions (JPDs) and for inference. They are becoming increasingly important in the biological sciences for the tasks of inferring cellular networks [1], modelling protein signalling pathways [2], systems biology, data integration [3], classification [4], and genetic data analysis [5]. The representation and use of probability theory makes BNs suitable for combining domain knowledge and data, expressing causal relationships, avoiding overfitting a model to training data, and learning from incomplete datasets. The probabilistic formalism provides a natural treatment for the stochastic nature of biological systems and measurements. This primer aims to introduce BNs to the computational biologist, focusing on the concepts behind methods for learning the parameters and structure of models, at a time when they are becoming the machine learning method of choice. There are many applications in biology where we wish to classify data; for example, gene function prediction. To solve such problems, a set of rules are required that can be used for prediction, but often such knowledge is unavailable, or in practice there turn out to be many exceptions to the rules or so many rules that this approach produces poor results. Machine learning approaches often produce better results, where a large number of examples (the training set) is used to adapt the parameters of a model that can then be used for performing predictions or classifications on data. There are many different types of models that may be required and many different approaches to training the models, each with its pros and cons. An excellent overview of the topic can be found in [6] and [7]. Neural networks, for example, are often able to learn a model from training data, but it is often difficult to extract information about the model, which with other methods can provide valuable insights into the data or problem being solved. A common problem in machine learning is overfitting, where the learned model is too complex and generalises poorly to unseen data. Increasing the size of the training dataset may reduce this; however, this assumes more training data is readily available, which is often not the case. In addition, often it is important to determine the uncertainty in the learned model parameters or even in the choice of model. This primer focuses on the use of BNs, which offer a solution to these issues. The use of Bayesian probability theory provides mechanisms for describing uncertainty and for adapting the number of parameters to the size of the data. Using a graphical representation provides a simple way to visualise the structure of a model. Inspection of models can provide valuable insights into the properties of the data and allow new models to be produced.
HomeRules: A Tangible End-User Programming Interface for Smart Homes

A considerable amount of research has been carried out towards enabling average users to customize their smart homes through trigger-action ("if. then.") programming. However, inhabitants of such smart environments keep having problems understanding, administering, troubleshooting, and deriving benefits from the technologies employed in their homes. By synthesizing a broad body of research on end-user programming in smart homes with observations of commercial products and our own experiences, we provide a set of guidelines for designers of future interfaces and tools. Stemming from them, we present the design and the initial evaluation of HomeRules, a mobile and tangible application for end-user programming in smart homes.
Speedup Learning for Repair-based Search by Identifying Redundant Steps

Repair-based search algorithms start with an initial solution and attempt to improve it by iteratively applying repair operators. Such algorithms can often handle large-scale problems that may be difficult for systematic search algorithms. Nevertheless, the computational cost of solving such problems is still very high. We observed that many of the repair steps applied by such algorithms are redundant in the sense that they do not eventually contribute to finding a solution. Such redundant steps are particularly harmful in repair-based search, where each step carries high cost due to the very high branching factor typically associated with it. Accurately identifying and avoiding such redundant steps would result in faster local search without harming the algorithm's problem-solving ability. In this paper we propose a speedup learning methodology for attaining this goal. It consists of the following steps: defining the concept of a redundant step; acquiring this concept during off-line learning by analyzing solution paths for training problems, tagging all the steps along the paths according to the redundancy definition and using an induction algorithm to infer a classifier based on the tagged examples; and using the acquired classifier to filter out redundant steps while solving unseen problems. Our algorithm was empirically tested on instances of real-world employee timetabling problems (ETP). The problem solver to be improved is based on one of the best methods for solving some large ETP instances. Our results show a significant improvement in speed for test problems that are similar to the given example problems.
Rotations of Periodic Strings and Short Superstrings

This paper presents two simple approximation algorithms for the shortest superstring problem with approximation ratios 223 (? 2.67) and22542(? 2.596). The framework of our improved algorithms is similar to that of previous algorithms in the sense that they construct a superstring by computing some optimal cycle covers on the distance graph of the given strings and then break and merge the cycles to finally obtain a Hamiltonian path, but we make use of new bounds on the overlap between two strings. We prove that for each periodic semiinfinite string ? =a1a2··· of periodq, there exists an integerk, such that forany(finite) stringsof periodpwhich isinequivalentto ? , the overlap betweensand therotation? k=akak+1··· is at mostp+12q. Moreover, ifp? q, then the overlap betweensand ? k is not larger than 23(p+q). The bounds are tight. In the previous shortest superstring algorithmsp+qwas used as the standard (tight) bound on overlap between two strings with periodspandq.
A flexible gaze tracking algorithm evaluation workbench

The development of gaze tracking algorithms is very much bound to the specific setup and properties of the respective system they are used in. This makes it hard e. g. to compare their performance. We propose Gazelnut, a modular system to ease the development and comparison of gaze tracking algorithms, which also makes it independent from the permanent access to specific hardware. Building on the message passing architecture of the "robot operating system" (ROS) the system provides a flexible base to record and replay sessions, record the input from multiple cameras, run exchangeable algorithms on such sessions, store their individual results on the recorded (or live) scene, run different algorithms in parallel to compare their results and attach additional diagnostic modules to the running system.
Efficient implementation of multiple on-chip signature checking

Detection latency in a BIST scheme is the delay between the time instant at which a faulty response appears and the time instant at which the fault is detected. Conventional BILBO-BIST schemes suffer from long detection latency since it is not until the signatures are scanned out and compared off-chip that a fault become apparent. Aliasing, which is a fallout of long detection latency, is a serious problem. We have proposed an improved BIST architecture which supports on-chip comparison of multiple signatures to minimize the probability of aliasing and total test time. Also we quantified the aliasing probability of the "Multiple On-chip Signature Comparison scheme" (MOSC) scheme proposed. In this paper, we describe an efficient implementation of the MOSC test architecture and report results on several benchmark circuits. We describe different optimization methods to reduce the overall test control area.
Learning to re-rank: query-dependent image re-ranking using click data

Our objective is to improve the performance of keyword based image search engines by re-ranking their original results. To this end, we address three limitations of existing search engines in this paper. First, there is no straight-forward, fully automated way of going from textual queries to visual features. Image search engines therefore primarily rely on static and textual features for ranking. Visual features are mainly used for secondary tasks such as finding similar images. Second, image rankers are trained on query-image pairs labeled with relevance judgments determined by human experts. Such labels are well known to be noisy due to various factors including ambiguous queries, unknown user intent and subjectivity in human judgments. This leads to learning a sub-optimal ranker. Finally, a static ranker is typically built to handle disparate user queries. The ranker is therefore unable to adapt its parameters to suit the query at hand which again leads to sub-optimal results. We demonstrate that all of these problems can be mitigated by employing a re-ranking algorithm that leverages aggregate user click data. We hypothesize that images clicked in response to a query are mostly relevant to the query. We therefore re-rank the original search results so as to promote images that are likely to be clicked to the top of the ranked list. Our re-ranking algorithm employs Gaussian Process regression to predict the normalized click count for each image, and combines it with the original ranking score. Our approach is shown to significantly boost the performance of the Bing image search engine on a wide range of tail queries.