Miracle-dz/UniFAR-FTUs huggingface dataset:
Auto-active Verification of Graph Algorithms, Written in OCaml

Functional programming offers the perfect ground for building correct-by-construction software. Languages of such paradigm normally feature state-of-the-art type systems, good abstraction mechanisms, and well-defined execution models. We claim that all of these make software written in a functional language excellent targets for formal certification. Yet, somehow surprising, techniques such as deductive verification have been seldom applied to large-scale programs, written in mainstream functional languages. In this paper, we wish to address this situation and present the auto-active proof of realistic OCaml implementations. We choose implementations issued from the OCamlgraph library as our target, since this is both a large-scale and widely-used piece of OCaml code. We use Cameleer, a recently proposed tool for the deductive verification of OCaml programs, to conduct the proofs of the selected case studies. The vast majority of such proofs are completed fully-automatically, using SMT solvers, and when needed we can apply lightweight interactive proof inside the Why3 IDE (Cameleer translates an input program into an equivalent WhyML one, the language of the Why3 verification framework). To the best of our knowledge, these are the first mechanized, mostly-automated proofs of graph algorithms written in OCaml.
Beam Alignment for the Cell-Free mmWave Massive MU-MIMO Uplink

Millimeter-wave (mmWave) cell-free massive multiuser (MU) multiple-input multiple-output (MIMO) systems combine the large bandwidths available at mmWave frequencies with the improved coverage of cell-free systems. However, to combat the high path loss at mmWave frequencies, user equipments (UEs) must form beams in meaningful directions, i.e., to a nearby access point (AP). At the same time, multiple UEs should avoid transmitting to the same AP to reduce MU interference. We propose an interference-aware method for beam alignment (BA) in the cell-free mmWave massive MU-MIMO uplink. In the considered scenario, the APs perform full digital receive beamforming while the UEs perform analog transmit beamforming. We evaluate our method using realistic mmWave channels from a commercial ray-tracer, showing the superiority of the proposed method over omnidirectional transmission as well as over methods that do not take MU interference into account.
DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation

Causal Inference has wide applications in various areas such as E-commerce and precision medicine, and its performance heavily relies on the accurate estimation of the Individual Treatment Effect (ITE). Conventionally, ITE is predicted by modeling the treated and control response functions separately in their individual sample spaces. However, such an approach usually encounters two issues in practice, i.e. divergent distribution between treated and control groups due to treatment bias, and significant sample imbalance of their population sizes. This paper proposes Deep Entire Space Cross Networks (DESCN) to model treatment effects from an end-to-end perspective. DESCN captures the integrated information of the treatment propensity, the response, and the hidden treatment effect through a cross network in a multi-task learning manner. Our method jointly learns the treatment and response functions in the entire sample space to avoid treatment bias and employs an intermediate pseudo treatment effect prediction network to relieve sample imbalance. Extensive experiments are conducted on a synthetic dataset and a large-scaled production dataset from the E-commerce voucher distribution business. The results indicate that DESCN can successfully enhance the accuracy of ITE estimation and improve the uplift ranking performance. A sample of the production dataset and the source code are released to facilitate future research in the community, which is, to the best of our knowledge, the first large-scale public biased treatment dataset for causal inference.
Mitigating Algorithmic Bias with Limited Annotations

Existing work on fairness modeling commonly assumes that sensitive attributes for all instances are fully available, which may not be true in many real-world applications due to the high cost of acquiring sensitive information. When sensitive attributes are not disclosed or available, it is needed to manually annotate a small part of the training data to mitigate bias. However, the skewed distribution across different sensitive groups preserves the skewness of the original dataset in the annotated subset, which leads to non-optimal bias mitigation. To tackle this challenge, we propose Active Penalization Of Discrimination (APOD), an interactive framework to guide the limited annotations towards maximally eliminating the effect of algorithmic bias. The proposed APOD integrates discrimination penalization with active instance selection to efficiently utilize the limited annotation budget, and it is theoretically proved to be capable of bounding the algorithmic bias. According to the evaluation on five benchmark datasets, APOD outperforms the state-of-the-arts baseline methods under the limited annotation budget, and shows comparable performance to fully annotated bias mitigation, which demonstrates that APOD could benefit real-world applications when sensitive information is limited.
Difficulty-Aware Simulator for Open Set Recognition

Open set recognition (OSR) assumes unknown instances appear out of the blue at the inference time. The main challenge of OSR is that the response of models for unknowns is totally unpredictable. Furthermore, the diversity of open set makes it harder since instances have different difficulty levels. Therefore, we present a novel framework, DIfficulty-Aware Simulator (DIAS), that generates fakes with diverse difficulty levels to simulate the real world. We first investigate fakes from generative adversarial network (GAN) in the classifier's viewpoint and observe that these are not severely challenging. This leads us to define the criteria for difficulty by regarding samples generated with GANs having moderate-difficulty. To produce hard-difficulty examples, we introduce Copycat, imitating the behavior of the classifier. Furthermore, moderate- and easy-difficulty samples are also yielded by our modified GAN and Copycat, respectively. As a result, DIAS outperforms state-of-the-art methods with both metrics of AUROC and F-score. Our code is available at https://github.com/wjun0830/Difficulty-Aware-Simulator.
Wigner Function Tomography via Optical Parametric Amplification

Wigner function tomography is indispensable for characterizing quantum states, but its commonly used version, balanced homodyne detection, suffers from several weaknesses. First, it requires efficient detection, which is critical for measuring fragile non-Gaussian states, especially bright ones. Second, it needs a local oscillator, tailored to match the spatiotemporal properties of the state under test, and fails for multimode and broadband states. Here we propose Wigner function tomography based on optical parametric amplification followed by direct detection. The method is immune to detection inefficiency and loss, and suitable for broadband, spatially and temporally multimode quantum states. To prove the principle, we experimentally reconstruct the Wigner function of squeezed vacuum occupying a single mode of a strongly multimode state. We obtain a squeezing of $-7.5\pm 0.4$ dB and a purity of $0.91^{+0.09}_{-0.08}$ despite more than $97\%$ loss caused mainly by filtering. Theoretically, we also consider the reconstruction of a squeezed single photon - a bright non-Gaussian state. Due to strong multimode parametric amplification, the method allows for the simultaneous tomography of multiple modes. This makes it a powerful tool for optical quantum information processing.
Asymptotic Normality for Plug-In Estimators of Generalized Shannon’s Entropy

Shannon’s entropy is one of the building blocks of information theory and an essential aspect of Machine Learning (ML) methods (e.g., Random Forests). Yet, it is only finitely defined for distributions with fast decaying tails on a countable alphabet. The unboundedness of Shannon’s entropy over the general class of all distributions on an alphabet prevents its potential utility from being fully realized. To fill the void in the foundation of information theory, Zhang (2020) proposed generalized Shannon’s entropy, which is finitely defined everywhere. The plug-in estimator, adopted in almost all entropy-based ML method packages, is one of the most popular approaches to estimating Shannon’s entropy. The asymptotic distribution for Shannon’s entropy’s plug-in estimator was well studied in the existing literature. This paper studies the asymptotic properties for the plug-in estimator of generalized Shannon’s entropy on countable alphabets. The developed asymptotic properties require no assumptions on the original distribution. The proposed asymptotic properties allow for interval estimation and statistical tests with generalized Shannon’s entropy.
Characterizations of Lipschitz functions via the commutators of maximal function in Orlicz spaces on stratified Lie groups

We give necessary and sufficient conditions for the boundedness of the maximal commutators $M_{b}$, the commutators of the maximal operator $[b, M]$ and the commutators of the sharp maximal operator $[b, M^{\sharp}]$ in Orlicz spaces $L^{\Phi}(\mathbb{G})$ on any stratified Lie group $\mathbb{G}$ when $b$ belongs to Lipschitz spaces $\dot{\Lambda}_{\beta}(\mathbb{G})$. We obtain some new characterizations for certain subclasses of Lipschitz spaces $\dot{\Lambda}_{\beta}(\mathbb{G})$.
Small-time behaviour and hydrodynamic limit of beta coalescents

We quantify the behaviour at small times of the beta coalescent Π = { Π ( t ) ,t ≥ 0 } with parameters a,b > 0 . To this end we study the non-trivial limits of the rescaled block counting process { n α # Π n ( tn β ) , t ≥ 0 } as n → ∞ for suitable α and β, the idea being to approximate Π with its restriction to { 1 , . , n } , Π n . If Π comes down from inﬁnity we obtain a Law of Large Numbers type of result, or hydrodynamic limit in the parlance of statistical physics, that exhibits a phase transition at α = − 1 . More speciﬁcally, for α > − 1 and β = α (1 − a ) the rescaling limit c ( t ) is deterministic see Theorem 2, and agrees with the rescaling limit as n → ∞ of { n α # Π ( tn β ) , t ≥ 0 } , see Theorem 3. However, for α = − 1 and β = a − 1 , we ﬁnd If Π does not come down from inﬁnity the above rescaling does not admit a diﬀusion limit. However, we can still study the average behaviour { n α E [ # Π n ( tn β )] , t ≥ 0 } of the rescaled number of blocks. Here we ﬁnd a rescaling limit m ( t ) for α = − 1 , β = 0 very diﬀerent from c ( t ) , namely
Successful and Effective Six Sigma Implementation in the Manufacturing Industry

The concept of quality has been defined in various ways, for example, Abott (1955) defines it classically as value (Abott, 1955); and others as conformance to specifications and/or requirements (Gilmore, 1974, Crosby & Free 1979), as fitness for use (Juran 1988) and as meeting and/or exceeding customers’ expectations (Gronroos 1982 and Reeves & Bednar 1994). The concept of quality is not new; however, as demonstrated above, all definitions are open to various interpretations and may be considered as highly subjective.
The O-spectrum for Brown-Peterson cohomology

A color photographic material comprising a support having coated thereon at least one silver halide emulsion layer containing a magenta dye forming coupler having a 3-anilino-5-pyrazolone ring in which the ortho position of the anilino group at the 3-position is substituted with a halogen atom, an alkyl group, an alkoxy group, an amide group, an aryl group, an aryloxy group, a cyano group, a nitro group, a hydroxyl group, or an amino group and having a hydrophobic residue of 8 to 32 carbon atoms, the silver halide emulsion layer or an emulsion layer adjacent to the silver halide emulsion layer containing a hydroquinone derivative having at least one substituent of at least 8 carbon atoms or a precursor capable of providing such a hydroquinone derivative by hydrolysis.
Stability analysis for dusty plasma under grain charge fluctuations due to non-Maxwellian electron distributions

The most relevant characteristic of a complex plasma is the dust charge as well as the dust charging process itself which controls different collective and individual behaviors of the plasma. The dust charging has been exhaustively studied providing several theoretical approaches that have improved the early Orbital Motion Limited (OML) description [1, 2]. The OML is considered as a suitable model and, at least, it provides a certain perspective of the main plasma parameters involved in the charging processes. Recent works have stressed the importance of the electron and ion velocity distribution functions in addressing the description of plasma stability analysis under the frame of plasma ﬂuid description, including dust charge ﬂuctuations. The consideration of non-Maxwellian distribution functions has been proved to induce plasma departures from the usual Maxwellian equilibrium, specially under the development of intense electric ﬁelds that can accelerate charges till superthermal velocities [3]. In these cases, the distribution function tails ﬁt well to a power-law dependence even for electrons. In this communication, we devote attention to the effect of non Maxwellian electron distribution functions on the collective plasma behavior through a linear analysis of perturbed ﬂuid equations. The stability of a partially ionized complex plasma with a non Maxwellian electron population is studied by including this feature on the dust charge ﬂuctuation for inﬁnite and ﬁnite dust grain mass.
Scale Effect in Ice Flexural Strength

Ice flexural strength is an important parameter in the assessment of ice loads on the hulls of ice-class ships, sloped offshore structures, and sloped bridge piers. While scale effects in compressive ice strength are well known, there has been debate as to the extent of scale effects in ice flexural strength. To investigate scale effects during flexural failure of both freshwater and saline ice, a comprehensive up-to-date database of beam flexural strength measurements has been compiled. The database includes 2073 freshwater ice beam tests with beam volumes between 0.00016 and 2.197 m3, and 2843 sea ice beam tests with volumes between 0.00048 and 59.87 m3. The data show a considerable decrease in flexural strength as the specimen size increases, when examined over a large range of scales. Empirical models of freshwater ice flexural strength as a function of beam volume, and of saline ice as function of beam and brine volumes have been developed using regression analysis. For freshwater ice, the scale-dependent flexural strength is given as: σf=839(V/V1)−0.13 For sea ice, the dependence of flexural strength has been modeled as: σ=1324(V/V1)−0.054e−4.969vb. Probabilistic models based on the empirical data were developed based on an analysis of the residuals, and can be used to enhance probabilistic analysis of ice loads where ice flexural strength is an input.
Security Aspects in Cognitive Radio Networks Detection and Mitigation of Primary User Emulation Attacks

— Cognitive Radio Networks (CR) is an advanced growing technique and a promising technology for the upcoming generation of the wireless networks. Deployment of such networks is hindered by the vulnerabilities that these networks are exposed to, in this paper we focus on security problems arising from Primary User Emulation Attacks (PUEA) in CR networks. We study the impact of this attack on CR networks, detection and defense approaches. We have setup the system model using Matlab software; the Neyman-Pearson composite hypothesis test NPCHT is used to obtain the hypothesis test and detect the PUEA. Simulation results proved that using NPCHT it is possible to keep the probability of successful PUEA low, and this depends on the threshold values; the number of malicious users in the system can significantly increase the probability of false alarm in the network, Also it shows that there is a range of network radii in which PUEA are most successful.
Anomaly-Detection In Diabetes Using SVM

Gradual multiplication of data over sectors provoked the discomfort in detection of abnormality or anomaly in such immense data, consequently the initiation of miscellaneous algorithms for such consequences had been generated. In this paper we have chosen One-Class Support Vector Machine for anomaly detection in diabetes, as it has the classification mining without target to detect the abnormality. The multitudinous factors on examination of the algorithm are studied and analyzed. The results of the algorithm were examined and analyzed for efficacy in progression of the algorithm.
Vitamin D: A D-Lightful Solution for Health

: Throughout evolution, sunlight-produced vitamin D in the skin has been critically important for health. Vitamin D, known as the sunshine vitamin, is actually a hormone. Once it is produced in the skin or ingested from the diet, it is converted sequentially in the liver and kidneys to its biologically active form 1,25-dihydroxyvitamin D. This hormone interacts with its receptor in the small intestine to increase the efﬁciency of intestinal calcium and phosphate absorption for the maintenance of theskeletonthroughoutlife. VitaminD deﬁciency during the ﬁrst few years of life results in a ﬂattened pelvis, making it difﬁcult for childbirth. Vitamin D deﬁciency causes osteopenia and osteoporosis, increasing risk of fracture. Essentially, every tissue and cell in the body has a vitamin D receptor. Therefore, vitamin D deﬁciency has been linked to increased risk for preeclampsia, requiring a cesarean section for birthing, multiple sclerosis, rheumatoid arthritis, types I and II diabetes, heart disease, dementia, deadly cancers, and infectious diseases. Therefore, sensible sun exposure along with vitamin D supplementation of at least 2000 IU/d for adults and 1000 IU/d for children is essential to maximize their health. D-24-hydroxylase (24-OHase) to catabolize 1,25(OH) 2 D to the water soluble biologically inactive calcitroic acid that is excreted in the bile. 1,25-Dihydroxyvitamin D enhances intestinal calcium absorption in the small intestine by stimulating the expression of the epithelial calcium channel (ECaC) and the calbindin 9K (calcium-binding protein; CaBP). 1,25-Dihydroxyvitamin D is recognized by its receptor in osteoblasts causing an increase in the expression of receptor activator of NF J B ligand (RANKL). Its receptor RANK on the preosteoclast binds RANKL, which induces the preosteoclast to become a mature osteoclast. The mature osteoclast removes calcium and phosphorus from the bone to maintain blood calcium and phosphorus levels. Adequate calcium and phosphorus levels promote the mineralization of the skeleton.
Work�ow Scheduling for Intelligent Modern Medical System with Privacy Protection Constraints for Iaas Cloud Using Modi�ed Grasshopper Algorithm

Cloud computing considered as perhaps the most popular �guring models. The IaaS cloud offers different processing assets to the clients on request premise. A work process in the cloud is addressed by a Direct Acyclic Graph where the hubs address the assignments and edges address the between reliance of the errand. Distributed computing offers profoundly unique climate in which the framework burden and status of asset changes often. As due to technical enhancements many people are affected with diseases a new intelligent modern medical system is developed. As the responsibility increments with expansion in Cloud Services and customers there is a need to deal with these solicitations or occupations. Effective work booking instrument with security insurance imperatives simultaneously as limiting both execution time and cost, likewise the work process planning calculations with other improvement destinations, for example, limiting energy utilization will be thought of. Assessment is done dependent on CPU time, response time, Time for Total Execution. Finally, the show of arranged Work Scheduling Algorithm evaluates use different replication scenarios and shows the effectiveness over the existing state-of-art protocols.
The importance of diversi ed physical activities in early childhood for later fundamental movement skills and physical activity level: a seven-year longitudinal study

Paulina S. Melby (  paulina.sander.melby@regionh.dk ) Health Promotion, Steno Diabetes Center Copenhagen, the Capital Region of Denmark Peter Elsborg Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, the Capital Region of Denmark Glen Nielsen Department of Nutrition, Exercise and Sports, University of Copenhagen Rodrigo A. Lima Research Group on Lifestyle and Health, School of Physical Education, University of Pernambuco, Recife Peter Bentsen Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, the Capital Region of Denmark Lars B. Andersen Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo
Control of the Amount of Oncolytic Virus Injection by Considering Time Delay

Oncolytic viral therapy is a new promising strategy against cancer. Oncolytic viruses can replicate in cancer cells rather than in normal cells, leading to lysis of the tumor mass and stimulate the immune system. During cancer viral therapy, there is a time delay from the initial virus infection of the tumor cells up to the time those infected cells reach the stage of being able to infect other cells. It is important to understand how the delay affects the cancer viral therapy. For this purpose, a mathematical model is introduced to identify this delay. To analyze the effects of delay on virus therapy, the model was reconstructed by adding both virus therapy and immunotherapy control. Finally, using a numerical simulation, a fuzzy parallel distributed compensation controller was designed for the first time. Numerical results showed that with proper control, the tumor cell population decreased to below 10% over time. It is also observed that the use of a delay-independent stability criterion for the design of the parallel distributed compensation controller has reduced the sensitivity of the system response to increasing time delay to an acceptable level. Since the studied system is introduced only in one reference and only the optimal controller is applied, the comparison shows the superiority and power of the designed fuzzy parallel distributed compensation controller.
SPSB: Semi-Permeable Spam Banner

— In the past few years Online Social Networking has gained importance because of its cheap & easily accessible features. Various online communication media platforms such as Social Networking, Electronic Mail Services (i.e. eMails) and various online chat messaging applications allows online users to reach & communicate with number of people at a barely negligible & affordable cost. Apart from this, it also enables the information to roam freely in network. Thus resulting into undesired communication happening on a larger scale. With increase in the number of eMail users there is a significant increase in irrelevant messages called Spam, Junk Mail or Ham. Hence, the objective is to develop a Semi Permeable System which bans the spams from entering the network with great efficiency hence, preventing Unwanted Communication and reducing cost of the network too. Various remedies taken earlier to eradicate this problem use filtering techniques based on the content, header & classification. Hence, SPSB is a proposed solution which will minimize the routing of irrelevant content in the network. It will act as a Semi Permeable Banner at the client side which will ban the Spams to further roam in the network and allow legitimate mails to enter the system. Hence SPSB will be a good authentication standard which will differentiate between mails which are legitimate & the ones that are not.