Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Bound on the Combinatorial Complexity of Approximating Polytopes

This article considers the question of how to succinctly approximate a multidimensional convex body by a polytope. Given a convex body K of unit diameter in Euclidean d-dimensional space (where d is a constant) and an error parameter ε > 0, the objective is to determine a convex polytope of low combinatorial complexity whose Hausdorff distance from K is at most ε. By combinatorial complexity, we mean the total number of faces of all dimensions. Classical constructions by Dudley and Bronshteyn/Ivanov show that O(1/ε(d-1)/2) facets or vertices are possible, respectively, but neither achieves both bounds simultaneously. In this article, we show that it is possible to construct a polytope with O(1/ε(d-1)/2) combinatorial complexity, which is optimal in the worst case. Our result is based on a new relationship between ε-width caps of a convex body and its polar body. Using this relationship, we are able to obtain a volume-sensitive bound on the number of approximating caps that are “essentially different. ” We achieve our main result by combining this with a variant of the witness-collector method and a novel variable-thickness layered construction of the economical cap covering.
Design Considerations and Performance Trade-Offs for 56Gb/s Discrete Multi-Tone Electrical Link

For communicating over mid-to-long-reach electrical links at a data-rate above 56Gb/s, ADC-based receiver (RX) has become a dominant architecture due to its strong equalization capability using complex digital signal processing algorithm benefiting from the extreme process node shrinkage. This paper discusses the design of >56Gb/s DAC/ADC-based wireline transceiver (TRX) with discrete multi-tone (DMT) modulation. Some notable differences between wireless orthogonal frequency division multiplexing (OFDM) systems and wireline DMT systems are discussed, and some practical design considerations for a multiple tens-of-Gb/s DMT RX is provided.
REMARK ON GENERALIZED UNIVERSAL COVERING SPACE IN DIGITAL COVERING THEORY

As a survey-type article, the paper reviews the recent results on a (generalized) universal covering space in digital cover- ing theory. The recent paper (19) established the generalized uni- versal (2;k)-covering property which improves the universal (2;k)- covering property of (3). In algebraic topology it is well-known that a simply connected and locally path connected covering space is a universal covering space. Unlike this property, in digital covering theory we can propose that a generalized universal covering space has its intrinsic feature. This property can be useful in classifying digital covering spaces and in studying a shortest k-path problem in data structure.
LinkRadar: Assisting the Analysis of Inter-app Page Links via Transfer Learning

Analyzing links among pages from different mobile apps is an important task of app analysis. Currently, most efforts of analyzing inter-app page links rely on static program analysis, which produces a lot of false positives, requiring significant manual effort to verify the links. To address the issue, in this paper, we propose LinkRadar, a data-driven approach to assisting the analysis of inter-app page links. Our key idea is to use dynamic program analysis to gather a set of actual inter-app page links, based on which we train a model to predict whether there exist links among pages from different apps to help verify the results of static program analysis. The challenge is that inter-app page links are hard to be triggered by dynamic program analysis, making it difficult to collect enough inter-app page links to train the model. Considering the similarity between intra-app page links and inter-app page links, we use transfer learning to deal with the data scarcity problem. Evaluation results show that LinkRadar is able to infer the inter-app page links with high accuracy.
Photoelectrochemical and theoretical investigations of spinel type ferrites (MxFe3−xO4) for water splitting: a mini-review

Abstract. Solar-assisted water splitting using photoelectrochemical cells (PECs) is one of the promising pathways for the production of hydrogen for renewable energy storage. The nature of the semiconductor material is the primary factor that controls the overall energy conversion efficiency. Finding semiconductor materials with appropriate semiconducting properties (stability, efficient charge separation and transport, abundant, visible light absorption) is still a challenge for developing materials for solar water splitting. Owing to the suitable bandgap for visible light harvesting and the abundance of iron-based oxide semiconductors, they are promising candidates for PECs and have received much research attention. Spinel ferrites are subclasses of iron oxides derived from the classical magnetite (FeIIFe2IIIO4) in which the FeII is replaced by one (some cases two) additional divalent metals. They are generally denoted as MxFe3−xO4 (M=Ca, Mg, Zn, Co, Ni, Mn, and so on) and mostly crystallize in spinel or inverse spinel structures. In this mini review, we present the current state of research in spinel ferrites as photoelectrode materials for PECs application. Strategies to improve energy conversion efficiency (nanostructuring, surface modification, and heterostructuring) will be presented. Furthermore, theoretical findings related to the electronic structure, bandgap, and magnetic properties will be presented and compared with experimental results.
Optimal Distributed Convex Optimization on Slowly Time-Varying Graphs

We study optimal distributed first-order optimization algorithms when the network (i.e., communication constraints between the agents) changes with time. This problem is motivated by scenarios where agents experience network malfunctions. We provide a sufficient condition that guarantees a convergence rate with optimal (up to logarithmic terms) dependencies on the network and function parameters if the network changes are constrained to a small percentage <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> of the total number of iterations. We call such networks slowly time-varying networks. Moreover, we show that Nesterov's method has an iteration complexity of <inline-formula><tex-math notation="LaTeX">$\Omega ((\sqrt{\kappa _\Phi \cdot \bar{\chi }} + \alpha \log (\kappa _\Phi \cdot \bar{\chi }))\log ({1}/{\varepsilon }))$</tex-math></inline-formula> for decentralized algorithms, where <inline-formula><tex-math notation="LaTeX">$\kappa _\Phi$</tex-math></inline-formula> is the condition number of the objective function, and <inline-formula><tex-math notation="LaTeX">$\bar{\chi }$</tex-math></inline-formula> is a worst case bound on the condition number of the sequence of communication graphs. Additionally, we provide an explicit upper bound on <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> in terms of the condition number of the objective function and network topologies.
CETVSP: Cost Efficient Trust-Based Vehicle Selection for Platoon

The vehicular ad hoc networks (VANETs) are increasingly progressing towards advancement to provide a more comfortable and sophisticated experience to users. Platooning is one of such examples in the pool of these advancements where a lead vehicle provides the driving parameters wirelessly to the vehicles following it so that they have a more comfortable and quality experience on the road. However, since the lead vehicle has so much control, ensuring the trustworthy behavior of the lead vehicle is critical. Our scheme, Cost Efficient Trust-Based Vehicle Selection for Platoon (CETVSP), ensures that capability by granting each lead vehicle with a trust score. The same trust model has also been expanded to be applied to selection of a reliable vehicle if the message has to be relayed through it. This only requires a small modification and hence reduces redundancy. Our simulation results show that our scheme is more efficient while requiring less computational expense. The security analysis proves that the scheme is safe against user attacks such as badmouthing and on-off attacks.
Rate Performance Analysis of User-Centric C-RAN with Orthogonal Multiple Access

To support the massive data traffic in the next generation cellular networks, the cloud radio access network (C-RAN) architecture is promising to significantly improve the spectral efficiency. When a massive number of remote radio heads (RRHs) are employed in the C-RAN, substantial rate gain can be achieved over that with co-located antennas. To reduce computational complexity at the central processor, a user-centric cell structure, known as virtual cell, is proposed, where each user is served by a few surrounding RRHs. This paper focuses on the scaling behavior of the average ergodic rate of a C-RAN with user-centric virtual cells. By assuming that orthogonal multiple access method is adopted among the users with overlapping virtual cells, a lower-bound of the average ergodic capacity is derived as an explicit function of the virtual cell radius r and the RRH intensity λA. Simulation results verify that the average ergodic capacity increases monotonically with r and λA in the orders of $O ((\alpha-2)\ log_{2} r)$ and $O((\frac{\alpha}{2}-1) \log_{2} r)$, respectively.
Analysis and modelling of the contact radius effect on the cutting forces in cylindrical and face turning of Ti 6 Al 4 V titanium alloy

Cutting forces are representative data to characterize machining operations. They have to be known to perform the part manufacturing. Therefore, cutting forces predictive models are useful and it is possible to optimize them by taking into account new parameters. Hence, this study deals with the geometrical modelling of tool-workpiece interaction and its influence on the cutting forces. The analysis focuses on convex contact radius between the machined part and the tool. Experiments are based on cylindrical and face turning of Ti6Al4V titanium alloy. The results highlight a significant influence of contact conditions on ploughing mechanisms, and consequently on the cutting forces intensity. This phenomenon is taken into account in the suggested models, providing a better accuracy of cutting forces modelled.
Infrastructure fault detection and prediction in edge cloud environments

As an emerging 5G system component, edge cloud becomes one of the key enablers to provide services such us mission critical, IoT and content delivery applications. However, because of limited fail-over mechanisms in edge clouds, faults (e.g., CPU or HDD faults) are highly undesirable. When infrastructure faults occur in edge clouds, they can accumulate and propagate; leading to severe degradation of system and application performance. It is therefore crucial to identify these faults early on and mitigate them. In this paper, we propose a framework to detect and predict several faults at infrastructure-level of edge clouds using supervised machine learning and statistical techniques. The proposed framework is composed of three main components responsible for: (1) data pre-processing, (2) fault detection, and (3) fault prediction. The results show that the framework allows to timely detect and predict several faults online. For instance, using Support Vector Machine (SVM), Random Forest(RF) and Neural Network(NN)models, the framework is able to detect non-fatal CPU and HDD overload faults with an F1 score of more than 95%. For the prediction, the Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) have comparable accuracy at 96.47% vs. 96.88% for CPU-overload fault and 85.52% vs. 88.73% for network fault.
Human being @ Risk

How do persons and societies cope with risk and vulnerability? This is the fascinating philosophical question to which Coeckelbergh’s book, Human beings @ risk intends to respond. Human beings are at risk since they are in the world: vulnerability, Coeckelbergh claims, is condition of being in the world, in other words it is something inherent to human condition. At the same time, human beings are always already engaged in the struggle against risk and vulnerability in order to minimize or radically eradicate them from their life: acting to reduce vulnerability is an instinct implanted in human beings by nature, which drives human behaviours in social environment like the Hobbesian instinct of self-preservation. The issue seems to be clear and linear enough: both vulnerability and the human struggle against vulnerability represent naturally and historically existential conditions, they are strictly related and the latter is a consequence of the former one. Nevertheless, the relationship between human being and vulnerability is more complex than what may appear at first sight, since the human struggle against vulnerability entails a reflection on the concept of human enhancement and a careful evaluation of related ethical problems: is it ethically acceptable to overcome the human limitations by the means of technology in order to remove or radically minimize vulnerability? “And can we become invulnerable? Are there limitations to ‘making the human’? ” (Coeckelbergh, 2013, p. 10). The ongoing debate on human enhancement and its assumption is highly polarised between who defends the human nature (conservatives like Habermas, Fukuyama, McKibben, Elliot), on one hand, and who wants to
Achieving Zero ms Handover Interruption in New Radio with Higher Throughput Using D2D Communication

The demand for high data rate and data volume has been increased rapidly over the last decade. This is due to the growth of bandwidth-hungry wireless applications and services. Thus, the 5G cellular network is expected to solve this problem while supporting seamless mobility. This brings the demand for low latency handover in cellular networks. According to 3GPP, the target for Handover Interruption Time (HIT) is 0 ms. Thus, in this paper, we propose to exploit Device-to-Device (D2D) communication to achieve 0 ms HIT and thus support seamless mobility. The D2D based handover considers two modes of operation, viz. direct and D2D. During handover, the gNodeB (gNB) computes the best mode of operation (direct or D2D) for the data transfer to the receiver User Equipment (UE). In the case of the D2D mode of operation, the gNB finds an RRC_Connected/RRC_Inactive UE to act as a relay for transferring the content from the gNB to the receiver UE with higher throughput. A complete handover procedure that explains the above D2D based handover mechanism is proposed. With extensive simulations, we demonstrate that the proposed D2D based handover procedure achieve 0 ms HIT with higher throughput as compared to the direct mode of data transmission. We also demonstrate that with the increase in the user density from 50 to 200 in the given cellular region, the number of D2D based handover increases by 573% as compared to that of the direct mode of operation which increases by 137%. Further, we observe that 42.9% of total handover achieves up to 20% increment in throughput by employing D2D based relay mode as compared to the direct mode.
Caching Scores for Faster Query Processing with Dynamic Pruning in Search Engines

We propose to use a score cache, which stores the score of the k-th result of a query, to accelerate top-k query processing with dynamic pruning methods (i.e., WAND and BMW). We introduce heuristics that, for a new query, generate its subsets and probe the score cache to obtain a lower-bound on its score threshold. Our experiments show up to 8.6% savings in mean processing time for the queries that are not seen before, i.e., cannot benefit from a result cache.
Reproductive Biology of Aphanius mento (Heckel in: Russegger, 1843) (Osteichthyes: Cyprinodontidae) in Kırkgöz Spring (Antalya-Turkey)

The reproduction of Aphanius mento is described from the Kirkgoz Spring (Turkey) (37o06' N; 30°35' E) between January 2002 and December 2002. In this study, the sex ratio, age, the relationship between fecundity and fish length, weight and gonad weight, the gonadosomatic index, egg diameter and the first age of maturation were observed. Age range was determined as 0-VII years for 654 individuals. The overall male to female ratio was 1.10 : 1.00. Spawning period was between May and July. The first maturation was found to be at age I. First maturity length (Lm) was estimated at 2.45 cm for females. The mean fecundity per individual was found to be 228.48±28.60. The mean egg diameter was calculated as 1240.09±53. 26µ.
TeamNet: A Collaborative Inference Framework on the Edge

With significant increases in wireless link capacity, edge devices are more connected than ever, which makes possible forming artificial neural network (ANN) federations on the connected edge devices. Partition is the key to the success of distributed ANN inference while unsolved because of the unclear knowledge representation in most of the ANN models. We propose a novel partition approach (TeamNet) based on the psychologically-plausible competitive and selective learning schemes while evaluating its performance carefully with thorough comparisons to other existing distributed machine learning approaches. Our experiments demonstrate that TeamNet with sockets and transmission control protocol (TCP) significantly outperforms sophisticated message passing interface (MPI) approaches and the state-of-the-art mixture of experts (MoE) approaches. The response time of ANN inference is shortened by as much as 53% without compromising predictive accuracy. TeamNet is promising for having distributed ANN inference on connected edge devices and forming edge intelligence for future applications.
Deep Learning for Blast Furnaces: Skip-Dense Layers Deep Learning Model to Predict the Remaining Time to Close Tap-holes for Blast Furnaces

Manufacturing steel requires extremely challenging industrial processes. In particular, predicting the exact time instance of opening and closing tap-holes in a blast furnace has a great influence on steel production efficiency and operating cost, in addition to human safety. However, currently predicting the time to open and close tap-holes of the blast furnace still highly relies on manual human expertise and labor. Also, most of the prior research is limited to indirectly model the level of liquids in the hearth, using complex mathematical models or classical machine learning approaches. In this paper, we use a data-driven deep learning method to more accurately predict the remaining time to close each tap-hole in a blast furnace and develop an AI-enabled automated advisory system to reduce manual human efforts as well as operation cost. We develop a multivariate time series forecasting algorithm using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) models to more accurately predict the opening and closing time for the Pohang Iron and Steel Company (POSCO) blast furnace. In particular, we use and validate data from one of the largest operating furnaces in the world to develop our system. Our proposed Skip-dense CNN (S-CNN) model achieves more than 90% accuracy within ±30 minutes tolerance, compared to other LSTM baseline models. Our S-CNN model has been successfully deployed at a large-scale blast furnace of POSCO since January 2018 and has achieved similar accuracy. And we even exceeded the reported human performance in a real operational environment.
The Electoral Underpinnings of Corruption in Rich and Poor Democratic Polities 1

This paper reports results of research funded by the National Science Foundation (SES–0074860), the Russell Sage Foundation, and the Academic Senate, the Center for European andEurasian Studies, the International Institute, and the Center for International Business Educationand Research at the University of California at Los Angeles. Portions of the research were con-ducted or are underway jointly with Eric C.C. Chang, Seth Hill, Lucio Picci, and Devesh Tiwari.The author is solely responsible for the material presented.
Haptiple: A Wearable, Modular and Multiple Haptic Feedback System for Embodied Interaction

Humans feel tactile sensations over their whole body. However, the parts of the body on which many haptic devices can be worn are restricted, and hence, these devices do not provide multiple haptic feedback over the whole body. Multiple haptic feedback can enhance user experience for various applications. Therefore, we propose a multiple haptic feedback system called Haptiple, which is a wearable and modular system for embodied interactions based on a wireless platform. The system consists of three types of modular haptic devices (vibro-tactile, pressure, and thermal/wind) that can be placed on multiple body parts such as the hand, wrist, ankle, and chest. We demonstrate interactive experiences to show the feasibility of our concept.
Monte Carlo event generation for LHC

The necessity of event generators for LHC physics studies is illustrated, and the Monte Carlo approach is outlined. A survey is presented of existing event generators, followed by a more detailed study of the different components that appear in a complete program. ∗ To appear in the proceedings of the CERN School of Computing, Ystad, Sweden, 23 August — 2 September 1991 CERN-TH.6275/91 October 1991
Effects at Cryogenic Temper i- aAs MESFET, and MOSFE evices

Front-end electronics for liquid ionization chamber calorimetry at hadron collider experiments may be exposed to substantial levels of ionizing radiation and neutron flnences in a cryogenic environment. Measurements of devices built with rad- hard technologies have shown that devices able to operate in these conditions exist. Several families of devices (Si-JFET's, rad- hard MOSFET's, and GaAs MESFET's) have been irradiated and tested at a stable cryogenic temperature up to doses of 55 Mrad of ionizing radiation and up to neutron fluences of 4~10'~ n/cm2. Radiation effects on dc characteristics and on noise will be presented.
A Systematic Approach to Sizing Capacitors in Split-SAR ADC to Achieve Optimum Redundancy

This paper proposes a systematic way of sizing capacitors in a split-Successive Approximation Register (SAR) analog-to-digital converter (ADC) so as to achieve optimum redundancy that can take care of comparator noise, comparator offset, and digital-to-analog converter (DAC) finite settling. The capacitor sizing technique to optimally distribute redundancy across all bit-cycling phases, has been demonstrated with system-level simulation using MATLAB for split-SAR with one, two, and three bridge-capacitors . The proposed technique of sizing capacitors to achieve optimum redundancy at every bit-cycling step can be easily extended for split-SAR with arbitrary number of bridge-capacitors.
MEC Support for 5G-V2X Use Cases through Docker Containers

The Multi-access Edge Computing (MEC) paradigm and the Cellular Vehicle-to-Everything (C-V2X) technology prove to be good candidates to address the vehicular applications' demands of high-performing connectivity and low-latency access to computing and storage resources. MEC provides cloud-like resources at the network edge, i.e., at the Multi-access Edge (ME) host. While vehicles move around, a service application instance running on a ME host may be triggered to move to another ME host to better support the application's demands. As a side effect, this migration may undermine service continuity. In this paper, we refer to the latest available ETSI MEC and 3GPP C-V2X specifications to investigate the issue of service migration between ME hosts in the context of vehicular communication. Early experimental results provide measures of the service migration latencies when ME applications run as Docker containers.