Miracle-dz/UniFAR-FTUs huggingface dataset:
Thin polytopes: Lattice polytopes with vanishing local $h^*$-polynomial

In this paper we study the novel notion of thin polytopes: lattice polytopes whose local $h^*$-polynomials vanish. The local $h^*$-polynomial is an important invariant in modern Ehrhart theory. Its definition goes back to Stanley with fundamental results achieved by Karu, Borisov&Mavlyutov, Schepers, and Katz&Stapledon. The study of thin simplices was originally proposed by Gelfand, Kapranov and Zelevinsky, where in this case the local $h^*$-polynomial simply equals its so-called box polynomial. Our main results are the complete classification of thin polytopes up to dimension 3 and the characterization of thinness for Gorenstein polytopes. The paper also includes an introduction to the local $h^*$-polynomial with a survey of previous results.
Context Unaware Knowledge Distillation for Image Retrieval

Existing data-dependent hashing methods use large backbone networks with millions of parameters and are computationally complex. Existing knowledge distillation methods use logits and other features of the deep (teacher) model and as knowledge for the compact (student) model, which requires the teacher's network to be fine-tuned on the context in parallel with the student model on the context. Training teacher on the target context requires more time and computational resources. In this paper, we propose context unaware knowledge distillation that uses the knowledge of the teacher model without fine-tuning it on the target context. We also propose a new efficient student model architecture for knowledge distillation. The proposed approach follows a two-step process. The first step involves pre-training the student model with the help of context unaware knowledge distillation from the teacher model. The second step involves fine-tuning the student model on the context of image retrieval. In order to show the efficacy of the proposed approach, we compare the retrieval results, no. of parameters and no. of operations of the student models with the teacher models under different retrieval frameworks, including deep cauchy hashing (DCH) and central similarity quantization (CSQ). The experimental results confirm that the proposed approach provides a promising trade-off between the retrieval results and efficiency. The code used in this paper is released publicly at \url{https://github.com/satoru2001/CUKDFIR}.
Uncertainties in the eta-Nucleon Scattering Length and Effective Range

The coupled eta-N, pi-N, gamma-N system is described by a K-matrix method. The parameters in this model are adjusted to get an optimal fit to pi-N --->pi- N$, pi-N --->eta-N$, gamma-N--->pi-N and gamma-N --->eta-N data in an energy range of about 100MeV or so each side of the eta threshold. The outcome is the appearance of two solutions one which has an eta-nucleon scattering length (a) of about 1.0 fm and a second with a~0.2fm. However, the second solution has an unconventional non-Lorentian form for the T-matrix in the region of the 1535(20)MeV and 1650(30)MeV S-wave pi-N resonances.
Egyptian propolis and selenium nanoparticles against murine trichinosis: a novel therapeutic insight

Abstract Trichinosis is a serious zoonotic disease that causes human morbidity and mortality. New effective natural remedies with minimal side effects that are well tolerated are needed to treat both enteral and parenteral trichinosis. This study evaluated the efficacy of selenium (Se), Se nanoparticles (SeNPs) and Egyptian propolis compared with albendazole as antiparasitic, anti-inflammatory and anti-angiogenic agents for treating murine trichinosis. We used parasitological, histopathological and immunohistochemical assays, as well as scanning electron microscopy, to examine adult worms. Overall, 80 Swiss albino male mice were divided into eight groups, with ten mice in each group, as follows: negative control, positive control, albendazole, propolis, Se, combination of propolis and Se, SeNPs and combination of SeNPs and propolis. Mice were slaughtered seven and 35 days after infection to examine the intestinal and muscular phases, respectively. This study demonstrated the efficacy of the combination of SeNPs and propolis. As revealed by electron microscopy, this combination caused damage to the adult worm cuticle. Additionally, compared with albendazole, it resulted in a significant reduction in adult worm and total larval counts; moreover, it caused a decrease in the number of larvae deposited in muscles, with a highly significant decrease in the inflammatory cell infiltrate around the larvae and a considerable decrease in the expression of the angiogenic marker vascular endothelial growth factor in muscles. In conclusion, the combination of SeNPs and propolis had antiparasitic, anti-inflammatory and anti-angiogenic effects on trichinosis. Consequently, this combination could be used as a natural alternative therapy to albendazole for treating trichinosis.
Management of depression in adults: summary of updated NICE guidance

• Discuss treatment options to match the needs and preferences of a person with a new episode of depression • Consider the least intrusive and least resource-intensive available treatment (eg, guided self-help) first for less severe depression • Do not offer antidepressant medication routinely as first line treatment for less severe depression unless that is the person’s preference • When stopping antidepressants, advise a relatively long tapering using a proportional (hyperbolic) reduction schedule
Thinking Outside the Continent and Outside the Box: Cross-Continental Comparative Studies Can Enrich Studies of Pre-Columbian Raised-Field Agriculture

Abstract. Despite an attempt at intercontinental synthesis by Denevan and Turner (1974) almost 50 years ago, studies of agricultural raised fields (RF) in the Neotropics and in Africa and New Guinea are separate research traditions, with almost no communication between them. Neotropical studies refer to “raised-field agriculture” and almost exclusively concern archaeological systems in wetlands. Studies in Africa and New Guinea refer to “mound” or “ridge” cultivation and concern mostly present-day systems (in Africa) or both present-day and archaeological systems (in New Guinea) in both uplands and wetlands. Ethnographic studies of present-day systems provide insights into questions about past systems that are inaccessible using archaeological methods alone. Our review suggests that the Neotropical focus on RF agriculture as an exclusively wetland adaptation is misleading. We argue that the most widespread purpose of building RF, in both wetland and upland environments, is to concentrate topsoil and organic matter, enabling creation of fertile patches in infertile and low-biomass grassland environments. Avoiding flooding is an important function of RF built in wetlands and wetland margins. We further show that Old World RF are often not perennial, but are short-lived structures that rotate over the landscape, being torn down and rebuilt nearby in successive cycles. Short fallow periods are allowed (or even favored) by methods of managing fertility. Finally, we argue that the restriction—on all continents—of archaeological raised fields to wetland and wetland-margin environments is, in part, a result of their better preservation from erosion in wetland than in upland environments.
Feature Extraction Pipeline and Analysis of Suspicious Events in Large-Scale LANs for Cyberattack Categorization

Cybersecurity is one of the most trending topics nowadays since more cyberattacks have been reported globally. A local area network (LAN) is commonly used in our daily life so that it should be crucially watched over. An analysis of network traffic captured by a network monitoring system can protect our LAN from cyberattacks. In this work, we propose SER-LAN which is the system for the analysis of suspicious events in LANs. In SER-LAN, the data pipeline extracts features of four protocols (ARP, TCP, UDP, and ICMP) from network traffic. In addition, an algorithm based on extracted features for categorization suspicious events in a LAN is presented. To demonstrate the data pipeline and the proposed algorithm, we show the analyses of suspicious events with real traffic from 20 LANs. According to the analysis, the top-3 most common suspicious events are network traffic with ICMP type 8, TCP port 445, and UDP port 137 respectively. Moreover, the result revealed that suspicious events which are the combination of 23 TCP ports and ICMP type 8 were observed in 5 LANs.
Bootstrapped fitness critics with bidirectional temporal difference

Evolutionary algorithms (EAs) are well suited for solving many real-world multiagent coordination problems with global, long-term feedback. However, EAs struggle when the feedback becomes sparse and uninformative. In such cases, a system designer can use Fitness Critics, which are functional models that estimate the value of an agent's contribution to transform the sparse domain feedback into a dense reward signal. However, existing methods for updating fitness critics do not leverage the temporal information about when a reward is received. Ideally, temporal difference (TD) methods can leverage temporal information about the sparse feedback signal to bootstrap Fitness Critics. Yet, due to the structure Fitness Critics, direct application of TD methods coevolutionary algorithms result in Fitness Critics that under-represent the rewards that are received earlier in the episode. This paper introduces Bidirectional Fitness Critics (BFCs), which makes use of a novel, bidirectional temporal difference method, to successfully bootstrap the training of fitness critics with temporal reward information, without under-representing early rewards. The paper demonstrates a significant increase in the converged performance of agents coevolved with BFCs on a multiagent coordination domain.
Leveraging Mobile Sensing and Bayesian Change Point Analysis to Monitor Community-scale Behavioral Interventions: A Case Study on COVID-19

During pandemics, effective interventions require monitoring the problem at different scales and understanding the various tradeoffs between efficacy, privacy, and economic burden. To address these challenges, we propose a framework where we perform Bayesian change-point analysis on aggregate behavior markers extracted from mobile sensing data collected during the COVID-19 pandemic. Results generated by 598 participants for up to four months reveal rich insights: We observe an increase in smartphone usage around February 10th, followed by an increase in email usage around February 27th and, finally, a large reduction in participant’s mobility around March 13th. These behavior changes overlapped with important news events and government directives such as the naming of COVID-19, a spike in the number of reported cases in Europe, and the declaration of national emergency by President Trump. We also show that our detected change points align with changes in large scale external sources, including number of COVID-19 tweets, COVID-19 search traffic, and a large-scale foot traffic data collected by SafeGraph, providing further validation of our method. Our results show promise towards the feasibility of using mobile sensing to understand communities’ responses to public health interventions.
Exploring the decision and objective space of SAT constrained multi-objective problems

Although most real-world problems include many constraints that limit the set of feasible solutions, there is significantly less study in constrained benchmark problems when compared to unconstrained ones. SAT constraints attempt to aid the study of constrained multi-objective optimization by allowing any number of equality and inequality constraints, with controllable feasibility ratio, to be added to state-of-the-art benchmark problems. This paper studies the features of SAT constraints by looking at some of their properties such as distribution, clustering and distance of feasible solutions in the decision and objective space when added to 0-1 multi-objective knapsack problems and multi-objective MNK-Landscapes.
A Scalable Many-core Overlay Architecture on an HBM2-enabled Multi-Die FPGA

The overlay architecture enables to raise the abstraction level of hardware design and enhances hardware-accelerated applications’ portability. In FPGAs, there is a growing awareness of the overlay structure as typified by many-core architecture. It works in theory; however, it is difficult in practice, because it is beset with serious design issues. For example, the size of FPGAs is bigger than before. It is exacerbating the issue of the place-and-route. Besides, a single FPGA is actually the sum of small-to-middle FPGAs by advancing packaging technology like silicon interposers. Thus, the tightly coupled many-core designs will face this covert issue that the wires among the regions are extremely restricted. This article proposes efficient essential processing elements, micro-architecture design, and the interconnect architecture toward a scalable many-core overlay design. In particular, our work proposes a novel compact buffering technique to reduce memory resource utilization in tightly connected overlays while preserving computational efficiency. This technique reduces the utilization of BlockRAM to nearly 50% while achieving a best-case computational efficiency of 91.93% in a three-dimensional Jacobi benchmark. Besides, the proposed enhancements led to around 2× and 3× improvement in performance and power efficiency, respectively. Moreover, the improved scalability allowed increasing compute resources and delivering around 4× better performance and power efficiency, as compared to the baseline Dynamically Re-programmable Architecture of Gather-scatter Overlay Nodes overlay.
A Multiserver Approximation for Cloud Scaling Analysis

Queueing models of web service systems run at increasingly large scales, with large customer populations and with multiservers introduced by scaling up the services. "Scalable" multiserver approximations, in the sense that they that are insensitive to customer population size, are essential for solution in a reasonable time. A thorough analysis of the potential errors, which is needed before the approximations can be used with confidence, is the goal of this work. Three scalable approximations are evaluated: an equivalent single server SS, an approximation RF introduced by Rolia, and one based on a binomial distribution for queue state AB. AB and SS are suggested by previous work but have not been evaluated before. For AB and SS, multiple classes are merged into one to calculate the waiting. The analysis employs a novel traffic intensity measure for closed multiserver workloads. The vast majority of errors are less than 1%, with the worst cases being up to about 30%. The largest errors occur near the knee of the throughput/response time curves. Of the approximations, AB is consistently the most accurate and SS the least accurate.
Preliminary results of advanced heuristic optimization in the risk-based energy scheduling competition

In this paper, multiple evolutionary algorithms are applied to solve an energy resource management problem in the day-ahead context involving a risk-based analysis corresponding to the proposed 2022 competition on evolutionary computation. We test numerous evolutionary algorithms for a risk-averse day-ahead operation to show preliminary results for the competition. We use evolutionary computation to follow the competition guidelines. Results show that the HyDE algorithm obtains a better solution with lesser costs when compared to the other tested algorithm due to the minimization of worst-scenario impact.
CTT: Load Test Automation for TOSCA-based Cloud Applications

Despite today's fast and rapid modeling and deployment capabilities to meet customer requirements in an agile manner, testing is still of utmost importance to avoid outages, unsatisfied customers, and performance problems. To tackle such issues, (load) testing is one of several approaches. In this paper, we introduce the Continuous Testing Tool (CTT), which enables the modeling of tests and test infrastructures along with the cloud system under test, as well as deploying and executing (load) tests against a fully deployed system in an automated manner. CTT employs the OASIS TOSCA Standard to enable end-to-end support for continuous testing of cloud-based applications. We demonstrate CTT's workflow, its architecture, as well as its application to DevOps-oriented load testing and load testing of data pipelines.
Genetic heterogeneity analysis using genetic algorithm and network science

Genome-wide association studies (GWAS) have linked thousands of genetic variants to the susceptibility of many common human diseases. However, the genetic explanations of diseases are often heterogeneous, imposing a substantial challenge for GWAS. We propose a feature construction method using genetic algorithm (GA) to recognize the heterogeneous risk effects of different genetic variable groups. Multiple GA-based feature selection runs are used to collect an ensemble of the high-performing feature subsets. We generate a feature co-selection network from the ensemble, where nodes represent genetic variables and edges represent their co-selection frequencies. A new synthetic feature, namely community risk score (CRS), is created for each network community. CRS quantifies the risk of a community of variables and allows for more effective heterogeneity analysis. We applied our method to two colorectal cancer GWAS datasets, one for training and the other for validation. We ran the GA-based feature selection on the training dataset and constructed the co-selection network. CRS was then created for each community in the network. We identified three colorectal cancer subtypes using the CRSs and clustering algorithms on the validation dataset. The function enrichment analysis in our results further highlighted gastric cancer related genes, tumor suppressors and DNA methylation genes.
A surrogate model-based genetic algorithm for the optimal policy in cart-pole balancing environments

Agent's learning for completing a given mission needs a lot of trial-and-error. Generally, an agent's performance is evaluated by a simulation. Thus, exorbitant simulation costs may be incurred when an agent is trained to solve real-world problems. We propose a surrogate model-based genetic algorithm for reducing simulation-based evaluation costs of the cart-pole balancing problem. Policy networks and returns by the networks were collected by training a deep Q-network (DQN) among reinforcement learning methods. A surrogate model for the fitness function of a genetic algorithm was completed through our heuristic measure and DNN-based models trained on DQN data. Our surrogate model-based genetic algorithm performed much faster than that with simulation-based evaluation, and it could successfully find optimal solutions that achieves the maximum cumulative reward.
Characterizing and Triaging Change Points

Testing software performance continuously can greatly benefit from automated verification done on continuous integration (CI) servers, but it generates a large number of performance test data with noise. To identify the change points in test data, statistical models have been developed in research. However, a considerable amount of detected change points is marked as the changes actually never need to be fixed (false positive). This work aims at giving a detailed understanding of the features of true positive change points and an automatic approach in change point triage, in order to alleviate project members' burdens. To achieve this goal, we begin by characterizing the change points using 31 features from three dimensions, namely time series, execution result, and file history. Then, we extract the proposed features for true positive and false positive change points, and train machine learning models to triage these change points. The results demonstrate that features can be efficiently employed to characterize change points. Our model achieves an AUC of 0.985 on a median basis.
From the Crisis of Financialization to the COVID Crisis: In Search of a Collective Action Between Market and State

Abstract This article is an exploratory essay on the possibility of governance of financial systems beyond the opposition between market and state to improve financial stability. Financial system governance is a set of rules and practices that should allow sustainable organization and management of markets. It closely relies on financial regulation in force. This article assumes that the monetary/financial system is a core institutional framework whose stability is of utmost importance and requires specific collective action to face social dilemmas that result in systemic concerns. Two analytic perspectives are used to frame a relevant collective action model: polycentric governance à la Ostrom and Ostrom, that fits the cases of common-pool resources, and Minskian institutionalist perspective that fits the analysis of endogenous instabilities. A distinction criterion between these two perspectives is then suggested: If an issue has a global character (societal criticalness of public goods), it would fit well with a “power-over” coordination framework whereas more local elements (the commons) could be governed through polycentric “power-with” mechanisms. The rationale for macro-prudential regulation against systemic failures is then put forward under the supervision of extra-market public agencies in charge of global coordination over local regulatory institutions in order to increase the flexibility and the speed of responses to growing instabilities.
La matemática borrosa y el precio de equilibrio: una aplicación a las aguas depuradas / Fuzzy mathematics and equilibrium price: An application to the treated wastewater

Giving the necessity to know the price of a non-marketed good, our objective throughout the present paper is the proposal of a mechanism for their determination. Starting from the contingent valuation method (CVM) used in a crisp environment; we will propose a methodology to aggregate fuzzy opinions in order to get the equilibrium price. Further analysis will allow us to evaluate the convenience of fixing subsidies or taxes on such non-marketed goods. Previous works have deal with fuzzy demand and supply functions (Yager, 2000; Dompere, 1997). This paper will focus on the process of requesting supplying information. Two groups of experts were selected, one representing demand and the other supply. After successive phases, both prices were determined, and their intersection made it possible to obtain the market price in accordance with classical theory. The study established criteria for determining the maximum number of phases, bearing in mind the dispersion of opinions and their variation in two consecutive phases. Finally, the mechanism was applied to determine the market price of treated wastewater in the province of Alicante (Spain). A survey was made to the wastewater treatment plants (WWTP) as suppliers and farmers and water distribution companies as consumers.
Structural characterization of human RPA70N association with DNA damage response proteins

The heterotrimeric Replication protein A (RPA) is the ubiquitous eukaryotic single-stranded DNA (ssDNA) binding protein and participates in nearly all aspects of DNA metabolism, especially DNA damage response. The N-terminal OB domain of the RPA70 subunit (RPA70N) is a major protein-protein interaction element for RPA. Previous crystallography studies of RPA70N with p53, DNA2 and PrimPol fragments revealed that RPA70N binds to amphipathic peptides that mimics ssDNA. NMR chemical-shift studies also provided valuable information of RPA70N residues interacting with target sequences. However, it is still not clear how RPA70N recognizes and distinguishes such a diverse group of proteins. Here we present high resolution crystal structures of RPA70N in complex with peptides from HelB, ATRIP, RMI1, WRN and BLM. The structures showed that in addition to the ssDNA mimicry mode of interaction, RPA70N employs multiple ways to bind its partners, some of which may serve to increase the avidity of RPA70N binding.