Miracle-dz/UniFAR-FTUs huggingface dataset:
Probing Channel Balances in the Lightning Network

As Lightning network payments are neither broadcasted nor publicly stored. Thus LN has been seen not only as scalability but also as privacy solution for Bitcoin. The protocol guarantees that only the latest channel state can be confirmed on channel closure. LN nodes gossip about channels available for routing and their total capacities. To issue a (multi-hop) payment, the sender creates a route based on its local knowledge of the graph. As local channel balances are not public, payments often fail due to insufficient balance at an intermediary hop. In that case, the payment is attempted along multiple routes until it succeeds. This constitutes a privacy-efficiency tradeoff: hidden balances improve privacy but hinder routing efficiency. In this work, we show that an attacker can easily discover channel balances using probing. This takes under a minute per channel and requires moderate capital commitment and no expenditures. We describe the algorithm and test our proof-of-concept implementation on Bitcoin's testnet. We argue that LN's balance between privacy and routing efficiency is suboptimal: channel balances are neither well protected nor utilized. We outline two ways for LN to evolve in respect to this issue. To emphasize privacy, we propose a modification of error handling that hides details of the erring channel from the sending node. This would break our probing technique but make routing failures more common, as the sender would not know which channel from the attempted route has failed. To improve efficiency, we propose a new API call that would let the sender query balances of channels that it is not a party of. We argue that combining these approaches can help LN take the best of both worlds: hide private data when feasible, and utilize public data for higher routing efficiency.
The Use of the Time Diary Method to Explore Academic Time Management: Insights From an Australian University

URLs: The Use of the Time Diary Method to Explore Academic Time Management: Insights From an Australian University Author(s): Duncan, R.G. ; Krivokapic-Skoko, B. ; Tilbrook, K.E. European Conference on Research Methodology for Business and Management Studies 2012 28-29 June 2012 Rachel McClean 199 206 Academics and universities have an interest in tracking the tasks and workloads of academics in the areas of teaching, research and administration, but do academics and their employers know how many hours a week an academic engages in particular tasks? We discuss the on-going development of an electronic time diary tool to measure an academic¿s teaching, research and administrative tasks. Our preliminary findings suggest that time spent communicating with students is now a significant portion of . Location: Univeristy of Bolton, UK http://researchoutput.csu.edu.au/R/-? func=dbin-jump-full&object_id=40911&local_base=GEN01-CSU01 PL: FT: Publisher: Academic Publishing Inetrnational Limited Place of Publication: Reading, UK The Use of the Time Diary Method to Explore Academic Time Management: Insights From an Australian University Dr Branka Krivokapic-Skoko, School of Management and Marketing, Charles Sturt University, Bathurst, 2795 NSW, Australia bkrivokapic@csu.edu.au Dr Roderick Duncan, School of Finance and Accounting, Charles Sturt University, Bathurst, 2795 NSW, Australia rduncan@csu.edu.au Dr. Kerry Tilbrook, School of Management and Marketing, Charles Sturt University, Bathurst, 2795 NSW, Australia ktilbrook@csu.edu.au
Personal Health Knowledge Graphs for Patients

Existing patient data analytics platforms fail to incorporate information that has context, is personal, and topical to patients. For a recommendation system to give a suitable response to a query or to derive meaningful insights from patient data, it should consider personal information about the patient's health history, including but not limited to their preferences, locations, and life choices that are currently applicable to them. In this review paper, we critique existing literature in this space and also discuss the various research challenges that come with designing, building, and operationalizing a personal health knowledge graph (PHKG) for patients.
Large-scale behavior of a particle system with mean-field interaction: Traveling wave solutions

Abstract We use probabilistic methods to study properties of mean-field models, which arise as large-scale limits of certain particle systems with mean-field interaction. The underlying particle system is such that n particles move forward on the real line. Specifically, each particle ‘jumps forward’ at some time points, with the instantaneous rate of jumps given by a decreasing function of the particle’s location quantile within the overall distribution of particle locations. A mean-field model describes the evolution of the particles’ distribution when n is large. It is essentially a solution to an integro-differential equation within a certain class. Our main results concern the existence and uniqueness of—and attraction to—mean-field models which are traveling waves, under general conditions on the jump-rate function and the jump-size distribution.
Effect of Obesity on Static Strength and Explosive Power Among Egyptian Children Living In Egypt Versus Egyptian Children Living In Saudi Arabia

Purposes: To determine the correlation between body mass index (BMI) and static strength (SS) and explosive power (EP), and to show the differences in all of them among Egyptian children living in Egypt and kingdom of Saudi Arabia (KSA). Subjects and procedures: 110 Egyptian children (10-15 years old) were selected from Egypt and KSA schools and classified according to BMI into obese, overweight, and normal group. Assessment of SS and EP have done using Hand Grip Strength and Standing Broad Jump tests respectively. Results: Significant negative correlations between BMI and EP and positive correlations between BMI and SS has found in all groups of Egyptian children in Egypt (EC-Egypt) and in KSA (EC-KSA) (P≤0.01). The EP scores were much higher in normal group compared to the overweight and obese children and the SS scores were much higher in obese and overweight groups compared to the normal children in both groups of EC-Egypt and EC-KSA (P≤0.01). The EP and SS scores were much higher in all groups of EC-Egypt compared to the corresponding groups of EC-KSA (P≤0.01) except between obese groups of EC-Egypt and EC-KSA have no differences (P>0.05). Conclusion: The overweight and obese Egyptian children who live in Egypt and KSA have a decrease in physical performance in activities requiring forward acceleration and antigravity movement of the total body weight. In contrast, overweight and obese children showed greater static strength. Concentration on non-weight bearing activities during intervention or physical education session is recommended with obese children.
Combination Networks With End-User-Caches: Novel Achievable and Converse Bounds Under Uncoded Cache Placement

Caching is an efficient way to reduce network traffic congestion during peak hours by storing some content at the users’ local caches. For the shared-link network with end-user-caches, Maddah-Ali and Niesen proposed a two-phase coded caching strategy. In practice, users may communicate with the server through intermediate relays. This paper studies the tradeoff between the memory size <inline-formula> <tex-math notation="LaTeX">${\mathsf M}$ </tex-math></inline-formula> and the network load <inline-formula> <tex-math notation="LaTeX">${\mathsf R}$ </tex-math></inline-formula> for the networks where a server with <inline-formula> <tex-math notation="LaTeX">${\mathsf N}$ </tex-math></inline-formula> files is connected to <inline-formula> <tex-math notation="LaTeX">${\mathsf H}$ </tex-math></inline-formula> relays (without caches), which in turn are connected to <inline-formula> <tex-math notation="LaTeX">${\mathsf K}$ </tex-math></inline-formula> users equipped with caches of <inline-formula> <tex-math notation="LaTeX">${\mathsf M}$ </tex-math></inline-formula> files. When each user is connected to a different subset of <inline-formula> <tex-math notation="LaTeX">${\mathsf r}$ </tex-math></inline-formula> relays, i.e., <inline-formula> <tex-math notation="LaTeX">${\mathsf K}= \binom {\mathsf H}{\mathsf r}$ </tex-math></inline-formula>, the system is referred to as a <italic>combination network with end-user-caches</italic>. In this work, converse bounds are derived for the practically motivated case of <italic>uncoded</italic> cache contents, that is, bits of the various files are directly pushed into the user caches without any coding. In this case, once the cache contents and the users’ demands are known, the problem reduces to a general index coding problem. This paper shows that relying on a well-known “acyclic index coding converse bound” results in converse bounds that are not tight for combination networks with end-user-caches. A novel converse bound that leverages the network topology is proposed, which is the tightest converse bound known to date. As a result of independent interest, an inequality that generalizes the well-known sub-modularity of entropy is derived. Several novel caching schemes are proposed, based on the Maddah-Ali and Niesen cache placement. These schemes leverage the structure of the combination network or/and perform interference elimination at the end-users. The proposed schemes are proved: (i) to be (order) optimal for some <inline-formula> <tex-math notation="LaTeX">$({\mathsf N}, {\mathsf M}, {\mathsf H}, {\mathsf r})$ </tex-math></inline-formula> parameters regimes under the constraint of uncoded cache placement, and (ii) to outperform the state-of-the-art schemes in numerical evaluations.
The Impact of Dynamics of Collaborative Software Engineering on Introverts: A Study Protocol

Background: Collaboration among software engineers through face-to-face discussions in teams has been promoted since the adoption of agile methods. However, these discussions might demote the contribution of software engineers who are introverts, possibly leading to sub-optimal solutions and creating work environments that benefit extroverts. Objective: We aim to evaluate whether providing software engineers with time to work individually and reason about a collective problem is a setting that makes intro-verts more comfortable to interact and contribute more, ultimately leading to better solutions. Method: We plan to conduct a between-subjects study, with teams in a control group that design a software architecture in a team discussion meeting and teams in a treatment group in which subjects work individually before engaging in a meeting. We will assess and compare the amount of contribution of introverts, their subjective experiences, and the designed solutions. Limitations: As extroverts will be present in both groups, we will not be able to conclude that better solutions are solely due to the increased participation of introverts. The analyses of their subjective experience and amount of contributions might provide evidence to suggest the reasons for observed differences.
Exact marginal inference in Latent Dirichlet Allocation

Assume we have potential "causes" $z\in Z$, which produce "events" $w$ with known probabilities $\beta(w|z)$. We observe $w_1,w_2,. ,w_n$, what can we say about the distribution of the causes? A Bayesian estimate will assume a prior on distributions on $Z$ (we assume a Dirichlet prior) and calculate a posterior. An average over that posterior then gives a distribution on $Z$, which estimates how much each cause $z$ contributed to our observations. This is the setting of Latent Dirichlet Allocation, which can be applied e.g. to topics "producing" words in a document. In this setting usually the number of observed words is large, but the number of potential topics is small. We are here interested in applications with many potential "causes" (e.g. locations on the globe), but only a few observations. We show that the exact Bayesian estimate can be computed in linear time (and constant space) in $|Z|$ for a given upper bound on $n$ with a surprisingly simple formula. We generalize this algorithm to the case of sparse probabilities $\beta(w|z)$, in which we only need to assume that the tree width of an "interaction graph" on the observations is limited. On the other hand we also show that without such limitation the problem is NP-hard.
Obstacle Tower Without Human Demonstrations: How Far a Deep Feed-Forward Network Goes with Reinforcement Learning

The Obstacle Tower Challenge is the task to master a procedurally generated chain of levels that subsequently get harder to complete. Whereas the most top performing entries of last year’s competition used human demonstrations or reward shaping to learn how to cope with the challenge, we present an approach that performed competitively (placed 7th) but starts completely from scratch by means of Deep Reinforcement Learning with a relatively simple feed-forward deep network structure. We especially look at the generalization performance of the taken approach concerning different seeds and various visual themes that have become available after the competition, and investigate where the agent fails and why. Note that our approach does not possess a short-term memory like employing recurrent hidden states. With this work, we hope to contribute to a better understanding of what is possible with a relatively simple, flexible solution that can be applied to learning in environments featuring complex 3D visual input where the abstract task structure itself is still fairly simple.
Likelihood landscape and maximum likelihood estimation for the discrete orbit recovery model

We study the nonconvex optimization landscape for maximum likelihood estimation in the discrete orbit recovery model with Gaussian noise. This is a statistical model motivated by applications in molecular microscopy and image processing, where each measurement of an unknown object is subject to an independent random rotation from a known rotational group. Equivalently, it is a Gaussian mixture model where the mixture centers belong to a group orbit.
Optimised Convolutional Neural Networks for Heart Rate Estimation and Human Activity Recognition in Wrist Worn Sensing Applications

Wrist-worn smart devices are providing increased insights into human health, behaviour and performance through sophisticated analytics. However, battery life, device cost and sensor performance in the face of movement-related artefact present challenges which must be further addressed to see effective applications and wider adoption through commoditisation of the technology. We address these challenges by demonstrating, through using a simple optical measurement, photoplethysmography (PPG) used conventionally for heart rate detection in wrist-worn sensors, that we can provide improved heart rate and human activity recognition (HAR) simultaneously at low sample rates, without an inertial measurement unit. This simplifies hardware design and reduces costs and power budgets. We apply two deep learning pipelines, one for human activity recognition and one for heart rate estimation. HAR is achieved through the application of a visual classification approach, capable of robust performance at low sample rates. Here, transfer learning is leveraged to retrain a convolutional neural network (CNN) to distinguish characteristics of the PPG during different human activities. For heart rate estimation we use a CNN adopted for regression which maps noisy optical signals to heart rate estimates. In both cases, comparisons are made with leading conventional approaches. Our results demonstrate a low sampling frequency can achieve good performance without significant degradation of accuracy. 5 Hz and 10 Hz were shown to have 80.2% and 83.0% classification accuracy for HAR respectively. These same sampling frequencies also yielded a robust heart rate estimation which was comparative with that achieved at the more energy-intensive rate of 256 Hz.
Deep Learning on Knowledge Graph for Recommender System: A Survey

Recent advances in research have demonstrated the effectiveness of knowledge graphs (KG) in providing valuable external knowledge to improve recommendation systems (RS). A knowledge graph is capable of encoding high-order relations that connect two objects with one or multiple related attributes. With the help of the emerging Graph Neural Networks (GNN), it is possible to extract both object characteristics and relations from KG, which is an essential factor for successful recommendations. In this paper, we provide a comprehensive survey of the GNN-based knowledge-aware deep recommender systems. Specifically, we discuss the state-of-the-art frameworks with a focus on their core component, i.e., the graph embedding module, and how they address practical recommendation issues such as scalability, cold-start and so on. We further summarize the commonly-used benchmark datasets, evaluation metrics as well as open-source codes. Finally, we conclude the survey and propose potential research directions in this rapidly growing field.
Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset

Classifier metrics, such as accuracy and F-measure score, often serve as proxies for performance in downstream tasks. For the case of generative systems that use predicted labels as inputs, accuracy is a good proxy only if it aligns with the perceptual quality of generated outputs. Here, we demonstrate this effect using the example of automatic drum transcription (ADT). We optimize classifiers for downstream generation by predicting expressive dynamics (velocity) and show with listening tests that they produce outputs with improved perceptual quality, despite achieving similar results on classification metrics. To train expressive ADT models, we introduce the Expanded Groove MIDI dataset (E-GMD), a large dataset of human drum performances, with audio recordings annotated in MIDI. E-GMD contains 444 hours of audio from 43 drum kits and is an order of magnitude larger than similar datasets. It is also the first human-performed drum dataset with annotations of velocity. We make this new dataset available under a Creative Commons license along with open source code for training and a pre-trained model for inference.
A Low-Complexity Space Time Block Codes Detection for Cell Free Massive MIMO Systems

The next generation of communication networks should deliver higher spectral efficiency and data rates for novel demands and utilizations. One of the techniques used in the fifth generation(5G) of communication networks is the Cell-free massive MIMO, where segmentation into cells is not established. Here, the BS antennas are distributed in the environment and the users can receive infromation from all the BSs, simultaneously. In this paper, we investigate a multiuser cell free massive MIMO system with linear decoders. We propose the Neumann series based inverse matrix approximation method in order to reduce the ZF and MMSE decoders' hardware and computational complexities. In this regard, each user is equipped with two antennas, and also the space time block codes (STBCs) are used in the uplink transmission for diversity gain. Besides, we obtain a lower bound on the throughput performance of the ZF decoder. Also, the performance of the proposed system is evaluated in terms of the spectral efficiency and the bit error rate (BER). The computer simulation results confirm the superior performance of the proposed system compared to that of single-antenna systems. Furthermore, the BER performance of the proposed method is close to that of the exact method.
Medical errors in the intensive care unit : Can we find the black box before the patient crashes ?

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Genetic risk factors for acute respiratory distress syndrome : What ’ s the right direction to take ?

should be obtained if indicated but can be helpful in assessing volume status or to rule out pneumonia, although consolidations may not show on chest radiograph in dehydrated patients (1). Can a protocol be perfect? No! It is not realistic to expect that all possible clinical conditions would be prespecified in a protocol. In this regard, it remains unclear how to interpret the fact that the protocol was mandatory. Does this mean that altering some treatment recommendations with respect to the individual patient and after good clinical judgment were not allowed? Can the results be explained by the fact that the patients in the postprotocol period were perhaps less sick than in the preprotocol period? This could be possible, although it seems rather unlikely. Moreover, illness severity scores are no predictors for intensive care unit or hospital length of stay in patients with DKA (15). Despite some weaknesses, in particular in the description of protocol development and implementation strategy, the message delivered by the study by Dr. Bull and colleagues (1) is that successful implementation of guidelines and protocols in daily medical practice can have significant positive effects. It’s time to work in harmony! Sandra Oeyen, MD Department of Intensive Care Ghent University Hospital Ghent, Belgium