Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Efficacy of Inhaled Technosphere Insulin (Afrezza) by Postmeal Dosing: In-silico Clinical Trial with the University of Virginia/Padova Type 1 Diabetes Simulator

Abstract Background: Technosphere® insulin (TI), an inhaled human insulin with a fast onset of action, provides a novel option for the control of prandial glucose. We used the University of Virginia (UVA)/Padova simulator to explore in-silico the potential benefit of different dosing regimens on postprandial glucose (PPG) control to support the design of further clinical trials. Tested dosing regimens included at-meal or postmeal dosing, or dosing before and after a meal (split dosing). Methods: Various dosing regimens of TI were compared among one another and to insulin lispro in 100 virtual type-1 patients. Individual doses were identified for each regimen following different titration rules. The resulting postprandial glucose profiles were analyzed to quantify efficacy and the risk for hypoglycemic events. Results: This approach allowed us to assess the benefit/risk for each TI dosing regimen and to compare results with simulations of insulin lispro. We identified a new titration rule for TI that could significantly improve the efficacy of treatment with TI. Conclusion: In-silico clinical trials comparing the treatment effect of different dosing regimens with TI and of insulin lispro suggest that postmeal dosing or split dosing of TI, in combination with an appropriate titration rule, can achieve a superior postprandial glucose control while providing a lower risk for hypoglycemic events than conventional treatment with subcutaneously administered rapid-acting insulin products.
Performance Gains and Drawbacks using Set Associative Cache

Abstract This paper explains why there are performance drawbacks for dense matrix multiplication algorithm using multilevel associative cache memory. We give an overview of cache memory organization and theoretical analysis why performance drawback appears in dense matrix multiplication algorithm. We also provide a method to avoid situations where this performance drawback is significant and how to achieve maximum performance. The analyzed problem appears in storage of matrix columns and inefficient usage of cache where the matrix will always map onto a small group of same cache sets and initiate a significant number of cache misses. In this case it looks like the processor is using only a small group of cache sets instead of complete number of sets in associative memory where maximum performance can be achieved.
Certain Generalizations on the Collision Channel without Feedback

In this paper, certain generalizations on the collision channel without feedback are presented, based on the original work of Massey and Mathys. We are concerned with situations in which, given a collision in a slot, the channel capacity is a non-zero, decreasing function of the number of users involved. This corresponds, for example, to spread-spectrum type of signaling. Due to this model, concatenated coding schemes are employed to efficiently exploit the time-varying nature of the channel, upon using reliability information from the inner code decoding process, at the outer code decoding process. Results concerning capacity and coding/decoding tradeoffs will be presented.
Statistical Entropy of a Schwarzschild-anti-de Sitter Black Hole

We calculate the intrinsic entropy of a Schwarzschild black hole in an asymptotically antide Sitter space. The statistical calculation of the entropy is based on a model for particle structure that leads to confinement. The constituents of the particle are distinguishable quasiparticles. The entropy (temperature) is less (greater) than the entropy of a Schwarzschild black hole in an asymptotically flat space. The equilibrium thermodynamic states are described by pure states, myriotic fields, and the distinguishability of the internal microstates may provide a solution to the black hole information paradox by suggesting a Bose-Einstein condensate whereby the zero mass state is a limit point (or accumulation point) of condensates on the event horizon.
Smart Cities: The Hopes and Hypes

Smart cities are being planned for several advanced applications and services for the inhabitants. Smart cities initiative promise many new services which are not possible in the traditional city frameworks. In the smart city framework, the basic aim is to provide all the essential services through sensor based systems which does not need much human intervention. This system is designed to operate on its own in a self-organizing manner. Therefore, the hopes are really big from the smart cities to enhance the quality of lives and the economy. However, some of the promises in the smart cities are very much over hyped. In this article, we analyse the realities of the smart cities and their practical significances based on the technological aspects of these projects. We also address the false promises that are around which are just the hypes. We clarify these hypes with appropriate logical explanations.
Towards multiobjective optimization and control of smart grids

The rapid uptake of renewable energy sources in the electricity grid leads to a demand in load shaping and flexibility. Energy storage devices such as batteries are a key element to provide solutions to these tasks. However, typically, a trade‐off between the performance‐related goal of load shaping and the objective of having flexibility in store for auxiliary services, which is, for example, linked to robustness and resilience of the grid, can be observed. We propose to make use of the concept of Pareto optimality in order to resolve this issue in a multiobjective framework. In particular, we analyze the Pareto frontier and quantify the trade‐off between the nonaligned objectives to properly balance them.
Compositional Inference Metaprogramming with Convergence Guarantees

Inference metaprogramming enables effective probabilistic programming by supporting the decomposition of executions of probabilistic programs into subproblems and the deployment of hybrid probabilistic inference algorithms that apply different probabilistic inference algorithms to different subproblems. We introduce the concept of independent subproblem inference (as opposed to entangled subproblem inference in which the subproblem inference algorithm operates over the full program trace) and present a mathematical framework for studying convergence properties of hybrid inference algorithms that apply different Markov-Chain Monte Carlo algorithms to different parts of the inference problem. We then use this formalism to prove asymptotic convergence results for probablistic programs with inference metaprogramming. To the best of our knowledge this is the first asymptotic convergence result for hybrid probabilistic inference algorithms defined by (subproblem-based) inference metaprogramming.
Toeplitz Inverse Covariance based Robust Speaker Clustering for Naturalistic Audio Streams

Speaker diarization determines who spoke and when? in an audio stream. In this study, we propose a model-based approach for robust speaker clustering using i-vectors. The ivectors extracted from different segments of same speaker are correlated. We model this correlation with a Markov Random Field (MRF) network. Leveraging the advancements in MRF modeling, we used Toeplitz Inverse Covariance (TIC) matrix to represent the MRF correlation network for each speaker. This approaches captures the sequential structure of i-vectors (or equivalent speaker turns) belonging to same speaker in an audio stream. A variant of standard Expectation Maximization (EM) algorithm is adopted for deriving closed-form solution using dynamic programming (DP) and the alternating direction method of multiplier (ADMM). Our diarization system has four steps: (1) ground-truth segmentation; (2) i-vector extraction; (3) post-processing (mean subtraction, principal component analysis, and length-normalization) ; and (4) proposed speaker clustering. We employ cosine K-means and movMF speaker clustering as baseline approaches. Our evaluation data is derived from: (i) CRSS-PLTL corpus, and (ii) two meetings subset of the AMI corpus. Relative reduction in diarization error rate (DER) for CRSS-PLTL corpus is 43.22% using the proposed advancements as compared to baseline. For AMI meetings IS1000a and IS1003b, relative DER reduction is 29.37% and 9.21%, respectively.
Dynamics of Multiple Bodies in a Corotation Resonance: Conserved Quantities and Relevance to Ring Arcs

The interactions among objects in a mean motion resonance are important for the orbital evolution of satellites and rings, especially Saturn’s ring arcs and associated moons. In this work, we examine interactions among massive bodies in the same corotation eccentricity resonance site that affect the orbital evolution of those bodies using numerical simulations. During these simulations, the bodies exchange angular momentum and energy during close encounters, altering their orbits. This energy exchange, however, does not mean that one body necessarily moves closer to exact corotation when the other moves away from it. Indeed, if one object moves toward one of these sites, the other object is equally likely to move toward or away from it. This happens because the timescale of these close encounters is short compared to the synodic period between these particles and the secondary mass (i.e., the timescale where corotation sites can be treated as potential maxima). Because the timescale of a gravitational encounter is comparable to the timescale of a collision, we could expect energy to be exchanged in a similar way for collisional interactions. In that case, these findings could be relevant for denser systems like the arcs in Neptune’s Adams ring and how they can be maintained in the face of frequent inelastic collisions.
Slovenian large vocabulary speech recognition with data-driven models of inflectional morphology

The paper describes experiments in large vocabulary speech recognition of the highly inflective Slovenian language. The main problem of an inflective language is its high OOV (out-of-vocabulary) rate. To achieve a usable OOV rate, smaller modeling units (namely stems and endings) are used instead of words. Word decompositions are based on data-driven methods. Experiments with different-sized vocabularies were performed to show the effects of data sparsity and acoustic confusability. The most remarkable improvement is obtained with a vocabulary of 20,000 units. We compare subword-based models with word-based models. All results are computed on word level. The best results are obtained with subword trigram language models. They improve recognition for 7.5%. By using larger vocabularies, the results are not improved. The problems of acoustic confusability of subword units becomes evident. Also, the statistics of some modelling units are poorly estimated due to their low frequency of occurrence.
Grounding Value Alignment with Ethical Principles

An important step in the development of value alignment (VA) systems in AI is understanding how values can interrelate with facts. Designers of future VA systems will need to utilize a hybrid approach in which ethical reasoning and empirical observation interrelate successfully in machine behavior. In this article we identify two problems about this interrelation that have been overlooked by AI discussants and designers. The first problem is that many AI designers commit inadvertently a version of what has been called by moral philosophers the "naturalistic fallacy," that is, they attempt to derive an "ought" from an "is. " We illustrate when and why this occurs. The second problem is that AI designers adopt training routines that fail fully to simulate human ethical reasoning in the integration of ethical principles and facts. Using concepts of quantified modal logic, we proceed to offer an approach that promises to simulate ethical reasoning in humans by connecting ethical principles on the one hand and propositions about states of affairs on the other.
Haptics for scientific visualisation

Visualisations of numerical data used in the fields of maths and science are not easily accessible to visually impaired students. This paper presents the development of a multimodal system that can present graphical data in real-time. Haptic and auditory interfaces are used to enable graphs to be perceived through the senses of touch and sound. Details of the experimental procedure used to evaluate the system are given, including a comparison between different haptic devices. The results show that the system can be used to quickly and accurately obtain information from a graph in comparison with existing techniques, such as swell paper. It has been demonstrated that both haptic devices tested can be successfully used with this system, with minimal difference in performance.
Design and analysis of an anti-slip fuzzy controller for heavy duty off road vehicles

This paper illustrates the design and analysis of a distributed fuzzy control system to prevent wheel slippage in heavy-duty off-road vehicles with hydrostatic power transmission using the vector space analytical approach. The controller described have been fully implemented in a commercially available city tractor and tested on a test track as well as under real working conditions. <<ETX>>
Towards Optimal Moment Estimation in Streaming and Distributed Models

One of the oldest problems in the data stream model is to approximate the pth moment \(\Vert \mathbf {X}\Vert _p^p = \sum _{i=1}^n \mathbf {X}_i^p\) of an underlying non-negative vector \(\mathbf {X}\in \mathbb {R}^n\) , which is presented as a sequence of \(\mathrm{poly}(n)\) updates to its coordinates. Of particular interest is when \(p \in (0,2]\) . Although a tight space bound of \(\Theta (\epsilon ^{-2} \log n)\) bits is known for this problem when both positive and negative updates are allowed, surprisingly, there is still a gap in the space complexity of this problem when all updates are positive. Specifically, the upper bound is \(O(\epsilon ^{-2} \log n)\) bits, while the lower bound is only \(\Omega (\epsilon ^{-2} + \log n)\) bits. Recently, an upper bound of \(\tilde{O}(\epsilon ^{-2} + \log n)\) bits was obtained under the assumption that the updates arrive in a random order. We show that for \(p \in (0, 1]\) , the random order assumption is not needed. Namely, we give an upper bound for worst-case streams of \(\tilde{O}(\epsilon ^{-2} + \log n)\) bits for estimating \(\Vert \mathbf {X}\Vert _p^p\) . Our techniques also give new upper bounds for estimating the empirical entropy in a stream. However, we show that for \(p \in (1,2]\) , in the natural coordinator and blackboard distributed communication topologies, there is an \(\tilde{O}(\epsilon ^{-2})\) bit max-communication upper bound based on a randomized rounding scheme. Our protocols also give rise to protocols for heavy hitters and approximate matrix product. We generalize our results to arbitrary communication topologies G, obtaining an \(\tilde{O}(\epsilon ^{2} \log d)\) max-communication upper bound, where d is the diameter of G. Interestingly, our upper bound rules out natural communication complexity-based approaches for proving an \(\Omega (\epsilon ^{-2} \log n)\) bit lower bound for \(p \in (1,2]\) for streaming algorithms. In particular, any such lower bound must come from a topology with large diameter.
Quality Aware Service Oriented Ontology Based Data Integration

Integration of multiple, distributed, and heterogeneous sources are essential for scientific and commercial domains. Ensuring data quality in data integration is an issue or challenge because of their varying quality levels. The existing data integration methodologies do not assure quality of data as it is difficult to assess. The Meta data of data sources do not provide quality details and it is difficult to choose best query plan. It is also difficult to predict the resultant data quality before integration. To mitigate above issues, this paper proposes Service Oriented Data Integration with Quality of Service (SODI-QoS) architecture. The SODI-QoS architecture has wrapper mediator layer, which consists of semantic conflict resolution layer and Quality of Service (QoS) layer. Semantic conflict resolution layer uses ontology to create local and global schema to resolve semantic conflicts. The QoS layer detects and resolves the incompleteness and inaccuracy of resultant data of data sources. The proposed architecture provides high-quality data results to the end-user and notification about the incompleteness and inaccuracy of the source is communicated to respective data sources. The E-shopping application has been proposed to analyze the performance of the SODI-QoS architecture. Experimental results illustrates that the accuracy and precision of SODI-QoS architecture has been improved by 12% 14% respectively than the ontology based data integration.
CTRL-ALT-LED: Leaking Data from Air-Gapped Computers Via Keyboard LEDs

Using the keyboard LEDs to send data optically was proposed in 2002 by Loughry and Umphress [1] (Appendix A). In this paper we extensively explore this threat in the context of a modern cyber-attack with current hardware and optical equipment. In this type of attack, an advanced persistent threat (APT) uses the keyboard LEDs (Caps-Lock, Num-Lock and Scroll-Lock) to encode information and exfiltrate data from airgapped computers optically. Notably, this exfiltration channel is not monitored by existing data leakage prevention (DLP) systems. We examine this attack and its boundaries for today's keyboards with USB controllers and sensitive optical sensors. We also introduce smartphone and smartwatch cameras as components of malicious insider and 'evil maid' attacks. We provide the necessary scientific background on optical communication and the characteristics of modern USB keyboards at the hardware and software level, and present a transmission protocol and modulation schemes. We implement the exfiltration malware, discuss its design and implementation issues, and evaluate it with different types of keyboards. We also test various receivers, including light sensors, remote cameras, 'extreme' cameras, security cameras, and smartphone cameras. Our experiment shows that data can be leaked from air-gapped computers via the keyboard LEDs at a maximum bit rate of 3000 bit/sec per LED given a light sensor as a receiver, and more than 120 bit/sec if smartphones are used. The attack doesn't require any modification of the keyboard at hardware or firmware levels.
Learning Key-Value Store Design

We introduce the concept of design continuums for the data layout of key-value stores. A design continuum unifies major distinct data structure designs under the same model. The critical insight and potential long-term impact is that such unifying models 1) render what we consider up to now as fundamentally different data structures to be seen as views of the very same overall design space, and 2) allow seeing new data structure designs with performance properties that are not feasible by existing designs. The core intuition behind the construction of design continuums is that all data structures arise from the very same set of fundamental design principles, i.e., a small set of data layout design concepts out of which we can synthesize any design that exists in the literature as well as new ones. We show how to construct, evaluate, and expand, design continuums and we also present the first continuum that unifies major data structure designs, i.e., B+tree, B-epsilon-tree, LSM-tree, and LSH-table. The practical benefit of a design continuum is that it creates a fast inference engine for the design of data structures. For example, we can predict near instantly how a specific design change in the underlying storage of a data system would affect performance, or reversely what would be the optimal data structure (from a given set of designs) given workload characteristics and a memory budget. In turn, these properties allow us to envision a new class of self-designing key-value stores with a substantially improved ability to adapt to workload and hardware changes by transitioning between drastically different data structure designs to assume a diverse set of performance properties at will.
ACTNET: end-to-end learning of feature activations and aggregation for effective instance image retrieval

We propose a novel CNN architecture called ACTNET for robust instance image retrieval from large-scale datasets. Our key innovation is a learnable activation layer designed to improve the signal-to-noise ratio (SNR) of deep convolutional feature maps. This works in tandem with multi-stream aggregation, where complementary deep features from different convolutional layers are transformed and balanced, using our novel activation layer, before aggregation into a global descriptor. Importantly, the learnable parameters of activation blocks are explicitly trained, jointly with the CNN parameters, in an end-to-end manner minimising triplet loss. This means that our network jointly learns the CNN filters and their optimal aggregation for the retrieval task. To our knowledge, this is the first time parametric functions are used to control and learn optimal aggregation. We conduct an in-depth experimental study on three non-linear activation functions: Sine-Hyperbolic, Exponential and modified Weibull, showing that while all bring significant gains the Weibull function performs best thanks to its ability to equalise strong activations. The results clearly demonstrate that activation functions significantly enhance the discriminative power of deep features, leading to state-of-the-art retrieval results.
Ross 128 – GL 447

Context. Long-term chromospheric activity in slow-rotating fully convective stars has scarcely been explored. Ross 128 (Gl 447) is a slow-rotator and inactive dM4 star that has been extensively observed. It hosts the fourth closest extrasolar planet. Aims. Ross 128 is an ideal target to test dynamo theories in slow-rotating low-mass stars. Methods. To characterize the magnetic activity of Ross 128, we studied the SK-indexes derived from CASLEO, HARPS, FEROS, UVES, and X-shooter spectra. Using the generalized Lomb-Scargle and CLEAN periodograms, we analyzed the whole SK time-series obtained between 2004 and 2018. We performed a similar analysis for the Na I-index, and we analyzed its relation with the SK-index. Results. From both indexes, we obtain a possible activity cycle with a period of about five years, which is one of a small handful of activity cycles that have been reported for a slow-rotating fully convective star.
Using Dictation to Measure Language Proficiency

Many teachers are faced with mixed-ability classes and have little information on the proficiency of their students. With group work being central to most pedagogies in second language acquisition, teachers may want to construct groups for specific purposes; knowledge of the relative proficiency of students is therefore very important. Although tools such as the Test of English for International Communication (TOEIC) are available, they may be expensive to implement, or results may be unavailable to the teacher. This paper discusses dictation tests as a possible way of measuring the relative proficiency of students and highlights the results of a study conducted in a university in Japan that used dictation as a test of proficiency and compared results with the TOEIC test. The authors explain how to design and conduct dictation in the classroom and demonstrate how dictation is a cheap, simple, and effective means of measuring language proficiency.
Spectral gap of the discrete Laplacian on triangulations

Our goal in this paper is to find an estimate for the spectral gap of the Laplacian on a 2-simplicial complex consisting on a triangulation of a complete graph. An upper estimate is given by generalizing the Cheeger constant. The lower estimate is obtained from the first non-zero eigenvalue of the discrete Laplacian acting on the functions of certain sub-graphs.