Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying Interpretation Reproducibility in Vision Transformer Models with TAVAC

The use of deep learning algorithms to extract meaningful diagnostic features from biomedical images holds the promise to improve patient care given the expansion of digital pathology. Among these deep learning models, Vision Transformer (ViT) models have been demonstrated to capture long-range spatial relationships with more robust prediction power for image classification tasks than regular convolutional neural network (CNN) models, and also better model interpretability. Model interpretation is important for understanding and elucidating how a deep learning model makes predictions, especially for developing transparent models for digital pathology. However, like other deep learning algorithms, with limited annotated biomedical imaging datasets, ViT models are prone to poor performance due to overfitting, which can lead to false predictions due to random noise. Overfitting affects model interpretation when predictions are made out of random noise. To address this issue, we introduce a novel metric – Training Attention and Validation Attention Consistency (TAVAC) – for evaluating ViT model degree of overfitting on imaging datasets and quantifying the reproducibility of interpretation. Specifically, the model interpretation is performed by comparing the high-attention regions in the image between training and testing. We test the method on four publicly available image classification datasets and two independent breast cancer histological image datasets. All overfitted models exhibited significantly lower TAVAC scores than the good-fit models. The TAVAC score quantitatively measures the level of generalization of model interpretation on a fine-grained level for small groups of cells in each H&E image, which cannot be provided by traditional performance evaluation metrics like prediction accuracy. Furthermore, the application of TAVAC extends beyond medical diagnostic AI models; it enhances the monitoring of model interpretative reproducibility at pixel-resolution in basic research, to reveal critical spatial patterns and cellular structures essential to understanding biological processes and disease mechanisms. TAVAC sets a new standard for evaluating the performance of deep learning model interpretation and provides a method for determining the significance of high-attention regions detected from the attention map of the biomedical images.
Alpha phase-coding supports feature binding during working memory maintenance

Working memory (WM) is the ability to retain and manipulate information in mind, which allows mnemonic representations to flexibly guide behavior. Successful WM requires that objects’ individual features are bound into cohesive representations, however the mechanisms supporting feature binding remain unclear. Binding errors (or swaps) provide a window into the intrinsic limits in capacity of WM. We tested the hypothesis that binding in WM is accomplished via neural phase synchrony and swaps result from its perturbations. Using magnetoencephalography data collected from human subjects, in a task designed to induce swaps, we showed that swaps are characterized by reduced phase-locked oscillatory activity during memory retention. We found that this reduction arises from increased phase-coding variability in the alpha-band, over a distributed network of sensorimotor areas. Our findings support the notion that feature binding in WM is accomplished through phase-coding dynamics that emerge from the competition between different memories.
Chemical Constituents of the Goat Margarine and Antibacterial Activity against Bacterial Pathogens in Sudan

The margarine constituents were examined by Gas Chromatography-Mass Spectrometry (GC-MS), it contained 41 compounds, fifteen of them were identified as the major compound, Hexadecanoic acid, methyl ester (22.39%), Methyl stearate (14.92%), methyl elaidate (13.80%), Methyl tetradecanoate (10.74%), Capric acid, methyl ester (8.34%) Lauric acid (4.52%) methyl octanoate (2.70%), linoleic acid methyl ester (2.57%), Methyl 11-octadecenoate (1.90%), methyl caproate (1.77%), Methyl pentadecanoate (1.72%), Methyl (8E,11E)-8,11-octadecadienoate (1.70%), Heptadecanoic acid, methyl ester, (1.46%),trans-13-Octadecenoic acid, methyl ester (Oleic acid) (1.20%), Methyl palmioleate (1.00%). The effect of goat margarine, against four different pathogenic bacteria Escherichia coli, Pseudomonas aeruginosa, Salmonella typhimurium, and Bacillus cereus, were carried out by using a disc diffusion technique, the highest antibacterial activity was detected against Salmonella typhimurium and the lowest one against Escherichia coli. The antibacterial activity of the antibiotics, (Ciprofloxacin, Tetracycline Ceftriaxone, Chloramphenicol and Gentamycin), were tested by the disc diffusion technique, and by measuring zones of inhibition, shows that there were differences, among all antibiotic the highest activity of antibiotic against bacteria was due to the action of ciprofloxacin. Ceftriaxone and Tetracycline antibiotic give lowest activity. Among the bacteria the highest inhibition zone by antibiotic against Salmonella typhimurium, and the lowest one against Escherichia coli
Exponential-Time Approximation Schemes via Compression

In this paper, we give a framework to design exponential-time approximation schemes for basic graph partitioning problems such as k -way cut , Multiway Cut , Steiner k -cut and Multicut , where the goal is to minimize the number of edges going across the parts. Our motivation to focus on approximation schemes for these problems comes from the fact that while it is possible to solve them exactly in 2 n n O (1) time (note that this is already faster than brute-forcing over all partitions or edge sets), it is not known whether one can do better. Using our framework, we design the first (1 + ϵ )-approximation algorithms for the above problems that run in time 2 f ( ϵ ) n (for f ( ϵ ) < 1) for all these problems. As part of our framework, we present two compression procedures. The first of these is a “lossless” procedure, which is inspired by the seminal randomized contraction algorithm for Global Min-cut of Karger [SODA ’93]. Here, we reduce the graph to an equivalent instance where the total number of edges is linearly bounded in the number of edges in an optimal solution of the original instance. Following this, we show how a careful combination of greedy choices and the best exact algorithm for the respective problems can exploit this structure and lead to our approximation schemes. Our first compression procedure bounds the number of edges linearly in the optimal solution, but this could still leave a dense graph as the solution size could be superlinear in the number of vertices. However, for several problems, it is known that they admit significantly faster algorithms on instances where solution size is linear in the number of vertices, in contrast to general instances. Hence, a natural question arises here. Could one reduce the solution size to linear in the number of vertices, at least in the case where we are willing to settle for a near-optimal solution, so that the aforementioned faster algorithms could be exploited? In the second compression procedure, using cut sparsifiers (this time, inspired by Benczúr and Karger [STOC ’96]) we introduce “solution linearization” as a methodology to give an approximation-preserving reduction to the regime where solution size is linear in the number of vertices for certain cut problems. Using this, we obtain the first polynomial-space approximation schemes faster than 2 n n O (1) for Minimum bisection and Edge Bipartization . Along the way, we also design the first polynomial-space exact algorithms for these problems that run in time faster than 2 n n O (1) , in the regime where solution size is linear in the number of vertices. The use of randomized contraction and cut sparsifiers in the exponential-time setting is novel to the best of our knowledge and forms our conceptual contribution.
Simple and Optimal Online Contention Resolution Schemes for k-Uniform Matroids

We provide a simple $(1-O(\frac{1}{\sqrt{k}}))$-selectable Online Contention Resolution Scheme for $k$-uniform matroids against a fixed-order adversary. If $A_i$ and $G_i$ denote the set of selected elements and the set of realized active elements among the first $i$ (respectively), our algorithm selects with probability $1-\frac{1}{\sqrt{k}}$ any active element $i$ such that $|A_{i-1}| + 1 \leq (1-\frac{1}{\sqrt{k}})\cdot \mathbb{E}[|G_i|]+\sqrt{k}$. This implies a $(1-O(\frac{1}{\sqrt{k}}))$ prophet inequality against fixed-order adversaries for $k$-uniform matroids that is considerably simpler than previous algorithms [Ala14, AKW14, JMZ22]. We also prove that no OCRS can be $(1-\Omega(\sqrt{\frac{\log k}{k}}))$-selectable for $k$-uniform matroids against an almighty adversary. This guarantee is matched by the (known) simple greedy algorithm that accepts every active element with probability $1-\Theta(\sqrt{\frac{\log k}{k}})$ [HKS07].
Relational Dynamics: The Flood Solution – Sustainable Energy and Rural Tourism

The effects of global warming are becoming apparent throughout the world. Europe has begun to experience more severe winters and increased rain (Steffen, 2011). Massive flooding in South Eastern Europe has devastated communities and repeatedly strains the economy of these regions resulting in mass trauma to the residents of multiple countries (Sito-Sucic & Djurica, 2014). Intergenerational effects of trauma (Bathory & Celik, 2014; Kaitz, Levy, Ebstein, Faraone, & Mankuta, 2009) have been noted to be an increasing world-wide concern. These traumatic effects are not only psychologically based but result in structural and functional changes within the brain and body (D. Bathory, 2012; D. S. Bathory, 2013a, 2013b; van der Kolk, Roth, Pelcovitz, Sunday, & Spinazzola, 2005). This chapter explores the application of decision making and Relational Dynamics to mass victims of floods by creating healing sites of sustainable energy and rural tourism to assist mass victims of natural disaster flooding.
Equivocal Blends: Prior Independent Lower Bounds

The prior independent framework for algorithm design considers how well an algorithm that does not know the distribution of its inputs approximates the expected performance of the optimal algorithm for this distribution. This paper gives a method that is agnostic to problem setting for proving lower bounds on the prior independent approximation factor of any algorithm. The method constructs a correlated distribution over inputs that can be described both as a distribution over i.i. d. good-for-algorithms distributions and as a distribution over i.i. d. bad-for-algorithms distributions. We call these two descriptions equivocal blends . Prior independent algorithms are upper-bounded by the optimal algorithm for the latter distribution even when the true distribution is the former. Thus, the ratio of the expected performances of the Bayesian optimal algorithms for these two decompositions is a lower bound on the prior independent approximation ratio. We apply this framework to give new lower bounds on canonical prior independent mechanism design problems. For one of these problems, we also exhibit a near-tight upper bound. Towards solutions for general problems, we give distinct descriptions of two large classes of correlated-distribution “solutions” for the technique, depending respectively on an order-statistic separability property and a paired inverse-distribution property. We exhibit that equivocal blends do not generally have a Blackwell ordering, which puts this paper outside of standard information design. 2012
Winter Braids Lecture Notes

A vanishing theorem for adjoint Reidemeister torsions Motivated by some observations in M-theory, Gang conjectures that for a hyperbolic 3-manifold, the inverse sum of the adjoint Reidemeister torsions over certain representations vanishes. In this talk, we prove the conjecture for the ﬁgure-eight knot and present numerical veriﬁcation for several knots. This is joint work with Dongmin Gang and Seonhwa Kim.
EXPLORING THE ROLE OF FORMANT FREQUENCIES IN THE CLASSIFICATION OF PHONATION TYPE

Phonation type has been found to interact with other acoustic characteristics of speech, including vowel formants. Here, we investigated the relation between phonation type and formants in three Swedish vowels, produced in three phonation types (neutral, breathy and pressed). Our findings show that the formant values change from neutral to breathy and pressed, respectively, but that this effect is more consistent for pressed than for breathy phonation. Moreover, while some systematic patterns can be identified, there is also substantial speaker variation in the size and in the direction of the change. We then employed the formant values, along with other acoustic features, to automatically classify phonation type. The addition of formant information to the classifier significantly improved the performance of the system.
MECHANISMS OF GENERALIZATION FOR PHONETIC LEARNING OF ACCENTED SPEECH

Listeners can rapidly adapt to an unfamiliar accent. Previous literature shows that such phonetic learning is often speaker-specific for speech sounds such as fricatives — it does not transfer to novel speakers, possibly because fricatives contain spectral properties that cue speaker identity. However, recent research shows that transfer to a novel sound contrast can occur within a single speaker, suggesting that there is room for analogous transfer of learning to a novel speaker with a sufficiently similar pronunciation. This study examines the generalization of phonetic learning for an atypical fricative pronunciation to novel speakers, focusing on the mechanisms that underlie such perceptual adaptation. Results from a set of experiments show that transfer of phonetic learning can occur to both novel speakers and novel phonetic contrasts, but that listeners may rely upon a more conservative mechanism when generalizing learning to novel speakers.
Assessment of quadrivalent characteristics influencing chromosome segregation by analyzing human preimplantation embryos from reciprocal translocation carriers

﻿Abstract Patterns of meiotic chromosome segregation were analyzed in cleavage stage and blastocyst stage human embryos from couples with autosomal reciprocal translocations (ART). The influence of quadrivalent asymmetry degree, the presence of terminal breakpoints, and the involvement of acrocentric chromosomes in the rearrangement were analyzed to evaluate their contribution to the formation of non-viable embryos with significant chromosomal imbalance due to pathological segregation patterns and to assess the selection of human embryos by the blastocyst stage. A selection of viable embryos resulting from alternate and adjacent-1 segregation and a significant reduction in the detection frequency of the 3 : 1 segregation pattern were observed in human embryos at the blastocyst stage. The presence of terminal breakpoints increased the frequency of 3 : 1 segregation and was also associated with better survival of human embryos resulting from adjacent-1 mode, reflecting the process of natural selection of viable embryos to the blastocyst stage. The demonstrated patterns of chromosome segregation and inheritance of a balanced karyotype in humans will contribute to optimizing the prediction of the outcomes of in vitro fertilization programs and assessing the risks of the formation of unbalanced embryos for ART carriers.
FAKTOR-FAKTOR YANG MEMPENGARUHI REPURCHASE INTENTION PENGGUNA PARACETAMOL DI JABODETABEK FACTORS AFFECTING REPURCHASE INTENTION OF PARACETAMOL USERS IN JABODETABEK

This study aims to investigate the relationship between brand heritage, brand trust, brand loyalty, repurchase intention, and brand strength in several paracetamol brands of PT. Oryza Medica Indonesia in the Greater Jakarta area, Indonesia. Data collection was carried out online with the population, namely users of the paracetamol brand PT. Oryza Medica Indonesia, of as many as 138 people. This type of quantitative research method uses SEM-Lisrel analysis. The results of the study show that brand heritage has a positive influence on brand trust. In addition, brand heritage also has a positive influence on brand strength. Furthermore, brand trust has a positive influence on repurchase intentions. However, brand trust does not have a positive effect on brand loyalty. Furthermore, brand trust has a positive influence on repurchase intentions. In addition, it was found that brand strength has a positive influence on brand loyalty. In the context of paracetamol users, this study emphasizes the importance of increasing consumer trust and loyalty to increase repurchase intention by providing insight into the importance of understanding and managing relationships in the context of brand investment by providing products and services that meet customer expectations to strengthen brand positions and maintain market share. strong market.
Effect of Hearing Loss on Prosodic Intervention in Parkinson’s Speech

This study tests the effect of hearing loss on prosody in people diagnosed with Parkinson’s disease (PD). Speech therapy for hypophonia in this population involves cueing speakers to increase their speech loudness. A complete audiological evaluation was completed on all participants that included pure tone hearing range, speech discrimination, comfortable loudness levels and dynamic range. Audiological testing was used to group the participants into two groups: mild to moderate and severe hearing loss. They were then asked to read a phonetically balanced sentence at four levels of self-perceived loudness (50%, 100% 200% and 400%). Changes in intensity and pitch range were compared for the two groups. The results indicate a negative impact of low dynamic hearing range on the participants’ perceptual scale for loudness which resulted in lower intensity and pitch range. Impact of these findings on current speech therapy is also discussed.
Bizkaiko neska gazteen kontsonante afrikatuen izari akustikoez

Abstract There have been two significant events in the near history of Bizkaian affricates: at a time, the three-unvoiced-affricate sound system preserved in other local varieties was reintroduced; but more recently, numerous informants do no longer distinguish two of the affricates. This research work examines the acoustic features of data produced by young Basque girls who differentiate the two affricates and those who utter just one in order to show the characteristic indicators of affricate sounds. Besides, the informants’ mother tongue, their individual traits and their capability to discriminate between both affricates shall be regarded as the variables of our analysis. Laburpena Bizkaiko euskararen afrikatuen historian une bi bereiz daitezke; bata gainerako euskalki batzuetan gorde diren hiru afrikatu ahoskabeak bira etorri zirenean, hau da, XVII. mendean, eta bestea, berrikiago, gaur egun informatzaile askoren artean gertatzen ari dena, hain zuzen ere, afrikatu bi horiek batera murriztea. Lan honetan, batetik, afrikatu biak eta bakarra darabiltzaten neska euskaldun gazte batzuen gauzapenen ezaugarri akustikoak aztertzen ditugu, afrikatuak ondoen ezaugarritzen dituzten indizeak agerian jartzeko. Bestetik, informatzaileen ama hizkuntza, norbanakoaren ezaugarriak eta afrikatu biak bereiztea edo ez gure analisiaren aldagaitzat hartuko ditugu.
LOW DENSITY ASSAMESE ALVEOLAR STOPS SHOW GREATER COARTICULATORY RESISTANCE COMPARED TO LABIALS AND VELARS

Coarticulatory Resistance (CR) in consonants is affected by density of segments in an inventory. Sparse consonantal systems allow consonants to coarticulate freely with adjoining vowels. Assamese, an Indo-Aryan (IA) language, contains alveolar stops that result from a historical merger between the dental and retroflex stops of the IA language family. Due to the low density of contrast in the coronal place of articulation (POA), we expect Assamese alveolar stops to exhibit greater acoustic variation. Slopes from first order F2 Locus Equations suggest that alveolars in a VCV context exhibit greater CR compared to velars and labials. In fact, slopes drawn between F2 at 25% and F2 at 5% of the vowel are also found to be significantly different between alveolars, labials and velars. We argue that consonantal systems seek articulatory equilibrium by selecting CR patterns that are governed by intrinsic articulatory complexity and motor demands, not just density.
Efﬁcient Transform Algorithms for Parallel Ultra-Low-Power IoT End Nodes

—Modern IoT end nodes must support computational intensive workloads at a limited power-budget. Parallel ultra-low-power architectures are a promising target for this scenario, and the availability of highly optimized software libraries is crucial to exploit parallelism and reduce software development costs. This letter proposes an efﬁcient parallel design of the widely used STFT and DWT transforms targeting ultra-low-power IoT devices. We address key performance challenges related to ﬁne-grained synchronization and banking conﬂicts in shared memory. We achieve high throughput (50.95 samples/ µ s, on average), good parallel speedup (up to 6.79 × ), and high energy efﬁciency (up to 172.55 GOp/s/W) on a cluster of 8 RISC-V cores optimized for parallel ultra-low-power (PULP) operation.
SENTIMENT ANALYSIS FRAMEWORK FOR TELUGU TEXT BASED ON NOVEL CONTRIVED PASSIVE AGGRESSIVE WITH FUZZY WEIGHTING CLASSIFIER (CPSC-FWC)

Natural language processing (NLP) is a subset of artificial intelligence demonstrating how algorithms can interact with individuals in their unique languages. In addition, sentiment analysis in NLP is better in numerous programs, including evaluating sentiment in Telugu. Several unsupervised machine-learning algorithms, such as k-means clustering with cuckoo search, are used to detect Telugu text. However, these techniques struggle to cluster data with variable cluster sizes and densities, slow search speeds, and poor convergence accuracy. This study developed a unique ML-based sentiment analysis system for Telugu text to address the shortcomings. Initially, in the pre-processing stage, the proposed Linear Pursuit Algorithm (LPA) removes words in white spaces, punctuation, and stops. Then, for POS tagging, this research proposed a Conditional Random Field with Lexicon weighting; following that, a Contrived Passive Aggressive with Fuzzy Weighting Classifier (CPSC-FWC) is proposed to classify the sentiments in Telugu text. Consequently, the method we propose produces efficient outcomes in terms of accuracy, precision, recall, and f1-score.
East African Journal of Information Technology

Human-computer interaction is paramount in the process of designing interactive-computer systems with high capabilities of guaranteeing users’ satisfaction. However, lack of suitable frameworks complicates their effective design. This study investigated suitable frameworks capable of guiding appropriate design of interactive systems for communication through activities (ISCA) in higher learning institutions in Tanzania. These systems are necessary for augmenting communication thereby overcoming persistent challenges related to their reliance on face-to-face, phone-based, and social networking websites communications. This study used a thorough user centred design approach where a framework for human activity design-centred (HADC) was employed. Authors qualitatively analysed data on communication and interaction issues from participants involved. Design science research was then combined with activity theory to develop a four-phased methodology which was used to design an