Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesizing MILP Constraints for Efficient and Robust Optimization

While mixed integer linear programming (MILP) solvers are routinely used to solve a wide range of important science and engineering problems, it remains a challenging task for end users to write correct and efficient MILP constraints, especially for problems specified using the inherently non-linear Boolean logic operations. To overcome this challenge, we propose a syntax guided synthesis (SyGuS) method capable of generating high-quality MILP constraints from the specifications expressed using arbitrary combinations of Boolean logic operations. At the center of our method is an extensible domain specification language (DSL) whose expressiveness may be improved by adding new integer variables as decision variables, together with an iterative procedure for synthesizing linear constraints from non-linear Boolean logic operations using these integer variables. To make the synthesis method efficient, we also propose an over-approximation technique for soundly proving the correctness of the synthesized linear constraints, and an under-approximation technique for safely pruning away the incorrect constraints. We have implemented and evaluated the method on a wide range of benchmark specifications from statistics, machine learning, and data science applications. The experimental results show that the method is efficient in handling these benchmarks, and the quality of the synthesized MILP constraints is close to, or higher than, that of manually-written constraints in terms of both compactness and solving time.
Trace-Guided Inductive Synthesis of Recursive Functional Programs

We propose a novel trace-guided approach to tackle the challenges of ambiguity and generalization in synthesis of recursive functional programs from input-output examples. Our approach augments the search space of programs with recursion traces consisting of recursive subcalls of the programs. Our method is based on a new version space algebra (VSA) for succinct representation and efficient manipulation of pairs of recursion traces and programs that are consistent with each other. We have implemented this approach in a tool called SyRup and evaluated it on benchmarks from prior work. Our evaluation demonstrates that SyRup not only requires fewer examples to achieve a certain success rate than existing synthesizers, but is also less sensitive to the quality of the examples.
Probabilistic Programming with Stochastic Probabilities

We present a new approach to the design and implementation of probabilistic programming languages (PPLs), based on the idea of stochastically estimating the probability density ratios necessary for probabilistic inference. By relaxing the usual PPL design constraint that these densities be computed exactly, we are able to eliminate many common restrictions in current PPLs, to deliver a language that, for the first time, simultaneously supports first-class constructs for marginalization and nested inference, unrestricted stochastic control flow, continuous and discrete sampling, and programmable inference with custom proposals. At the heart of our approach is a new technique for compiling these expressive probabilistic programs into randomized algorithms for unbiasedly estimating their densities and density reciprocals. We employ these stochastic probability estimators within modified Monte Carlo inference algorithms that are guaranteed to be sound despite their reliance on inexact estimates of density ratios. We establish the correctness of our compiler using logical relations over the semantics of λSP, a new core calculus for modeling and inference with stochastic probabilities. We also implement our approach in an open-source extension to Gen, called GenSP, and evaluate it on six challenging inference problems adapted from the modeling and inference literature. We find that: (1) can automate fast density estimators for programs with very expensive exact densities; (2) convergence of inference is mostly unaffected by the noise from these estimators; and (3) our sound-by-construction estimators are competitive with hand-coded density estimators, incurring only a small constant-factor overhead.
A Privacy-preserving Auction Mechanism for Learning Model as an NFT in Blockchain-Driven Metaverse

The Metaverse, envisioned as the next-generation Internet, will be constructed via twining a practical world in a virtual form, wherein Meterverse service providers (MSPs) are required to collect massive data from Meterverse users (MUs). In this regard, a critical demand exists for MSPs to motivate MUs to contribute computing resources and data while preserving user privacy. Federated learning (FL), as a privacy-preserving collaborative machine learning paradigm, can support distributed intensive computation in Metaverse. In this work, we first investigate minting the machine learning models into NFT with FL assistance (referred to as FL-NFT), such that MUs as stakeholders can control the ownership and share the economic value of user-generated content (UGC). Specifically, MUs are encouraged to establish a decentralized autonomous organization (i.e., MU-DAO) to aggregate local models and mint FL-NFT. MUs and MSPs optimize the strategies by formulating an imperfect information Stackelberg game (IISG) to trade off the cost and benefit. We apply the backward induction to derive the equilibrium solution. Then, we construct a privacy-preserving multi-winner sealed-bid auction mechanism (PMS-AM), in which the Hidden Markov Model (HMM) assists MSPs in choosing rational bidding strategies according to historical bids, and the double auction mechanism determines the winners and price of FL-NFT. Finally, the numerical results based on theoretical analysis and simulations demonstrate that the proposed PMS-AM can increase the quality of FL-NFT and achieve the economic properties of incentive mechanisms such as individual rationality and incentive compatibility.
Taype: A Policy-Agnostic Language for Oblivious Computation

Secure multiparty computation (MPC) allows for joint computation over private data from multiple entities, usually backed by powerful cryptographic techniques that protect sensitive data. Several high-level programming languages have been proposed to make writing MPC applications accessible to non-experts. These languages typically require developers to enforce security policies within the logic of the secure application itself, making it difficult to update security requirements, or to experiment with different policies. This paper presents the design and implementation of Taype, a language that permits security concerns to be decoupled from the program logic. To do so, Taype provides the first implementation of oblivious algebraic data types and tape semantics, two language features recently proposed by a core calculus for oblivious computation, λOADT+. We evaluate our implementation of Taype on a range of benchmarks, demonstrating its ability to encode a range of security polices for a rich class of data types.
Conflict-Driven Synthesis for Layout Engines

Modern web browsers rely on layout engines to convert HTML documents to layout trees that specify color, size, and position. However, existing layout engines are notoriously difficult to maintain because of the complexity of web standards. This is especially true for incremental layout engines, which are designed to improve performance by updating only the parts of the layout tree that need to be changed. In this paper, we propose Medea, a new framework for automatically generating incremental layout engines. Medea separates the specification of the layout engine from its incremental implementation, and guarantees correctness through layout engine synthesis. The synthesis is driven by a new iterative algorithm based on detecting conflicts that prevent optimality of the incremental algorithm. We evaluated Medea on a fragment of HTML layout that includes challenging features such as margin collapse, floating layout, and absolute positioning. Medea successfully synthesized an incremental layout engine for this fragment. The synthesized layout engine is both correct and efficient. In particular, we demonstrated that it avoids real-world bugs that have been reported in the layout engines of Chrome, Firefox, and Safari. The incremental layout engine synthesized by Medea is up to 1.82× faster than a naive incremental baseline. We also demonstrated that our conflict-driven algorithm produces engines that are 2.74× faster than a baseline without conflict analysis.
Training for Heat-of-the-Moment Thinking: Ethics Training to Prepare for Operations

Military ethics training has tended to focus on imparting ethical attitudes and on improving deliberative moral decision-making through classroom instruction. However, military personnel can be exposed to extreme conditions on operations, which can lead to heat-of-the-moment thinking. Under stress, individuals are more likely to engage in automatic processing than deliberative processing, and visceral states such as anger and disgust can increase a person’s risk of behaving unethically. We propose that military ethics training could be improved by reinforcing classroom ethics training with interventions to counteract these risk factors. As training interventions, we recommend incorporating affect-labeling, goal-setting, and perspective-taking into realistic, pre-deployment training to make moral decision-making more robust against stress and other emotional experiences typical in combat. We outline steps researchers and trainers can take to test whether these interventions have the desired impact on ethical behavior.
Osteopathic manipulative treatment for concussions and postconcussive syndrome in athletes: a literature review

Abstract Context Concussions are the most common type of traumatic brain injury (TBI) and can often occur in athletes. These injuries have many deleterious acute symptoms and can lead to the development of postconcussive syndrome (PCS). Osteopathic manipulative treatment (OMT) is a treatment option that may benefit patients with concussions and PCS. Objectives The objective of this review is to determine whether OMT can improve symptoms associated with concussions and PCS in athletes. Methods A comprehensive literature review was conducted between August 2021 and March 2022 by two authors (Z.K.L. and K.D.T.) who searched the literature utilizing PubMed, Google Scholar, and Cochrane Library. Articles reviewed included case reports, case studies, randomized control trials, meta-analyses, and peer-reviewed journal articles. Search terms included concussion, postconcussive symptoms, osteopathic manipulative medicine, and manipulation. To be included into this study, articles must have included OMT from an osteopathic physician or manipulative techniques by nonosteopathic providers for patients with a concussion or PCS, and the insulting injury must have occurred in an athletic setting. No disagreements occurred between authors about what studies to include. However, we were prepared to come to a unanimous decision through discussion among the authors. A narrative synthesis was performed. No other data analyses were conducted in this study. Results Included in this review were nine articles including randomized control trials, retrospective review, case series, longitudinal, retrospective studies, and case reports. The literature shows the positive effects of OMT and manipulative techniques on symptom resolution after a concussion. However, most of the literature is qualitative rather than quantitative in nature, lacking in randomized control trials. Conclusions There is a scarcity of high-quality studies evaluating the effectiveness of OMT on concussions and PCS. More research is needed to understand the degree of benefit for this treatment option.
Metrica per famiglie di domini limitati e proprietà generiche degli autovalori

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Modelling The Hot Rolling Process Using A Finite Volume Approach

A finite volume model for the hot rolling process is proposed to determine useful parameters in the hot rolling such as the loads, temperature, stress and strain fields and the microstructure. A non-Newtonian formulation and a Eulerian description are adopted with the finite volume method. The strain-rate field is firstly obtained from the pressure and velocities solutions. Then, the temperature field together with a constitutive equation provides the apparent viscosity of the material and thus the stress field. The adopted constitutive equations describe the microstructural phenomena occurring in the hot rolling, i.e., work-hardening, recovery and recrystallisation. The predictions obtained with the proposed finite volume model are in good agreement with the industrial measured rolling loads and surface temperatures for the 7-stand finishing mill of a C-Mn steel strip.
Accelerometer and multi-sensor technology

Many different types of inertial sensors can be used for sensing and measuring the magnitude of an 'accelerating' force. These sensors are of many different types and design. The review has included the mechanical sensors, using the classical pendulum principle, to the modern solid-state devices. Generally, all these instruments are suitable for strapdown applications and in such an environment will give accuracies ranging from tens of micro-gravitational acceleration (micro-g) to fractions of a 'g'. The mechanical accelerometers come in various forms, with a selection of materials and designs for the pendulum's hinge mechanism. These sensors may be fluid filled in order to improve the damping of the motion of the pendulum. The pendulum may be constrained to very small displacements, through the use of force feedback techniques, in order to achieve high accuracy. Alternatively, the sensor may be operated in an open-loop mode.
Deep Phenotyping of the Lipidomic Response in COVID and non-COVID Sepsis

Lipids may influence cellular penetrance by pathogens and the immune response that they evoke. Here we find a broad based lipidomic storm driven predominantly by secretory (s) phospholipase A2 (sPLA2) dependent eicosanoid production occurs in patients with sepsis of viral and bacterial origin and relates to disease severity in COVID-19. Elevations in the cyclooxygenase (COX) products of arachidonic acid (AA), PGD2 and PGI2, and the AA lipoxygenase (LOX) product, 12-HETE, and a reduction in the high abundance lipids, ChoE 18:3, LPC-O-16:0 and PC-O-30:0 exhibit relative specificity for COVID-19 amongst such patients, correlate with the inflammatory response and link to disease severity. Linoleic acid (LA) binds directly to SARS-CoV-2 and both LA and its di-HOME products reflect disease severity in COVID-19. AA and LA metabolites and LPC-O-16:0 linked variably to the immune response. These studies yield prognostic biomarkers and therapeutic targets for patients with sepsis, including COVID-19. An interactive purpose built interactive network analysis tool was developed, allowing the community to interrogate connections across these multiomic data and generate novel hypotheses.
Brain cell-specific origin of circulating microRNA biomarkers in experimental temporal lobe epilepsy

The diagnosis of epilepsy is complex and challenging and would benefit from the availability of molecular biomarkers, ideally measurable in a biofluid such as blood. Experimental and human epilepsy are associated with altered brain and blood levels of various microRNAs (miRNAs). Evidence is lacking, however, as to whether any of the circulating pool of miRNAs originates from the brain. To explore the link between circulating miRNAs and the pathophysiology of epilepsy, we first sequenced argonaute 2 (Ago2)-bound miRNAs in plasma samples collected from mice subject to status epilepticus induced by intraamygdala microinjection of kainic acid. This identified time-dependent changes in plasma levels of miRNAs with known neuronal and microglial-cell origins. To explore whether the circulating miRNAs had originated from the brain, we generated mice expressing FLAG-Ago2 in neurons or microglia using tamoxifen-inducible Thy1 or Cx3cr1 promoters, respectively. FLAG immunoprecipitates from the plasma of these mice after seizures contained miRNAs, including let-7i-5p and miR-19b-3p. Taken together, these studies confirm that a portion of the circulating pool of miRNAs in experimental epilepsy originates from the brain, increasing support for miRNAs as mechanistic biomarkers of epilepsy.
Mapping the genomic landscape of multidrug resistance in Plasmodium falciparum and its impact on parasite fitness

Drug-resistant Plasmodium falciparum parasites have swept across Southeast Asia and now threaten Africa. By implementing a P. falciparum genetic cross using humanized mice, we report the identification of key determinants of resistance to artemisinin (ART) and piperaquine (PPQ) in the dominant Asian KEL1/PLA1 lineage. We mapped k13 as the central mediator of ART resistance and identified secondary markers. Applying bulk segregant analysis, quantitative trait loci mapping and gene editing, our data reveal an epistatic interaction between mutant PfCRT and multicopy plasmepsins 2/3 in mediating high-grade PPQ resistance. Susceptibility and parasite fitness assays implicate PPQ as a driver of selection for KEL1/PLA1 parasites. Mutant PfCRT enhanced susceptibility to lumefantrine, the first-line partner drug in Africa, highlighting a potential benefit of opposing selective pressures with this drug and PPQ. We also identified that the ABCI3 transporter can operate in concert with PfCRT and plasmepsins 2/3 in mediating multigenic resistance to antimalarial agents.
The small protein MntS evolved from a signal peptide and acquired a novel function regulating manganese homeostasis in Escherichia coli

Small proteins (<50 amino acids) are emerging as ubiquitous and important regulators in organisms ranging from bacteria to humans, where they commonly bind to and regulate larger proteins during stress responses. However, fundamental aspects of small proteins, such as their molecular mechanism of action, downregulation after they are no longer needed, and their evolutionary provenance, are poorly understood. Here, we show that the MntS small protein involved in manganese (Mn) homeostasis binds and inhibits the MntP Mn transporter. Mn is crucial for bacterial survival in stressful environments but is toxic in excess. Thus, Mn transport is tightly controlled at multiple levels to maintain optimal Mn levels. The small protein MntS adds a new level of regulation for Mn transporters, beyond the known transcriptional and post‐transcriptional control. We also found that MntS binds to itself in the presence of Mn, providing a possible mechanism of downregulating MntS activity to terminate its inhibition of MntP Mn export. MntS is homologous to the signal peptide of SitA, the periplasmic metal‐binding subunit of a Mn importer. Remarkably, the homologous signal peptide regions can substitute for MntS, demonstrating a functional relationship between MntS and these signal peptides. Conserved gene neighborhoods support that MntS evolved from the signal peptide of an ancestral SitA protein, acquiring a life of its own with a distinct function in Mn homeostasis.
Novel histones and histone variant families in prokaryotes

Histones are important chromatin-organising proteins in eukaryotes and archaea. They form superhelical structures around which DNA is wrapped. Recent studies have shown that some archaea and bacteria contain alternative histones that exhibit different DNA binding properties, in addition to highly divergent sequences. However, the vast majority of these new histones are identified in metagenomes and thus are difficult to study in vivo. The recent revolutionary breakthroughs in in silico protein structure prediction by AlphaFold2 and RoseTTAfold allow for unprecedented insights into the possible function and structure of currently unstudied proteins. Here, we categorise the complete prokaryotic histone space into 17 distinct groups based on AlphaFold predictions. We identify a new superfamily of histones, termed α3 histones, which are common in archaea and present in several bacteria. Importantly, we establish the existence of a large family of histones throughout archaea and in some bacteriophages that, instead of wrapping DNA, likely bridge DNA, therewith diverging from conventional nucleosomal histones.
The PALB2 DNA-binding domain is an intrinsically disordered recombinase

The Partner and Localizer of BRCA2 (PALB2) tumor suppressor is a scaffold protein that links BRCA1 with BRCA2 to initiate homologous recombination (HR). PALB2 interaction with DNA strongly enhances HR efficiency. The PALB2 DNA-binding domain (PALB2-DBD) supports DNA strand exchange, a complex multistep reaction supported by only a few protein families such as RecA-like recombinases or Rad52. The mechanisms of PALB2 DNA binding and strand exchange are unknown. We performed circular dichroism, electron paramagnetic spectroscopy, and small-angle X-ray scattering analyses and determined that PALB2-DBD is intrinsically disordered, even when bound to DNA. The intrinsically disordered nature of this domain was further supported by bioinformatics analysis. Intrinsically disordered proteins (IDPs) are prevalent in the human proteome and have many important biological functions. The complexity of the strand exchange reaction significantly expands the functional repertoire of IDPs. The results of confocal single-molecule FRET indicated that PALB2-DBD binding leads to oligomerization-dependent DNA compaction. We hypothesize that PALB2-DBD uses a chaperone-like mechanism to aid formation and resolution of complex DNA and RNA multichain intermediates during DNA replication and repair. Since PALB2-DBD alone or within the full-length PALB2 is predicted to have strong liquid-liquid phase separation (LLPS) potential, protein-nucleic acids condensates are likely to play a role in complex functionality of PALB2-DBD. Similar DNA-binding intrinsically disordered regions may represent a novel class of functional domains that evolved to function in eukaryotic nucleic acid metabolism complexes.