Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Detection of Speculative Execution Combinations

Modern processors employ different speculation mechanisms to speculate over different kinds of instructions. Attackers can exploit these mechanisms simultaneously in order to trigger leaks of speculatively-accessed data. Thus, sound reasoning about such speculative leaks requires accounting for all potential speculation mechanisms. Unfortunately, existing formal models only support reasoning about fixed, hard-coded speculation mechanisms, with no simple support to extend said reasoning to new mechanisms. In this paper, we develop a framework for reasoning about composed speculative semantics that capture speculation due to different mechanisms and implement it as part of the Spectector verification tool. We implement novel semantics for speculating over store and return instructions and combine them with the semantics for speculating over branch instructions. Our framework yields speculative semantics for speculating over any combination of these instructions that are secure by construction, i.e., we obtain these security guarantees for free. The implementation of our novel semantics in Spectector let us verify programs that are vulnerable to Spectre v1, Spectre v4, and Spectre v5 vulnerabilities as well as new snippets that are only vulnerable to their compositions.
Analyzing and visualizing polarization and balance with signed networks: the U.S. Congress case study

Signed networks and balance theory provide a natural setting for real-world scenarios that show polarization dynamics, positive/negative relationships and political partisanship. For example, they have been proven effective in studying the increasing polarization of the votes in the two chambers of the U.S. Congress from World War II on Andris, Lee, Hamilton, Martino, Gunning & Selden (2015, PLoS ONE, 10, 1–14) and Aref & Neal (2020, Sci. Rep., 10, 1–10). To provide further insights into this particular case study, we propose the application of a pipeline to analyze and visualize a signed graphs configuration based on the exploitation of the corresponding Laplacian matrix spectral properties. The overall methodology is comparable with others based on the frustration index, but it has at least two main advantages: first, it requires a much lower computational cost and second, it allows for a quantitative and visual assessment of how arbitrarily small subgraphs (even single nodes) contribute to the overall balance (or unbalance) of the network. The proposed pipeline allows the exploration of polarization dynamics shown by the U.S. Congress from 1945 to 2020 at different resolution scales. In fact, we are able to spot and point out the influence of some (groups of) congressmen in the overall balance, as well as to observe and explore polarizations evolution of both chambers across the years.
"More Than Words": Linking Music Preferences and Moral Values Through Lyrics

This study explores the association between music preferences and moral values by applying text analysis techniques to lyrics. Harvesting data from a Facebook-hosted application, we align psychometric scores of 1,386 users to lyrics from the top 5 songs of their preferred music artists as emerged from Facebook Page Likes. We extract a set of lyrical features related to each song's overarching narrative, moral valence, sentiment, and emotion. A machine learning framework was designed to exploit regression approaches and evaluate the predictive power of lyrical features for inferring moral values. Results suggest that lyrics from top songs of artists people like inform their morality. Virtues of hierarchy and tradition achieve higher prediction scores ($.20 \leq r \leq .30$) than values of empathy and equality ($.08 \leq r \leq .11$), while basic demographic variables only account for a small part in the models' explainability. This shows the importance of music listening behaviours, as assessed via lyrical preferences, alone in capturing moral values. We discuss the technological and musicological implications and possible future improvements.
Relativistic Effects in the Study of Structure and Electronic Properties of Uo2 within Dft+U Method

To study crystals that contain heavy atoms, it is important to consider the relativistic effects, as electrons in orbitals close to the atom's nucleus can reach speeds comparable to that of light in a vacuum. In this study, we utilized the first-principles DFT+U method to analyze the electronic structure and geometric properties of uranium dioxide (UO2) using three formulations: full-relativistic, scalar-relativistic, and non-relativistic. Our findings demonstrate that the non-relativistic scheme produces results that deviate significantly from experimental values for both lattice constant and band gap. In contrast, the scalar-relativistic regime yields highly accurate results for the geometric properties of UO2, and is therefore sufficient for most studies. However, for a more precise analysis, the full-relativistic calculations with spin-orbit effects should be employed, which result in a $6.2\%$ increase in the Kohn-Sham band-gap and a $0.05\%$ decrease in the lattice constant compared to the scalar-relativistic approach.
Theoretical study on the effect of graded InyGa1−yN layer on p‐GaN/InyGa1−yN/n‐GaN p–i–n solar cell

Using TCAD 2D simulation, the effect of compositional grading configuration of intrinsic InyGa1−yN layer on p‐GaN/InyGa1−yN/n‐GaN p–i–n heterojunction solar cell has been investigated. Short‐circuit current density is increased which can be ascribed to the reduction of polarization induced electric field, which is in the opposite direction of internal built in electric field. With the introduction of the intrinsic InyGa1−yN graded layer, up to indium mole fraction of 0.50, the characteristic solar cell parameters may be drastically enhanced compared to uniform profile. The proposed p–i–n solar cell has a fill factor of 87.7%, short‐circuit current density of 7.64 mA cm−2, open‐circuit voltage of 2.2 V and efficiency of 18.53% under AM1.5 illumination. It was observed that valance band and conduction band offsets are smoother at the interface of InyGa1−yN/n‐GaN, in case of graded profiles and enhances the minority carrier collection.
Structure recovery for partially observed discrete Markov random fields on graphs under not necessarily positive distributions

We propose a penalised conditional likelihood criterion to estimate the basic neighbourhood of each node in a discrete Markov random field that can be partially observed. We prove the convergence of the estimator in the case of a finite or countable infinite set of nodes. The estimated neighbourhoods can be combined to estimate the underlying graph. In the finite case, the graph can be recovered with probability one. In contrast, we can recover any finite sub‐graph with probability one in the countable infinite case by allowing the candidate neighbourhoods to grow as a function o(log n), with n the sample size. Our method requires minimal assumptions on the probability distribution, and contrary to other approaches in the literature, the usual positivity condition is not needed. We evaluate the estimator's performance on simulated data and apply the methodology to a real dataset of stock index markets in different countries.This article is protected by copyright. All rights reserved.
Ab Initio
 Study of Magnetic Tunnel Junctions Based on Half-Metallic and Spin-Gapless Semiconducting Heusler Compounds: Reconfigurable Diode and Inverse Tunnel-Magnetoresistance Effect

Magnetic tunnel junctions (MTJs) have attracted strong research interest within the last decades due to their potential use as nonvolatile memory such as MRAM as well as for magnetic logic applications. Half-metallic magnets (HMMs) have been suggested as ideal electrode materials for MTJs to achieve an extremely large tunnel-magnetoresistance (TMR) effect. Despite their high TMR ratios, MTJs based on HMMs do not exhibit current rectification, i.e., a diode effect, which was achieved in a magnetic tunnel junction concept based on HMMs and type-II spin-gapless semiconductors (SGSs). The proposed concept has recently been experimentally demonstrated using Heusler compounds. In the present work, we investigate from first-principles MTJs based on type-II SGS and HMM quaternary Heusler compounds FeVTaAl, FeVTiSi, MnVTiAl, and CoVTiSb. Our $ab$ $initio$ quantum transport calculations based on a nonequilibrium Green's function method have demonstrated that the MTJs under consideration exhibit current rectification with relatively high on:off ratios. We show that, in contrast to conventional semiconductor diodes, the rectification bias voltage window (or breakdown voltage) of the MTJs is limited by the spin gap of the HMM and SGS Heusler compounds. A unique feature of the present MTJs is that the diode effect can be configured dynamically, i.e., depending on the relative orientation of the magnetization of the electrodes, the MTJ allows the electrical current to pass either in one or the other direction, which leads to an inverse TMR effect. The combination of nonvolatility, reconfigurable diode functionality, tunable rectification voltage window, and high Curie temperature of the electrode materials makes the proposed MTJs very promising for room-temperature spintronic applications and opens ways to magnetic memory and logic concepts as well as logic-in-memory computing.
Time-Range Fda Beampattern Characteristics

Current literature show that frequency diverse arrays (FDAs) are able of producing range-angle-dependent and time-variant transmit beampatterns, but the resulting time and range dependencies and their characteristics are still not well understood. This paper examines the FDA transmission model and the model for the FDA array factor, considering their time-range relationship. We develop two FDA transmit beampatterns, both yielding the auto-scanning capability of the FDA transmit beams. The scan speed, scan volume, and initial mainlobe direction of the beams are also analyzed. In addition, the equivalent conditions for the FDA integral transmit beampattern and the multiple-input multiple-output (MIMO) beampattern are investigated. Various numerical simulations illustrate the auto-scanning property of the FDA beampattern and the proposed equivalent relationship with the MIMO beampattern, providing the basis for an improved understanding and design of the FDA transmit beampattern.
Density-Based Clustering and Anomaly Detection

As of 1996, when a special issue on density-based clustering was published (DBSCAN) (Ester et al., 1996), existing clustering techniques focused on two categories: partitioning methods, and hierarchical methods. Partitioning clustering attempts to break a data set into K clusters such that the partition optimizes a given criterion. Besides difficulty in choosing the proper parameter K, and incapacity of discovering clusters with arbitrary shape, partitioning clustering techniques are very sensitive to outliers. Although the k-medoids method (Kaufman & Rousseeuw, 1990) is more robust than k-means (MacQueen, 1967) in the presence of outliers, they cannot discover outliers. Hierarchical clustering algorithms produce a nested sequence of clusters, with a single all-inclusive cluster at the top and single point clusters at the bottom. CURE (Guha et al., 1998) is capable of finding clusters of arbitrary shapes and reduces the effect of outliers; however, it only considers cluster proximity yet ignores cluster interconnectivity, and an outlier is still assigned to the cluster which has the closest representative point to it.
Towards surface analysis on diabetic feet soles to predict ulcerations using photometric stereo

Diabetic foot ulceration is a major complication for patients with diabetes mellitus. Approximately 15% to 25% of patients with Type I and Type II diabetes eventually develop feet ulcers. If not adequately treated, these ulcers may lead to foot infection, and ultimately to total (or partial) lower extremity amputation, which means a great loss in health-related quality of life. The incidence of foot ulcers may be prevented by early identification and subsequent treatment of pre-signs of ulceration, such as callus formation, redness, fissures, and blisters. Therefore, frequent examination of the feet is necessary, preferably on a daily basis. However, self-examination is difficult or impossible due to consequences of the diabetes. Moreover, frequent examination by health care professionals is costly and not feasible. The objective of our project is to develop an intelligent telemedicine monitoring system that can be deployed at the patients' home environment for frequent examination of patients feet, to timely detect pre-signs of ulceration. The current paper reports the preliminary results of an implementation of a photometric stereo imaging system to detect 3D geometric abnormalities of the skin surfaces of foot soles. Using a flexible experimental setup, the system parameters such as number and positions of the illuminators have been selected so as to optimize the performance with respect to reconstructed surface. The system has been applied to a dummy foot sole. Finally, the curvature on the resulting 3D topography of the foot sole is implemented to show the feasibility of detecting the pre-signs of ulceration using photometric stereo imaging. The obtained results indicate clinical potential of this technology for detecting the pre-signs of ulceration on diabetic feet soles.
A model for the optimal design of a supply chain network driven by stochastic fluctuations

Supply chain optimization schemes have more often than not underplayed the role of inherent stochastic fluctuations in the associated variables. The present article focuses on the associated reengagement and correlated renormalization of supply chain predictions now with the inclusion of stochasticity induced fluctuations in the structure. With a processing production plant in mind that involves stochastically varying production and transportation costs both from the site to the plant as well as from the plant to the customer base, this article proves that the producer may benefit through better outlay in the form of higher sale prices with lowered optimized production costs only through a suitable selective choice of producers whose production cost probability density function abides a Pareto distribution. Lower the Pareto exponent, better is the supply chain prediction for cost optimization. On the other hand, other symmetric (normal) and asymmetric (lognormal) distributions lead to upscaled costs both in terms of inlays and outlays. While this is an averaged out statistics over large time regimes, transient features may still affect such probabilistic predictions and offset results. The predictions are shown to be in good harmony with model results shown.
A Two‐Step Flexible Ultrasound Strategy to Enhance Tumor Radiotherapy via Metal–Phenolic Network Nanoplatform

The tumor hypoxic microenvironment is a dynamic status that is closely related to tumor growth, metastasis, clearance, and recurrence. Hypoxic conditions are present in almost all tumors, which greatly reduce the clinical effects of tumor therapy. Considering the physical tolerance and dose‐related side effects of clinical cancer radiotherapy (RT) patients, herein, a universal two‐step strategy is designed based on safe, noninvasive, deep penetration, and intensity‐controlled ultrasound method to alleviate hypoxia and enhance tumor RT. The first step is to alleviate the tumor hypoxia by physical stimulation from noninvasive and controllable low‐intensity pulsed ultrasound (LIPUS). In the second step, based on the use of polyethylene glycol (PEG) polymer, PEG‐polyphenols are applied to encapsulate radiosensitizers platinum (Pt) and assembled with sonosensitizers PEG‐purpurin 18 (PEG‐P18) into nanoparticles (NPs) as PP18‐Pt NPs with good biocompatibility and pharmacokinetic properties in combination with RT and sonodynamic therapy to achieve a high level of reactive oxygen species‐mediated tumor oxidative stress. The results show that LIPUS is a safe modality to effectively modulate hypoxia levels. The two‐step strategy based on PP18‐Pt NPs can effectively inhibit the growth of breast cancer tumors and activate the body's antitumor immune response.
Studies in Reflecting Abstraction

Introduction: Reflecting Abstraction in Context (Robert L. Campbell) Part 1: The Abstraction of Logico-arithmetic Relations. Preface: Logico-arithmetical or Algebraic Abstraction. 1. Abstraction, Differentiation, and Integration in the Use of Elementary Arithmetic Operations. 2. The Construction of Common Multiples. 3. The Inversion of Arithmetic Operations. 4. Abstraction Generalization During Transfers of Units. 5. Problems of Class Inclusion and Logical Implication. 7. The Form and Logical Implication. 8. The Formation of Analogies. 9. From Concrete Forms of the Klein Group to the INRC Group. Part 2: The Abstraction of Order. 9. Additive and Exponential Series. 10. Conditions on Reading off Complex Additive Series. 11. Ordering Practical Activity. 12. Changes in Ordering or Necessary Backtracking. Conclusion of Part Two. Part 3: The Abstraction of Spatial Relationships. 13. Relations Between the Surface Area and the Perimeter of Rectangles. 14. The Movements of a Suspended Projectile. 15. Diagonals. 16. The Displacement of a Reference Point in a System of Cyclic Movements. 17. Abstraction from Displacements and from their Coordinates. 18. Rotations and Translations. 19. The Rotation of a Bar Around a Pivot During the Sensorimotor Period. Conclusion of Part 3. General Conclusions: I. Projection. II. The Creation of Novelties Specific to Reflecting Abstraction. III. Equilibration, the Source of Noveltles, and Relationships Between the Intentions and Extensions of Structures. IV. Empirical and Reflecting Abstraction.
Effectiveness of therapeutic massage for improving motor symptoms in Parkinson's disease: A systematic review and meta-analysis

Background Parkinson's disease (PD) causes movement disorders [called motor symptoms (MS)], and motor dysfunction poses a great barrier to the quality of life. Although pharmacological therapy like levodopa can relieve the symptoms, it can also cause complications, such as psychosis, nausea, and dyskinesia. A therapy with more minor side effects is needed for PD. Therapeutic massages are the most commonly used forms of complementary and alternative medicine (CAM), but no systematic review and meta-analysis have focused on the efficacy of massage on PD. Objective To evaluate the quality of evidence and efficacy of therapeutic massage for improving MS in PD. Methods We independently searched four electronic databases, including Chinese National Knowledge Infrastructure (CNKI), MEDLINE/PubMed, Embase, and Cochrane Library, for randomized controlled trials (RCTs) about therapeutic massage and other available manual therapies improving MS in PD from January 1, 2012, to December 31, 2021 (recent 10 years). The main outcome measures were total effectiveness and the Unified Parkinson's Disease Rating Scale (UPDRS), including UPDRS total, II, and III. For the statistical analysis, the risk ratio, standard mean difference, and 95% confidence interval (CI) were used to calculate effect sizes between groups. To determine heterogeneity, statistical index I2 was used. Results A total of 363 PD participants in seven RCTs and one randomized pilot-control study were included in this meta-analysis. The total effectiveness showed that therapeutic massage was more effective than the intervention of the control group for improving MS [ratio risk (RR): 1.33, 95% CI (1.14–1.55), p = 0.0002]. The UPDRS-III scores showed that massage improves motor function more than the control group [SMD = −0.46, 95% CI (−0.67, −0.24), p < 0.00001]. But we found that massage performed no better than the control group in improving daily life activities [SMD = −0.15, 95% CI (−0.40, 0.10), p = 0.23]. Conclusion Therapeutic massage was effective in improving MS in PD. It is suggested to be an appropriate form of CAM in treating PD. Systematic Review Registration https://www.crd. york.ac. uk/PROSPERO/display_record. php? RecordID=323182, identifier: CRD42022323182.
Network pharmacology and in vitro experiments-based strategy to investigate the mechanisms of KangXianYiAi formula for hepatitis B virus-related hepatocellular carcinoma

The Chinese traditional medicine KangXianYiAi formula (KXYA) is used to treat hepatic disease in the clinic. Here we aim to confirm the therapeutic effects and explore the pharmacological mechanisms of KXYA on hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). We first collected and analyzed clinical data of 40 chronic hepatitis B (CHB) patients with precancerous liver lesions under KXYA treatment. Then, the cell viability, migration, cell cycle, and apoptosis of HepAD38 cells with KXYA treatment were examined. Next, we performed network pharmacological analysis based on database mining to obtain the key target pathways and genes of KXYA treatment on HBV-related HCC. We finally analyzed the expression of the key genes between normal and HBV-related HCC tissues in databases and measured the mRNA expression of the key genes in HepAD38 cells after KXYA treatment. The KXYA treatment could reduce the liver nodule size of CHB patients, suppress the proliferation and migration capabilities, and promote apoptosis of HepAD38 cells. The key pathways of KXYA on HBV-related HCC were Cancer, Hepatitis B, Viral carcinogenesis, Focal adhesion, and PI3K-Akt signaling, and KXYA treatment could regulate the expression of the key genes including HNF4A, MAPK8, NR3C1, PTEN, EGFR, and HDAC1. The KXYA exhibited a curative effect via inhibiting proliferation, migration, and promoting apoptosis of HBV-related HCC and the pharmacological mechanism was related to the regulation of the expression of HNF4A, MAPK8, NR3C1, PTEN, EGFR, and HDAC1.