Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Positive Affect on the Subjective Utility of Gains and Losses: It Is Just Not Worth the Risk

A modification of the procedure originally used by Davidson, Suppes, and Siegel (1956) to measure subjective utility was used to study the influence of positive affect on individuals' perceived value (utility) functions. Results indicated, as expected, that persons in whom positive affect had been induced showed a more negative subjective utility for losses than did controls. This indicates that losses seem worse to people who are feeling happy than to those in a control condition. The subjective utility functions of the two groups did not differ as much, however, when people were considering potential gain. Thus, at least in the situation tested in this study, potential gains did not seem to be more appealing (nor less so) for affect subjects than they did for controls. These findings are discussed in relation to theoretical issues in decision making and work suggesting that positive affect can pro- mote increased sensitivity to losses in situations of potential meaningful loss. Recent
Biodynamics Delta efficiencies of running and cycling

BIJKER, K. E. , G. DE GROOT, and A. P. HOLLANDER. Delta efficiencies of running and cycling. Med. Sci. Sports Exerc. , Vol. 33, No. 9, 2001, pp. 1546–1551. Purpose: The purpose of the present study was to compare delta efficiencies of running with cycling, while several factors that can possibly influence delta efficiency were excluded. Methods: Twelve subjects performed a submaximal running and cycling test on subsequent days. Delta efficiencies of running and cycling were compared at equal metabolic intensities. Furthermore, rest periods were included in the protocol to avoid fatigue. Pedaling and stride frequencies were held constant during the tests. Finally, the influence of two ways of applying extra external load (inclination of treadmill and horizontal impeding forces) on the delta efficiency of running and cycling was investigated. Results: The results of the present study show that the mean delta efficiency of running (45.5%) is still significantly higher than the mean delta efficiency of cycling (25.7%). The way extra external load is applied does not influence delta efficiency. Conclusion: The way of loading and the difference in metabolic intensity can be excluded as causes for the observed difference in delta efficiency between running and cycling. It is suggested that a different contribution in the metabolic load attributable to muscular activity of the arms and/or trunk that does not directly contribute to the work needed to overcome the amount of applied external load may be a relevant factor.
Communication Efﬁcient Federated Learning with Secure Aggregation and Differential Privacy

Optimizing the privacy-utility-communication tradeoff is a key challenge for federated learning. Under distributed differential privacy (DP) via secure aggregation (SecAgg), we prove that the worst-case communication cost per client must be at least Ω (cid:16) d log (cid:16) n 2 ε 2 d (cid:17)(cid:17) to achieve O (cid:0) dn 2 ε 2 (cid:1) centralized error, which matches the error under central DP. Despite this bound, we leverage the near-sparse structure of model updates, evidenced through recent empirical studies, to obtain improved tradeoffs for distributed DP. In particular, we leverage linear compression methods, namely sketching, to attain compression rates of up to 50 × with no signiﬁcant decrease in model test accuracy achieving a noise multiplier 0 . 5 . Our work demonstrates that fundamental tradeoffs in differentially private federated learning can be drastically improved in practice.
Visualizing High-Dimensional Hyperbolic Data

Hierarchies can often be found in real-world data and semantics. Hyperbolic space can naturally embed hierarchical structures, making it an attractive alternative to traditional Euclidean or spherical space when learning representations. Many popular machine learning methods and architectures have been adapted to embed and use hierarchical data, leading to significant improvements over Euclidean counterparts. Additionally, high-dimensional embeddings can capture more information than low-dimensional ones, which can also lead to performance improvements. While high-dimensional hyperbolic embeddings can lead to better representations, visualizing them in a human understandable way can be challenging. Visualizations of learned embeddings are important for both understanding the representation model and characteristics of the data, so for this and other reasons, many hyperbolic models do not leverage high-dimensional embeddings. To address this problem, we propose CO-SNE which extends the Euclidean space visualization tool, t-SNE to hyperbolic space. CO-SNE is able to deflate high-dimensional embeddings into low-dimensional space without losing their hyperbolic characteristics. We present results that show CO-SNE outperforms previous methods on visualizing high-dimensional hyperbolic data, including real-life biological data and learned hyperbolic embeddings. We also show the the hierarchical nature of image segmentation by visualizing the results of hierarchical semantic segmentation. Overall, CO-SNE is able to successfully visualize high-dimensional hyperbolic data, resulting in visualizations that can present insight on the data and the methods generating the data.
Implantable neural spheroid networks utilizing a concave microwell array

The goal of this study was to create pre-formed neural spheroid networks (NSN) on a polydimethyl siloxane (PDMS) concave microwell array for eventual implantation into the rat brain. Recent studies have shown that stem cells have great potential in treating various neurological insults of the central nervous system, ranging from traumatic brain and spinal cord injury, to neurodegenerative disorders. However, the use of stem cell lines in research are controversial due to the method of obtaining cells, in their formation of teratomas and degeneration into cancer cells, their non-specific differentiation, and lastly in their inability to control the location of neural connections. A novel approach to address this issue utilizes pre-formed neural networks consisting of neural spheroids on polymer scaffolds for the implantation into the rat brain. Yet, it was observed that the cylindrical shape of the wells hindered the transfer process. This study aimed to overcome the lack of neural spheroid network detachment by utilizing concave well structures, using a simple method developed in this laboratory.
New Scheduling Models for Cloud Computing Systems: Analysis, Algorithms and Applications

Scientific research in the area of mathematically orientated scheduling problems has started about sixty years ago by seminal papers by Johnson and Bellman. In the last decade developing scheduling algorithms for distributed computing systems has become a centre of interest for practical applications of scheduling problems. However, most of the applied scheduling models for distributed computing systems do not fully make use of existing mathematically orientated scheduling theory developed within the last sixty years. The goal of this thesis is to help bridging the gap between mathematically orientated scheduling theory and applied scheduling models for distributed computed systems. In the thesis we first give a definition of cloud computing which is a specialised form of distributed computing. Then we present some important features that appear in cloud computing that are worth to be modelled in mathematically orientated scheduling models. These features include objectives like the energy consumption or the reliability as well as restrictions like the immediate start condition or the consideration of several different computational resources. We continue with a short review of the literature on cloud computing where we highlight differences in the literature on applied scheduling models and mathematically orientated scheduling models, respectively. Thereafter we present a new energy model in which the energy consumption is calculated based on the cores of a processor. Then we give an overview of existing literature on energy efficient scheduling algorithms. Finally, we propose a scheduling model which combines the immediate start condition with the objectives of minimising the total flow time and the energy consumption. For the special cases with a single processor or with equal-work jobs algorithms with running time O(n log n) are suggested where n denotes the number of jobs. For the general case, the complexity status of minimising the total flow time while respecting an upper limit on the consumed energy remained open.
Reinforcement Learning from Optimization Proxy for Ride-Hailing Vehicle Relocation

Idle vehicle relocation is crucial for addressing demand-supply imbalance that frequently arises in the ride-hailing system. Current mainstream methodologies - optimization and reinforcement learning - suffer from obvious computational drawbacks. Optimization models need to be solved in real-time and often trade off model fidelity (hence quality of solutions) for computational efficiency. Reinforcement learning is expensive to train and often struggles to achieve coordination among a large fleet. This paper designs a hybrid approach that leverages the strengths of the two while overcoming their drawbacks. Specifically, it trains an optimization proxy, i.e., a machine-learning model that approximates an optimization model, and then refines the proxy with reinforcement learning. This Reinforcement Learning from Optimization Proxy (RLOP) approach is computationally efficient to train and deploy, and achieves better results than RL or optimization alone. Numerical experiments on the New York City dataset show that the RLOP approach reduces both the relocation costs and computation time significantly compared to the optimization model, while pure reinforcement learning fails to converge due to computational complexity.
Human resource risk assessment in selected hotels in the West Pomeranian Voivodeship

Correspondence to: Anna Tokarz-Kocik Uniwersytet Szczeciński Instytut Zarządzania ul. Cukrowa 8 71-004 Szczecin, Poland Tel. : +48 91 444 31 67 Abstract: The objective of the article is to assess human resource risk in selected hotels. A theoretical part of the study is a result of the use of the desk research method. An empirical part includes the results of interviews with human resource managers conducted in five hotels located in the West Pomeranian Voivodeship and the assessment of the human resource risk level in the analyzed hotels. Respondents assessed two elements: the probability of occurring risk events and their impact on a hotel. The human resource risk level is a product of these two parameters expressed in points. Due to a limited number of research entities, the article involves the analysis of the case study. Therefore, the research findings cannot be generalized with regard to the whole industry. However, they can serve as the basis for a discussion on the phenomenon of human resource risk in the hotel industry and its impact on work efficiency, thus the functioning of the whole organization.
The Effect of Production of Paper and Paper Products and Gross National Product on Electricity Consumption

Anahtar Kelimeler Elektrik tüketimi, Gayri safi yurt içi hasıla, Kağıt ve kağıt ürünleri, Panel veri Özet: Enerji, ekonomik ve endüstriyel kalkınma için önemli ve üretim sürecinin devamı için vazgeçilmez bir üretim girdisidir. Kolay kullanım ve diğer avantajları sebebiyle ekonomik ve sosyal yaşamın oldukça geniş bir bölümünde yaygın olarak elektrik enerjisinden yararlanılmaktadır. Bu çalışmanın amacı, kağıt ve kağıt ürünleri üretimi ve ekonomik büyümenin bir göstergesi olan gayri safi milli hasılanın elektrik enerjisi tüketimi üzerindeki etkisini panel veri analizi ile incelemektir. Bu amaç doğrultusunda, Eurostat ve Wordbank’tan elde edilen 20062016 yılları arasında 20 OECD ülkesi için panel veri seti oluşturulmuştur. Model için yatay kesit bağımlılığı, değişen varyans ve otokorelasyon varsayımları sınanmış ve varsayımlardan sapma olduğu görülmüştür. Bu sapmalara dirençli Driscoll-Kraay tahmin edici kullanılarak parametreler tahmin edilmiştir. Analiz sonucunda, kağıt ve kağıt ürünleri üretiminde meydana gelecek %1’lik bir artış elektrik enerjisi tüketimini %0.22 oranında, kişi başı gayri safi milli hasıladaki %1’lik bir artışın ise elektrik tüketiminde yaklaşık olarak %0.62 dolayında arttırdığı bulgusuna ulaşılmıştır.
Diarrhea 1 None Symptom disappeared Unlikely related 11 Hepatic heman ‐ gioma 1 None Symptom disappeared Not related

Refractory functional dyspepsia (RFD) is diagnosed when symptoms persist for at least 6 months despite at least two medical treatments. No consensus treatment guidelines exist. The im‐ plicated causes of functional biliary dyspepsia are a narrowed cystic duct, Sphincter of Oddi dys‐ function, microlithiasis, and gallbladder dyskinesia. We investigated the treatment effects of litho‐ lytic agents. RFD patients were prospectively enrolled in six tertiary medical centers. All subjects took chenodeoxycholic and ursodeoxycholic acids (CNU) twice daily for 12 weeks. We monitored their medication adherence, laboratory results, and complications. The 7‐point global symptom scale test scores were determined before and after treatment. Of the 52 patients who were prospec‐ tively screened, 37 were included in the final analysis. The mean age was 51.3 years: 14 were males, and 23 were females. Before treatment, the mean number and duration of symptoms were 2.4 and 48.2 months, and a mean of 3.3 FD‐related drugs were taken. The mean CNU adherence was 95.3%. The mean global symptom scale score decreased from 5.6 pretreatment to 2.6 posttreatment. The symptom improvement rate was 94.6% (35 out of 37 patients). The only adverse event was mild diarrhea (10.8%) that was resolved after conservative management. Conclusions: CNU improved the symptoms of RFD patients who did not respond to conventional medications. Litholytic agents are good treatment options for patients with RFD and biliary dyspepsia secondary to biliary micro‐ lithiasis. Further prospective, large‐scale mechanistic studies are warranted.
Robust and Efficient Uncertainty Aware Biosignal Classification via Early Exit Ensembles

Ensembles of deep learning models can be used for estimating predictive uncertainty. Existing ensemble approaches, however, introduce a high computational and memory cost limiting their applicability to real-time biosignal applications (e.g. ECG, EEG). To address these issues, we propose early exit ensembles (EEEs) for estimating predictive uncertainty via an implicit ensemble of early exits. In particular, EEEs are a collection of weight sharing sub-networks created by adding exit branches to any backbone neural network architecture. Empirical evaluation of EEEs demonstrates strong performance in accuracy and uncertainty metrics as well as computation gain highlighting the benefit of combining multiple structurally diverse models that can be jointly trained. Compared to state-of-the-art baselines (with an ensemble size of 5), EEEs can improve uncertainty metrics up to 2× while providing test-time speed-up and memory reduction of approx. 5×. Additionally, EEEs can improve accuracy up to 3.8 percentage points compared to single model baselines.
Towards Automatically Repairing Compatibility Issues in Published Android Apps

The heavy fragmentation of the Android ecosystem has led to se-vere compatibility issues with apps, including those that crash at runtime or cannot be installed on certain devices but work well on other devices. To address this problem, various approaches have been proposed to detect and fix compatibility issues automatically. However, these all come with various limitations on fixing the com-patibility issues, e.g., can only fix one specific type of issues, cannot deal with multi-invocation issues in a single line and issues in re-leased apps. To overcome these limitations, we propose a generic approach that aims at fixing more types of compatibility issues in released Android apps. To this end, our prototype tool, Repair-Droid, provides a generic app patch description language for users to create fix templates for compatibility issues. The created tem-plates will then be leveraged by RepairDroid to automatically fix the corresponding issue at the bytecode level (e.g., right before users install the app). RepairDroid can support template creations for OS-induced, device-specific and inter-callback compatibility issues detected by three state-of-the-art approaches. Our experimental re-sults show that RepairDroid can fix 7,660 out of 8,976 compatibility issues in 1,000 randomly selected Google Play apps. RepairDroid is generic to configure new compatibility issues and outperforms the state-of-the-art on effectively repairing compatibility issues in released Android apps.
Objective models of EMG signals for cyclic processes such as a human gait

EMG signals are small potentials appearing at the surface of human skin during muscle work. They arise due to changes in the physiological state of cell membranes in the muscle fibers. They are characterized by a relatively low frequency range (500 Hz) and a low amplitude signal (of the order of μV), making it difficult to record. Raw EMG signal is inherently random shape. However we can distinguish certain features related to the activation of the muscles of a deterministic or quasi-deterministic associated with the movement and its parametric description. Objective models of EMG signals were created on the base of actual data obtained from the VICON system installed at the University of Physical Education in Warsaw. The object of research (healthy woman) moved repeatedly after a fixed track. On her body 35 reflective markers to record the gait kinematics and 8 electrodes to record EMG signals were placed. We obtained research data included more than 1,000 EMG signals synchronized with the phases of gait. Test result of the work is an algorithm for obtaining the average EMG signal received from the multiple registration gait cycles carried out in the same reproducible conditions. The method described in the article is essentially a pre-finding measurement data from the two quasi-synchronous signals at different sampling frequencies for further processing. This signal is characterized by a significant reduction of high frequency noise and emphasis on the specific characteristics of individual records found in muscle activity.
A LGORITHMIC H EAP L AYOUT M ANIPULATION IN THE L INUX K ERNEL

To evaluate the severity of a security vulnerability a security researcher usually tries to prove its exploitability by writing an actual exploit. In the case of buffer overflows on the heap, a necessary part of this is manipulating the heap layout in a way that creates an exploitable state, usually by placing a vulnerable object adjacent to a target object. This requires manual effort and extensive knowledge of the target. With a target as complex as the Linux kernel, this problem becomes highly non-trivial. At the current time, there has been little research in terms of employing algorithmic solutions for this. In this work, we present Kernel-SIEVE, a framework for evaluating heap layout manipulation algorithms that target the SLAB/SLUB allocator in the Linux kernel. Inspired by previous work that targets user-space allocators [33–35] it provides an interface for triggering allocations/deallocations in the kernel and contains a feedback loop that returns the resulting distance of two target objects. With this, we create the (to our knowledge) first performance benchmarks for heap layout manipulation algorithms in the Linux kernel. We present and evaluate two algorithms: A pseudo-random search, whose performance serves as a baseline, and KEvoHeap, a genetic algorithm based on Heelan’s EvoHeap [33,35]. We show that KEvoHeap is successful at creating the desired heap layout in all test cases and also surpasses the user-space performance benchmarks of EvoHeap. Finally, we discuss the challenges of applying these kinds of algorithms in real-world scenarios and weigh different possible approaches to tackle the problems that arise. Our research results are publicly available on GitHub [43].
(Gulf of Guinea, West Africa)

Allele frequencies for eight STRs (CD4, FES/FPS, MBPB, TH01, TP53, TPO, F13A1, VWA) were estimated from samples (sized between 279 and 328) of unrelated individuals born in S. TomeÂ e Prõ Âncipe (Gulf of Guinea, West Africa). # 2001 Elsevier Science Ireland Ltd. All rights reserved.
Exploiting Input Sanitization for Regex Denial of Service

Web services use server-side input sanitization to guard against harmful input. Some web services publish their sanitization logic to make their client interface more usable, e.g., allowing clients to debug invalid requests locally. However, this usability practice poses a security risk. Specifically, services may share the regexes they use to sanitize input strings - and regex-based denial of service (ReDoS) is an emerging threat. Although prominent service outages caused by ReDoS have spurred interest in this topic, we know little about the degree to which live web services are vulnerable to ReDoS. In this paper, we conduct the first black-box study measuring the extent of ReDoS vulnerabilities in live web services. We apply the Consistent Sanitization Assumption: that client-side sanitization logic, including regexes, is consistent with the sanitization logic on the server-side. We identify a service's regex-based input sanitization in its HTML forms or its API, find vulnerable regexes among these regexes, craft ReDoS probes, and pinpoint vulnerabilities. We analyzed the HTML forms of 1,000 services and the APIs of 475 services. Of these, 355 services publish regexes; 17 services publish unsafe regexes; and 6 services are vulnerable to ReDoS through their APIs (6 domains; 15 subdomains). Both Microsoft and Amazon Web Services patched their web services as a result of our disclosure. Since these vulnerabilities were from API specifications, not HTML forms, we proposed a ReDoS defense for a popular API validation library, and our patch has been merged. To summarize: in client-visible sanitization logic, some web services advertise Re-DoS vulnerabilities in plain sight. Our results motivate short-term patches and long-term fundamental solutions. “Make measurable what cannot be measured. ” -Galileo Galilei
Preparing Software Engineers to Develop Robot Systems

Robotics is a rapidly expanding field that needs software engineers. Most of our undergraduates, however, are not equipped to manage the unique challenges associated with the development of software for modern robots. In this work we introduce a course we have designed and delivered to better prepare students to develop software for robot systems. The course is unique in that: 1) it emphasizes the distinctive challenges of software development for robots paired with the software engineering techniques that may help manage those challenges, 2) it provides many opportunities for experiential learning across the robotics and software engineering interface, and 3) it lowers the barriers for learning how to build such systems. We describe the principles and innovations of the course, its content and delivery, and finish with the lessons we have learned.