Miracle-dz/UniFAR-FTUs huggingface dataset:
How Many Unicycles on a Wheel?

To define our terms: For n ≥ 3, the wheel graph Wn consists of n vertices connected in a cycle (the rim), together with one additional vertex (the hub) which is connected to all of them. The standard drawing of the wheel has the rim vertices equally spaced around a circle with the hub at the center. A spanning unicycle in a graph is a subset of the edges which leaves the vertices connected and contains exactly one cycle. Think of it as using graph edges to build a “ring road” through some or all of the vertices, and then choosing just enough additional edges to reach any remaining vertices off the ring. If you do this on a graph with n vertices, you will find you always use exactly n edges. At this point, you might like to pause and work out how many spanning unicycles can be found in W4 before proceeding to the following theorem, our main result, which discloses the number of unicycles in any wheel graph.
An Anonymous yet Accountable Contract Wallet System using Account Abstraction

Account abstraction allows a contract wallet to initiate transaction execution. Thus, account abstraction is useful for preserving the privacy of externally owned accounts (EOAs) because it can remove a transaction issued from an EOA to the contract wallet and hides who issued the transaction by additionally employing anonymous authentication procedures such as ring signatures. However, unconditional anonymity is undesirable in practice because it prevents to reveal who is accountable for a problem when it arises. Thus, maintaining a balancing between anonymity and accountability is important. In this paper, we propose an anonymous yet accountable contract wallet system. In addition to account abstraction, the proposed system also utilizes accountable ring signatures (Bootle et al., ESORICS 2015). The proposed system provides (1) anonymity of a transaction issuer that hides who agreed with running the contract wallet, and (2) accountability of the issuer, which allows the issuer to prove they agreed with running the contract wallet. Moreover, due to a security requirement of accountable ring signatures, the transaction issuer cannot claim that someone else issued the transaction. This functionality allows us to clarify the accountability involved in issuing a transaction. In addition, the proposed system allows an issuer to employ a typical signature scheme, e.g., ECDSA, together with the ring signature scheme. This functionality can be considered an extension of the common multi-signatures that require a certain number of ECDSA signatures to run a contract wallet. The proposed system was implemented using zkSync (Solidity). We discuss several potential applications of the proposed system, i.e., medical information sharing and asset management.
Lipschitz Transport Maps via the Follmer Flow

Inspired by the construction of the F{\"o}llmer process, we construct a unit-time flow on the Euclidean space, termed the F{\"o}llmer flow, whose flow map at time 1 pushes forward a standard Gaussian measure onto a general target measure. We study the well-posedness of the F{\"o}llmer flow and establish the Lipschitz property of the flow map at time 1. We apply the Lipschitz mapping to several rich classes of probability measures on deriving dimension-free functional inequalities and concentration inequalities for the empirical measure.
Vectorial phase retrieval in super-resolution polarization microscopy

In single-molecule orientation localization microscopy, valuable information about the orientation and longitudinal position of each molecule is often encoded in the shape of the point spread function (PSF). Yet, this shape can be significantly affected by aberrations and other imperfections in the imaging system, leading to an erroneous estimation of the measured parameters. A basic solution is to model the aberrations as a scalar mask in the pupil plane that is characterized through phase retrieval algorithms. However, this approach is not suitable for cases involving polarization-dependent aberrations, introduced either through unintentional anisotropy in the elements or by using birefringent masks for PSF shaping. Here, this problem is addressed by introducing a fully vectorial model in which the polarization aberrations are represented via a spatially dependent Jones matrix, commonly used to describe polarization-dependent elements. It is then shown that these aberrations can be characterized by a set of PSF measurements at varying focal planes and for various polarization projections. This PZ-stack of PSFs, which contains diversity in both phase and polarization projection, is used in a phase retrieval algorithm based on nonlinear optimization to determine the aberrations. This methodology is demonstrated with numerical simulations and experimental measurements. The pyPSFstack software developed for modeling and characterization is made freely available.
Resource Management for IRS-Assisted WP-MEC Networks With Practical Phase Shift Model

Wireless powered mobile edge computing (WPMEC) has been recognized as a promising solution to enhance the computational capability and sustainable energy supply for lowpower wireless devices (WDs). However, when the communication links between the hybrid access point (HAP) and WDs are hostile, the energy transfer efficiency and task offloading rate are compromised. To tackle this problem, we propose to employ multiple intelligent reflecting surfaces (IRSs) to WP-MEC networks. Based on the practical IRS phase shift model, we formulate a total computation rate maximization problem by jointly optimizing downlink/uplink IRSs passive beamforming, downlink energy beamforming, and uplink multiuser detection (MUD) vector at HAPs, task offloading power and local computing frequency of WDs, and the time slot allocation. Specifically, we first derive the optimal time allocation for downlink wireless energy transmission (WET) to IRSs and the corresponding energy beamforming. Next, with fixed time allocation for the downlink WET to WDs, the original optimization problem can be divided into two independent subproblems. For the WD charging subproblem, the optimal IRSs passive beamforming is derived by utilizing the successive convex approximation (SCA) method and the penaltybased optimization technique, and for the offloading computing subproblem, we propose a joint optimization framework based on the fractional programming (FP) method. Finally, simulation results validate that our proposed optimization method based on the practical phase shift model can achieve a higher total computation rate compared to the baseline schemes.
BNS-Net: A Dual-channel Sarcasm Detection Method Considering Behavior-level and Sentence-level Conflicts

Sarcasm detection is a binary classification task that aims to determine whether a given utterance is sarcastic. Over the past decade, sarcasm detection has evolved from classical pattern recognition to deep learning approaches, where features such as user profile, punctuation and sentiment words have been commonly employed for sarcasm detection. In real-life sarcastic expressions, behaviors without explicit sentimental cues often serve as carriers of implicit sentimental meanings. Motivated by this observation, we proposed a dual-channel sarcasm detection model named BNS-Net. The model considers behavior and sentence conflicts in two channels. Channel 1: Behavior-level Conflict Channel reconstructs the text based on core verbs while leveraging the modified attention mechanism to highlight conflict information. Channel 2: Sentence-level Conflict Channel introduces external sentiment knowledge to segment the text into explicit and implicit sentences, capturing conflicts between them. To validate the effectiveness of BNS-Net, several comparative and ablation experiments are conducted on three public sarcasm datasets. The analysis and evaluation of experimental results demonstrate that the BNS-Net effectively identifies sarcasm in text and achieves the state-of-the-art performance.
Default Process Modeling and Credit Valuation Adjustment

This paper presents a convenient framework for modeling default process and pricing derivative securities involving credit risk. The framework provides an integrated view of credit valuation adjustment by linking distance-to-default, default probability, survival probability, and default correlation together. We show that risky valuation is Martingale in our model. The framework reduces the technical issues of performing risky valuation to the same issues faced when performing the ordinary valuation. The numerical results show that the model prediction is consistent with the historical observations.
DTW+S: Shape-based Comparison of Time-series with Ordered Local Trend

Measuring distance or similarity between time-series data is a fundamental aspect of many applications including classification, clustering, and ensembling/alignment. Existing measures may fail to capture similarities among local trends (shapes) and may even produce misleading results. Our goal is to develop a measure that looks for similar trends occurring around similar times and is easily interpretable for researchers in applied domains. This is particularly useful for applications where time-series have a sequence of meaningful local trends that are ordered, such as in epidemics (a surge to an increase to a peak to a decrease). We propose a novel measure, DTW+S, which creates an interpretable"closeness-preserving"matrix representation of the time-series, where each column represents local trends, and then it applies Dynamic Time Warping to compute distances between these matrices. We present a theoretical analysis that supports the choice of this representation. We demonstrate the utility of DTW+S in several tasks. For the clustering of epidemic curves, we show that DTW+S is the only measure able to produce good clustering compared to the baselines. For ensemble building, we propose a combination of DTW+S and barycenter averaging that results in the best preservation of characteristics of the underlying trajectories. We also demonstrate that our approach results in better classification compared to Dynamic Time Warping for a class of datasets, particularly when local trends rather than scale play a decisive role.
Which algorithm to select in sports timetabling?

Any sports competition needs a timetable, specifying when and where teams meet each other. The recent International Timetabling Competition (ITC2021) on sports timetabling showed that, although it is possible to develop general algorithms, the performance of each algorithm varies considerably over the problem instances. This paper provides an instance space analysis for sports timetabling, resulting in powerful insights into the strengths and weaknesses of eight state-of-the-art algorithms. Based on machine learning techniques, we propose an algorithm selection system that predicts which algorithm is likely to perform best when given the characteristics of a sports timetabling problem instance. Furthermore, we identify which characteristics are important in making that prediction, providing insights in the performance of the algorithms, and suggestions to further improve them. Finally, we assess the empirical hardness of the instances. Our results are based on large computational experiments involving about 50 years of CPU time on more than 500 newly generated problem instances.
Short-Term Load Forecasting Using A Particle-Swarm Optimized Multi-Head Attention-Augmented CNN-LSTM Network

—Short-term load forecasting is of paramount importance in the efficient operation and planning of power systems, given its inherent non-linear and dynamic nature. Recent strides in deep learning have shown promise in addressing this challenge. However, these methods often grapple with hyperparameter sensitivity, opaqueness in interpretability, and high computational overhead for real-time deployment. In this paper, I propose a novel solution that surmounts these obstacles. Our approach harnesses the power of the Particle-Swarm Optimization algorithm to autonomously explore and optimize hyperparameters, a Multi-Head Attention mechanism to discern the salient features crucial for accurate forecasting, and a streamlined framework for computational efficiency. Our method undergoes rigorous evaluation using a genuine electricity demand dataset. The results underscore its superiority in terms of accuracy, robustness, and computational efficiency. Notably, our Mean Absolute Percentage Error of 1.9376 marks a significant advancement over existing state-of-the-art approaches, heralding a new era in short-term load forecasting.
High-Speed (7, 2) Compressor Using A Fast Carry-Generation Logic based on Sorting Network

Fast binary compressors are the main components of many basic digital calculation units. In this paper, a high-speed (7,2) compressor with a fast carry-generation logic is proposed. The carry-generation logic is based on the sorting network, and it can generate a carry bit within 2 logical stages other than 3 stages as in previous school book full adders. Collaborating with the adjusted full adder logic, the proposed (7,2) compressor achieves using only 11 basic logical stages. Testing this new design in a binary arry with 7 rows and 8 columns, and the result shows that this design have higher proformance than previous designs. This method is suitable for high proformance cases in multiplication design or other cryptography hardware blocks.
An Adaptive and Modular Blockchain Enabled Architecture for a Decentralized Metaverse

A metaverse breaks the boundaries of time and space between people, realizing a more realistic virtual experience, improving work efficiency, and creating a new business model. Blockchain, as one of the key supporting technologies for a metaverse design, provides a trusted interactive environment. However, the rich and varied scenes of a metaverse have led to excessive consumption of on-chain resources, raising the threshold for ordinary users to join, thereby losing the human-centered design. Therefore, we propose an adaptive and modular blockchain-enabled architecture for a decentralized metaverse to address these issues. The solution includes an adaptive consensus/ledger protocol based on a modular blockchain, which can effectively adapt to the ever-changing scenarios of the metaverse, reduce resource consumption, and provide a secure and reliable interactive environment. In addition, we propose the concept of Non-Fungible Resource (NFR) to virtualize idle resources. Users can establish a temporary trusted environment and rent others’ NFR to meet their computing needs. Finally, we simulate and test our solution based on XuperChain, and the experimental results prove the feasibility of our design.
Manipulation of Neuronal Network Firing Patterns using Temporal Deep Unfolding-based MPC

Because neuronal networks are intricate systems composed of interconnected neurons, their control poses challenges owing to their nonlinearity and complexity. In this paper, we propose a method to design control input to a neuronal network to manipulate the firing patterns of modules within the network. We propose a methodology for designing a control input based on temporal deep unfolding-based model predictive control (TDU-MPC), a control methodology based on the deep unfolding technique actively investigated in the context of wireless signal processing. During the method development, we address the unique characteristics of neuron dynamics, such as zero gradients in firing times, by approximating input currents using a sigmoid function. The effectiveness of the proposed method is confirmed via numerical simulations. In networks with 15 and 30 neurons, the control was achieved to switch the firing frequencies of two modules without directly applying control inputs. This study includes a tailored methodology for networked neurons, the extension of TDU-MPC to nonlinear systems with reset dynamics, and the achievement of desired firing patterns in neuronal networks.
LRCs: Duality, LP Bounds, and Field Size

We develop a duality theory of locally recoverable codes (LRCs) and apply it to establish a series of new bounds on their parameters. We introduce and study a refined notion of weight distribution that captures the code's locality. Using a duality result analogous to a MacWilliams identity, we then derive an LP-type bound that improves on the best known bounds in several instances. Using a dual distance bound and the theory of generalized weights, we obtain non-existence results for optimal LRCs over small fields. In particular, we show that an optimal LRC must have both minimum distance and block length relatively small compared to the field size.
International Conference on Ultrarelativistic Nucleus-Nucleus Collisions ( Quark Matter 2018 ) Exploring the Phase Space of Jet Splittings at ALICE using Grooming and Recursive Techniques

Hard splittings in the evolution of a hard scattered parton may be modified by the presence of a dense strongly interacting medium. Grooming procedures can be used to isolate such hard components of a jet and allows one to focus on the two subjets resulting from a sufficiently hard partonic splitting. Measurements of the symmetry parameter (zg), angular separation (Rg) and number of splittings (nSD) of Soft Drop groomed jets are reported as measured with the ALICE Detector in pp and Pb–Pb collisions at √ s= 7 TeV and √ sNN= 2.76 TeV, respectively. The use of recursive splittings and their mappings to identify interesting regions of phase space in the zg − Rg plane are also discussed.
Relaxation behaviours in ferromagnetic monolayers

: In this article, we brieﬂy review the studies on magnetic relaxation behaviours. The theoretical as well as experimental investigations are reported brieﬂy. A major part of this article is devoted to the recent Monte Carlo investigations into the roles of boundary conditions, dynamics and the Geometrical structures on the relaxation of magnetic monolayers modelled by two-dimensional Ising ferromagnet. We have studied all these eﬀects for a two dimensional Ising system with two types of deformations, namely preserving and area non-preserving.The Glauber protocol and Metropolis dynamical rules are employed in our simulations and we investigated the systems with both periodic and open boundary conditions. The major ﬁndings are the exponential relaxation and the dependence of relaxation time ( τ ) on the aspect ratio R (length over breadth). A power law dependence ( τ ∼ R − s ) has been observed for larger values of aspect ratio ( R ). The linear thermal ( T ) dependence of exponent ( s ) has been noticed ( s = aT + b ). The transient behaviours of the spin-ﬂip density have also been studied here for both surface and bulk/core. Both the saturated bulk/core and saturated surface spin-ﬂip density are observed to follow the logarithmic dependence f d = a + b log( L ) with the system size ( L ). For open boundary condition with any kind (Metropolis/Glauber) of dynamical rule, the faster relaxation has been observed. Similarly, Metropolis algorithm yields faster relaxation for any kind (open/periodic) of boundary condition. We appeal to the experimentalists for experimental support which may be applied in judicious magnetic coating of the credit cards for quicker response.
Longitudinal Digital Mood Charting in Bipolar Disorder: Experiences with ChronoRecord Over 20 Years

Abstract Introduction Longitudinal study is an essential methodology for understanding disease trajectories, treatment effects, symptom changes, and long-term outcomes of affective disorders. Daily self-charting of mood and other illness-related variables is a commonly recommended intervention. With the widespread acceptance of home computers in the early 2000s, automated tools were developed for patient mood charting, such as ChronoRecord, a software validated by patients with bipolar disorder. The purpose of this study was to summarize the daily mood, sleep, and medication data collected with ChronoRecord, and highlight some of the key research findings. Lessons learned from implementing a computerized tool for patient self-reporting are also discussed. Methods After a brief training session, ChronoRecord software for daily mood charting was installed on a home computer and used by 609 patients with affective disorders. Results The mean age of the patients was 40.3±11.8 years, a mean age of onset was 22±11.2 years, and 71.4% were female. Patients were euthymic for 70.8% of days, 15.1% had mild depression, 6.6% had severe depression, 6.6% had hypomania, and 0.8% had mania. Among all mood groups, 22.4% took 1–2 medications, 37.2% took 3–4 medications, 25.7 took 5–6 medications, 11.6% took 7–8 medications, and 3.1% took >8 medications. Conclusion The daily mood charting tool is a useful tool for increasing patient involvement in their care, providing detailed patient data to the physician, and increasing understanding of the course of illness. Longitudinal data from patient mood charting was helpful in both clinical and research settings.
PENERAPAN METODE AHP DAN METODE TOPSIS DALAM SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN ASISTEN LABORATORIUM KOMPUTER PADA STMIK WIDYA CIPTA DHARMA SAMARINDA

Penelitian pengambilan keputusan proses penerimaan asisten laboratorium yang di rancang untuk menentukan siapa saja yang akan diterima menjadi asisten laboratorium komputer. Seiring dengan perkembangan teknologi, maka pemanfaatan teknologi informasi dapat digunakan guna mempermudah dalam hal pengambilan suatu keputusan. Metode yang dipakai dalam pengambilan keputusan proses penerimaan asisten laboratorium adalah AHP (Analitical Hierarcy Process) dan Metode TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). Dalam proses seleksi tersebut terdapat beberapa kriteria penilaian yang dilakukan diantaranya kemampuan mengajar, penguasaan materi, wawancara, tanggung jawab dan disiplin.
DFCPP Runtime Library for Dataflow Programming

The Dataflow for C++(DFCPP) designed and implemented in this paper is a parallel programming library for dataflow computing on a general control flow hardware platform. Compared with existing dataflow programming libraries, DFCPP has an easy-to-use user interface and richer expression ability and can express different kinds of dataflow tasks. DFCPP designs a memory manager and task scheduler for the NUMA (Non-Uniform Memory Access) architecture and has filled the gap of existing dataflow runtime libraries in this field. The memory manager can allocate memory space on specific NUMA nodes according to the needs of tasks. As for the task scheduler, on the one hand, it places jobs at the location of their input data based on the idea, "The data is where it is; Move a thread to the data it needs most. " On the other hand, the task scheduler uses an improved task-stealing algorithm to steal the most suitable task. Combined with the memory manager and task scheduler, DFCPP can decrease the times of remote NUMA node access, thereby improving execution efficiency.