Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Communication Complexity of Private Function Sharing and Computation

This paper studies the communication complexity of sharing and computing a private function f among n parties holding private inputs xi with 1 ≤ i ≤ n in the information theoretical setting. Each party locally computes a message from a share of f and send it to a referee in parallel. The referee obtains the output f (x1,…,xn) while keeping f and xi private other than what can be inferred from the output. The communication complexity is evaluated by the maximum length of shares and messages. First, a lower bound on the communication complexity is presented. In particular, the length of the message is lower bounded by a restricted f. Second, the lower bound increases as the robustness level becomes larger, indicating a tradeoff between efficiency and robustness. For well-known families of functions, the lower bounds are instantiated, some of which matches the latest upper bounds up to a constant factor.
Shared Information for the Cliqueylon Graph

Shared information is a measure of mutual dependence among m≥2 jointly distributed discrete random variables. A new undirected probabilistic graphical model, a cliqueylon graph, is introduced, with potential applications in leader-follower swarms and neuron clusters with correlations of varying strength. Shared information is characterized explicitly for the cliqueylon, relying on structural properties of an underlying optimization. Implications for the data compression problem of omniscience are highlighted.
Source Polarization-Adjusted Convolutional Codes

Motivated by applications to low-latency secret key generation in physical-layer security, we study Polarization-Adjusted Convolutional (PAC) codes for source coding with side information. Source PAC codes operate in a dual manner to channel PAC codes by introducing a rate-one convolutional code after the polarization transform. The decoding of source PAC codes requires a careful scheduling of the successive cancellation decoder and a careful optimization of the rate profiling. Our empirical results demonstrate the improved performance of source PAC codes over regular polar codes using Successive Cancellation List (SCL) decoding. We illustrate the performance in terms for key generation rate in a secret-key generation setup over an Additive White Gaussian Noise (AWGN) channel, suggesting that PAC codes could improve the performance of physical-layer security schemes at short blocklength.
The Israeli Palestinian Mountain Aquifer: A Case Study in Ground Water Conflict Resolution

The dispute between Israelis and Palestinians over shared water resources of the Mountain Aquifer is one potential obstacle in the path of peace in the Middle East. This aquifer is the only source of water for Palestinians in theWest Bank and the main provider of freshwater to Israelis. The majority of its natural recharge area lies within the West Bank territories with two of its three basins flowing naturally toward Israel. In 1967, Israel occupied theWest Bank and imposed strict control policies over the utilization of the Mountain Aquifer? s water. At present, Palestinians and Israelis are moving toward a political resolution of their more than half a century old conflict. In the Declaration of Principles, Israelis and Palestinians created preconditions for the coming negotiations and the Israelis recognized water rights for Palestinians. There is no clear indication of the extent to which water would be under Palestinian control during the interim period, but there is an implicit recognition of the need to reform the existing water allocation system. In the Final Status Negotiations, the Joint Mountain Aquifer Committee, members from the Israeli government and the Palestinian National Authority must make decisions regarding the equitable distribution and joint management of the shared water of the Mountain Aquifer. This paper provides a decision case for a course at the graduate or senior undergraduate level based on water resources issues impacting the peace process.
A Machine-Learning Approach to Classify Depressive Posts from Reddit Social Media

Depression leading to suicide has become a major concern in our society. It's high time to investigate the reason behind it. As numerous people are expressing their concerns regarding depression on Reddit, such post can be taken into consideration in order to find the reason behind it. There has been a lot of research work in this field but most of it is done using traditional machine learning approach with NLP techniques. Moreover, recently there's has not been any significant research work regarding depression. In this paper, we found the indicators that could indicate how relevant Reddit users feel about depression using machine learning, ensemble learning, and deep learning techniques. Moreover, we pinpointed the terms that appear more frequently in narratives of depression. To strengthen the research work, we took the dataset over the period from 2017 to 2021. Furthermore, we demonstrated the effectiveness of LSTM & BERT on recent dataset.
Velocity Based Pore Pressure Prediction - A Case Study at One of the Iranian South West Oil Fields

Porepressure is one of the important concepts in hydrocarbon exploration and drilling. Abnormal pore pressures are encountered worldwide, often resulting in drilling problems such as kicks, blowouts, borehole instability, stuck pipe, and lost circulation. Thus quantitative predrill prediction of pore pressure is required for the safe and economic drilling of wells in overpressured formations. In addition, porepressure maps can be used to study hydrocarbon migration paths and cap rock quality. In this paper we tried to generate a reliable velocity model by combining stacking velocities, acoustic impedances from seismic inversion and available sonic well logs using geostatistical techniques. We used Bower’s velocity to pressure transformation that calibrated with offset wells. finally all the results checked and controlled. The quality control step reveals that despite of geological complexity we faced in this field, our final results are satisfactory and the method we used has been appropriate.
UWB and IMU Fusion Localization System Based on Unmanned Vehicles in Complex Factory Areas

In the face of serious satellite signal occlusion in the factory environment, using GPS and IMU combination for positioning still results in positioning jumps of about 1 meter, which cannot meet the positioning requirements of heavy trucks. Moreover, there is a large amount of metal interference in the factory area, which seriously affects the positioning effect of UWB. To address this problem, this paper proposes an optimized IMU and UWB fusion positioning method. Based on the regional roaming algorithm, the current reliable UWB measurement source is searched, and a pruning mean loss function based on TOA is designed to process the UWB pseudo-range measurement values. The inertial measurement unit (IMU) is introduced, and on the basis of IMU error state update in VINS, residual factors of UWB are added. Through optimization, UWB and IMU are loosely coupled for fusion positioning. Multiple experiments have demonstrated that this method offers a reliable positioning guarantee for heavy trucks operating within the challenging factory environment.
Curricular Object Manipulation in LiDAR-based Object Detection Supplemental File

We illustrate our voxel division strategies in Fig. 1. For the vehicle, we divide the bounding box into 3× 2× 2 voxels along the length, width, and height. For Pedestrian we divide the box into 5 voxels by height. The clustering rules in our experiments. We cluster objects in the ground-truth database into groups by the distance fd, size fs, relative angle fa, and occupancy ratio fo. In our experiments, (1) the distances of objects are divided into [0m, 30m), [30m, 50m), and [50m,+∞). (2) Following [1], the sizes of the vehicles are divided into [0m, 4m), [4m, 8m), and [8m,+∞). (3) For the relative angle fa, we divide fa(modπ2 ) of the vehicles into [0, π6 ), [ π 6 , π 3 ), and [ π 3 , π 2 ]. (4) The occupancy ratios of the objects range from 0 to 1. We divide them into [0, 0.2), [0.2, 0.4), [0.4, 0.6), [0.6, 0.8), and [0.8, 1]. Importantly, in the Tab. 1 of our main paper, we use the distance and occupancy ratio to cluster pedestrians. In this way, the pedestrians are clustered into 15 groups. In the Tab. 2 of our main paper, all of the factors are utilized and the vehicles are clustered into 135 groups. In the Tab. 3 of our main paper, different combinations of (1), (2), (3), and (4) are implemented for clustering the vehicle class.
Rate-limited Quantum-to-Classical Optimal Transport: A Lossy Source Coding Perspective

We consider the rate-limited quantum-to-classical optimal transport in terms of output-constrained rate-distortion coding for discrete quantum measurement systems with limited classical common randomness. The main coding theorem provides the achievable rate region of a lossy measurement source coding for an exact construction of the destination distribution (or the equivalent quantum state) while maintaining a threshold of distortion from the source state according to a generally defined distortion observable. The constraint on the output space fixes the output distribution to an i.i. d. predefined probability mass function. Therefore, this problem can also be viewed as information-constrained optimal transport which finds the optimal cost of transporting the source quantum state to the destination state via an entanglement-breaking channel with limited communication rate and common randomness.1
Capacity-Distortion Tradeoff of Noisy Gaussian State Amplification

The problem of joint information and noisy Gaussian state amplification is investigated in this paper. The optimal capacity-distortion tradeoff is characterized. For the achievability part, the Gelfand-Pinsker scheme is evaluated using minimum mean squared error of Gaussian random variables. For the converse part, Cauchy-Schwartz inequality is invoked to transform the optimal linear estimation into a canonical form.
CLOTH4D: A Dataset for Clothed Human Reconstruction

Clothed human reconstruction is the cornerstone for creating the virtual world. To a great extent, the quality of recovered avatars decides whether the Metaverse is a passing fad. In this work, we introduce CLOTH4D, a clothed human dataset containing 1,000 subjects with varied appearances, 1,000 3D outfits, and over 100,000 clothed meshes with paired unclothed humans, to fill the gap in large-scale and high-quality 4D clothing data. It enjoys appealing characteristics: 1) Accurate and detailed clothing textured meshes-all clothing items are manually created and then simulated in professional software, strictly following the general standard in fashion design. 2) Separated textured clothing and under-clothing body meshes, closer to the physical world than single-layer raw scans. 3) Clothed human motion sequences simulated given a set of 289 actions, covering fundamental and complicated dynamics. Upon CLOTH4D, we novelly designed a series of temporally-aware metries to evaluate the temporal stability of the generated 3D human meshes, which has been over-looked previously. Moreover, by assessing and retraining current state-of-the-art clothed human reconstruction methods, we reveal insights, present improved performance, and propose potential future research directions, confirming our dataset's advancement. The dataset is available at.
Design and Analysis of an Antenna using Ring Slotted Rectangular Reflector with Double Substrate for 5G mm-Wave Application

This work aims to provide a high-gain antenna with good efficiency using ring-slotted rectangular metamaterial reflectors array with a double-layer substrate for 5G wireless application. The proposed antenna is designed on a compact Rogers Substrate Rt-5880 with a relative permittivity εr of 2.2 and a loss tangent δ value of 0.0009. One of the unique characteristics of the suggested antenna is the very high gain of 13.21 dBi with 91.7% efficiency at the resonance frequency of 30 GHz. The proposed design's resonance frequency is 30 GHz, which is enough spectrum for error-free transmission. Ring-slotted rectangular metamaterial reflectors have been used on top to achieve the desired performance. The simulation findings meet the requirements for a reflection coefficient value of less than 10 dB. There is a directed radiation pattern. The suggested architecture is identified as a promising 5G wireless communication option by all these alluring performance criteria.
Running Mechanics of the New Zealand sea lion (Phocarctos hookeri): changes with speed and size

iii Acknowledgements iv List of Figures vii List of Tables ix List of Appendices x Chapter 1: Introduction 1 1.1 Locomotion in pinnipeds 1 1.2 Locomotor kinematics of terrestrial quadrupedal mammals 3 1.3 Terrestrial locomotion of otariids 10 1.4 New Zealand sea lion 11 1.5 Aims 12 Chapter 2: Methods 13 2.1 Study location and study animals 13 2.2 Video trials 13 2.3 Video processing 17 2.4 Kinematic parameters 22 2.5 Statistical analysis 24 Chapter 3: Results 27 3.1 Number of individual and video trials 27 3.2 Gait plot parameters 27 3.3 Stride length and stride frequency 32 3.4 Swing and stance duration 38
ANALYSIS OF FARMERS' PERCEPTION OF CLIMATE CHANGES AND ADAPTATION STRATEGIES

questionnaires and Cronbach's alpha coefficients for variables of the second, the third and the fourth parts were calculated, 0.79, 0.88, 0.91 respectively. This indicated that the questionnaire was highly reliable. Findings of farmer perceptions regarding changes in the climate indicated that most farmers perceived that the temperature distribution has undergone a significant shift in addition to an overall increase in temperatures. By contrast almost none believed they had decreased.
Neumann Network with Recursive Kernels for Single Image Defocus Deblurring

Single image defocus deblurring (SIDD) refers to recovering an all-in-focus image from a defocused blurry one. It is a challenging recovery task due to the spatially-varying defocus blurring effects with significant size variation. Motivated by the strong correlation among defocus kernels of different sizes and the blob-type structure of defocus kernels, we propose a learnable recursive kernel representation (RKR) for defocus kernels that expresses a defocus kernel by a linear combination of recursive, separable and positive atom kernels, leading to a compact yet effective and physics-encoded parametrization of the spatially-varying defocus blurring process. Afterwards, a physics-driven and efficient deep model with a cross-scale fusion structure is presented for SIDD, with inspirations from the truncated Neumann series for approximating the matrix inversion of the RKR-based blurring operator. In addition, a reblurring loss is proposed to regularize the RKR learning. Extensive experiments show that, our proposed approach significantly outperforms existing ones, with a model size comparable to that of the top methods.
BFC+: Analysis and Improvement of the Congestion Control Algorithm BFC

BFC is a per-flow per-hop congestion control method published in NSDI in 2022. BFC utilizes router support for multiple queues to perform per-flow per-hop level traffic scheduling within the network to maximize link utilization without requiring endpoint participation. A necessary condition for BFC to perform optimally is that routers must have sufficient buffer space and be able to truly isolate multiple flows within the same router. However, our experiments found that BFC's queue allocation algorithm cannot always effectively isolate multiple flows. BFC determines whether a queue is available by checking if the queue is empty, but in actual networks, it is common for queues to be empty during the absence of congestion or in the early stages of congestion. This can mistakenly allocate multiple flows to the same queue, causing BFC's scheduling ability to not perform optimally. This paper solves this problem and proposes an improved method called BFC+. Our experiments show that compared to BFC, BFC+'s queue allocation is more reasonable and has better performance.
Actuation Mechanism and Control of Quadrupedal Robots: A Review

Quadrupedal robots have been an active research area since the 1980s due to their promising potential in rough terrain and real-world navigation compared with wheeled or tracked robots. While many reviews have been written in this field, they are mostly about specific sub-categories. This paper aims to provide a broad entry point for researchers to understand the main technologies of quadrupedal robots, and also gain insight on recent breakthroughs by discussing both the actuation design and control methods of quadrupedal robots. This paper also proposes some unique views on specific challenges this research field is facing, such as crowd navigation and simulation challenges.
Gas Stove Burner Shape with Inductive Tailed Rotational Symmetric Metamaterial Absorber for C and X Band Application

This paper presents a multiband metamaterial (MM) absorber that displays the metamaterial characteristics at each resonance frequency. Broadband absorption is achieved in the X band at the range of 10.373 GHz to 10.825 GHz. Four resonances gain in C and X band frequency with single negative (SNG) MM properties at the frequencies of 4. 12, 5.776, 10.472, and 10.696 GHz, where the maximum absorptions are 99.4%, 99.9%, 95%, and 90.4% respectively. At last, compared with recent work on MM concerning the highest angle insensitive in Transverse Electromagnetic (TEM) mode, broadband absorption, compact size identifies it as a more useful ideal absorber.
Research on SWCNT non-contact strain sensor based on photoluminescence principle

Developing an optical full field non-contact strain sensor based on the spectral shift characteristics of single walled carbon nanotube (SWCNT), combining with the principle of photoluminescence is an emerging application field of SWCNT material that has important application prospects. The current paper analyzed the photoluminescence principle of SWCNT material, and discussed the optical path for the fluorescence excitation and acquisition of the SWCNT. Selection of the optical components in the optical path are completed. Then, based on the theory of polarize light, the fluorescence excitation of SWCNT and the controllable adjustment of polarize light in fluorescence spectra are studied. Experimental results indicate that the controllable adjustment accuracy of the polarize light is high, which conforms to Malus's law.
Integrated Healthcare for Cripple and Disabled Person in Home or Hospital

Cripples are those who cannot move and can not do their work by themselves, it is necessary to look after them for their emergency need. Due to lack of manpower in hospitals, an attendant can not stay with the patient all the time. Nowadays, the family members are also busy with various activities at home and outside, in such a situation it is difficult to take care of the patient round the clock. This paper aims to provide healthcare to disabled patient using the proposed integrated healthcare device, where the healthcare may be considered and the patient can request or command for his necessary items like medicine, food, water, emergency, etc. by voice or gesture message. Patient's bed is continuously monitored to see if it is fresh or wet. The attendant does not need to stay all the time with the patient. Patient data is stored in a cloud database so that the remote doctor can provide consultation or treatment to the patient through an Android App.
Optimal Multidimensional Differentially Private Mechanisms in the Large-Composition Regime

We construct vector differentially-private (DP) mechanisms that are asymptotically optimal in the limit of the number of compositions growing without bound. First, we derive via the central limit theorem a reduction from DP to a KL-divergence minimization problem. Second, we formulate the general theory of spherically-symmetric DP mechanisms in the large-composition regime. Specifically, we show that additive, continuous, spherically-symmetric DP mechanisms are optimal if one considers a spherically-symmetric cost (e.g., bounded noise variance) and an ℓ2 sensitivity metric. We then formulate a finite-dimensional problem that produces noise distributions that can get arbitrarily close to optimal among monotone mechanisms. Finally, we demonstrate numerically that our proposed mechanism achieves better DP parameters than the vector Gaussian mechanism for the same variance constraint.
Assessment of Parkinson's Motor Severity by Multi-feature and Multi-scale Motion Convolutional Neural Network with Positional Encoding

Parkinson's disease is one of the most common neurodegenerative diseases. Its main clinical manifestations are a variety of movement disorders, including abnormal gait, which seriously affect the performance of motor function, leading to shaking hands at rest, bradykinesia, myotonia, and difficulty in maintaining postural balance. The current work focuses on the methods of wearable devices and non-wearable devices to evaluate sports injuries. However, the network models based on the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) to quantify the severity of Parkinson's disease injuries have problems such as incomplete generalization of input features, insufficient feature extraction of network models, and imbalance between recognition accuracy and model size. A lightweight and high-precision convolutional neural network model is essential for accurate and quantitative assessment of disease progression and for slowing or preventing further disease progression. Regarding the above issues, we use a computer vision-based model to observe video recordings of the human body, extract their 3D body skeletons, track them through time, and input them into our proposed a Triple-feature Triple-motion Convolutional Neural Network (PE-TTNET) with Positional encoding for training. We position encode the triple scale features and classify the gait severity of PD patients based on MDS-UPDRS. The experimental results indicate that PE-TTNET achieved better results compared to previous network models in testing the clinical dataset we collected, with higher Fl-score and average accuracy in classifying the severity level of Parkinson's disease.
Copper and Zinc, Biological Role and Significance of Copper/Zinc Imbalance

The human body has an elaborate system for managing and regulating the amount of key trace metals circulating in blood and stored in cells. Nutrient metals from our diet are incorporated into blood if blood levels are depleted, transported into cells if cellular levels are inadequate, or excreted if blood and cell levels are sufficient or overloaded. When this system fails to function properly, abnormal levels and ratios of trace metals can develop. One of the most common trace-metal imbalances is elevated copper and depressed zinc. The ratio of copper to zinc is clinically more important than the concentration of either of these trace metals [1].