Miracle-dz/UniFAR-FTUs huggingface dataset:
Fluctuating Environment Can Negate Cheater Success Due to Speed-Agility Trade-Off

Stability of microbial cooperation through common goods is susceptible to cheating. Evidence suggests that cheating plays a less prominent role in many natural systems than hitherto predicted by models of eco-evolutionary dynamics and evolutionary game theory. While several cheater negating factors such as spatial segregation have been identified, most consider single-nutrient regimes. Here we propose a cheater-suppressing mechanism based on previous experimental observations regarding the biochemical trade-off between growth speed and delay in switching to alternative nutrients. As changing the nutrient source requires redistribution of enzymatic resources to different metabolic pathways, the advantage in speed is offset by lower agility due to longer time required for resource re-allocation. Using an in silico model system of sucrose utilisation by Saccharomyces cerevisiae, we find that a tradeoff between growth rate and diauxic lag duration can supress cheaters under fluctuating nutrient availability and thereby stabilise cooperation. The resulting temporal dynamics constrain cheaters despite their competitive benefit for the growth on the primary nutrient via avoided public goods synthesis costs. We further show that this speed-agility trade-off can function in synergy with spatial segregation to avoid the collapse of the community due to the cheaters. Taken together, the growth-agility trade-off may contribute to cheater suppression in microbial ecosystems experiencing fluctuating environments, such as plant root microbiota and gut microbiota.
An exploration of metabolite and gene responses in mouse skeletal muscles responding to acute sedentary hypoxia

Skeletal muscles are involved in responses to acute hypoxia as the largest organ in the body. However, as a hypoxic-tolerant tissue, responses in skeletal muscles caused by acute sedentary hypoxia are much less studied. We measured metabolites in skeletal muscles from mice exposed to 8% O2 for 0 minute, 15 minutes and 2 hours and studied the potential relationship between metabolite levels and mRNA levels by reconstructing genome-based metabolic networks and meta-analyzing differentially expressed genes acquired in skeletal muscles after 2 hours of 8% O2 exposure. The metabolite measurement indicated a significant increase in glutamine metabolism but not lactate metabolism in mouse skeletal muscles after 2 hours of hypoxia, where the metabolic responses as a whole were moderate. The central-dogma based metabolic flux analysis suggested an involvement of glutamine metabolism, though, as a whole, metabolite changes and gene changes didn’t show a high correlation. Among metaoblites, glutamine metabolism indicated a significant response and a consistent change which could be interpreted by genome-based network analysis. In summary, though this study suggested a moderate metabolic response which has a weak correlation with gene expression change as a whole, glutamine metabolism indicated rapid responses in skeletal muscles responding to acute sedentary hypoxia.
Discovery of a pre-mRNA structural scaffold as a contributor to the mammalian splicing code

The specific recognition of splice signals at or near exon-intron junctions is not explained by their weak conservation and instead is postulated to require a multitude of features embedded in the pre-mRNA strand. We explored the possibility of three-dimensional structural scaffold of AdML – a model pre-mRNA substrate – guiding early spliceosomal components to the splice signal sequences. We find that mutations in the non-cognate splice signal sequences impede recruitment of early spliceosomal components due to disruption of the global structure of the pre-mRNA. We further find that the pre-mRNA segments potentially interacting with the early spliceosomal component U1 snRNP are distributed across the intron, that there is a spatial proximity of 5′ and 3′ splice sites within the pre-mRNA scaffold, and that an interplay exists between the structural scaffold and splicing regulatory elements in recruiting early spliceosomal components. These results suggest that early spliceosomal components can recognize a three-dimensional structural scaffold beyond the short splice signal sequences, and that in our model pre-mRNA, this scaffold is formed across the intron involving the major splice signals. This provides a conceptual basis to analyze the contribution of recognizable three-dimensional structural scaffolds to the splicing code across the mammalian transcriptome.
Crystal Structure of a Retroviral Polyprotein: Prototype Foamy Virus Protease-Reverse Transcriptase (PR-RT)

In most cases, proteolytic processing of the retroviral Pol portion of the Gag-Pol polyprotein precursor produces protease (PR), reverse transcriptase (RT), and integrase (IN). However, foamy viruses (FVs) express Pol separately from Gag and, when Pol is processed, only the IN domain is released. Here, we report a 2.9 Å resolution crystal structure of the mature PR-RT from prototype FV (PFV) that can carry out both proteolytic processing and reverse transcription but is in a configuration not competent for proteolytic or polymerase activity. PFV PR-RT is monomeric and the architecture of PFV PR is similar to one of the subunits of HIV-1 PR, which is a dimer. There is a C-terminal extension of PFV PR (101-145) that consists of two helices which are adjacent to the base of the RT palm subdomain, and anchors PR to RT. The polymerase domain of PFV RT consists of fingers, palm, thumb, and connection subdomains whose spatial arrangements are similar to the p51 subunit of HIV-1 RT. The RNase H and polymerase domains of PFV RT are connected by flexible linkers. Significant spatial and conformational (sub)domain rearrangements are therefore required for nucleic acid binding. The structure of PFV PR-RT provides insights into the conformational maturation of retroviral Pol polyproteins.
Adam in Private: Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation

Machine Learning (ML) algorithms, especially deep neural networks (DNN), have proven themselves to be extremely useful tools for data analysis, and are increasingly being deployed in systems operating on sensitive data, such as recommendation systems, banking fraud detection, and healthcare systems. This underscores the need for privacy-preserving ML (PPML) systems, and has inspired a line of research into how such systems can be constructed efficiently. However, most prior works on PPML achieve efficiency by requiring advanced ML algorithms to be simplified or substituted with approximated variants that are “MPC-friendly” before multi-party computation (MPC) techniques are applied to obtain a PPML systems. A drawback of this approach is that it requires careful fine-tuning of the combined ML and MPC algorithms, and might lead to less efficient algorithms or inferior quality ML (such as lower prediction accuracy). This is an issue for secure training of DNNs in particular, as this involves several arithmetic algorithms that are thought to be “MPCunfriendly”, namely, integer division, exponentiation, inversion, and square root extraction. In this work, we take a structurally different approach and propose a framework that allows efficient and secure evaluation of full-fledged state-of-the-art ML algorithms via secure multi-party computation. Specifically, we propose secure and efficient protocols for the above seemingly MPC-unfriendly computations (but which are essential to DNN). Our protocols are three-party protocols in the honest-majority setting, and we propose both passively secure and actively secure with abort variants. A notable feature of our protocols is that they simultaneously provide high accuracy and efficiency. This framework enables us to efficiently and securely compute modern ML algorithms such as Adam (Adaptive moment estimation) and the softmax function “as is”, without resorting to approximations. As a result, we obtain secure DNN training that outperforms state-of-the-art threeparty systems; our full training is up to 6.7 times faster than just the online phase of FALCON (Wagh et al. at PETS’21) and up to 4.2 times faster than Dalskov et al. (USENIX’21) on the standard benchmark network for secure training of DNNs. The potential advantage of our approach is even greater when considering more complex realistic networks. To demonstrate this, we perform measurements on real-world DNNs, AlexNet and VGG16, which are large networks containing millions of parameters. The performance of our framework for these networks is up to a factor of 26 ∼ 33 faster for AlexNet and 48 ∼ 51 faster for VGG16 to achieve an accuracy of 60% and 70%, respectively, when compared to FALCON. Even compared to CRYPTGPU (Tan et al. IEEE S&P’21), which is optimized for and runs on powerful GPUs, our framework achieves a factor of 2.1 and 4.1 faster performance, respectively, on these networks.
zkCNN: Zero Knowledge Proofs for Convolutional Neural Network Predictions and Accuracy

Deep learning techniques with neural networks are developing prominently in recent years and have been deployed in numerous applications. Despite their great success, in many scenarios it is important for the users to validate that the inferences are truly computed by legitimate neural networks with high accuracy, which is referred to as the integrity of machine learning predictions. To address this issue, in this paper, we propose zkCNN, a zero knowledge proof scheme for convolutional neural networks (CNN). The scheme allows the owner of the CNN model to prove to others that the prediction of a data sample is indeed calculated by the model, without leaking any information about the model itself. Our scheme can also be generalized to prove the accuracy of a secret CNN model on a public dataset. Underlying zkCNN is a new sumcheck protocol for proving fast Fourier transforms and convolutions with a linear prover time, which is even faster than computing the result asymptotically. We also introduce several improvements and generalizations on the interactive proofs for CNN predictions, including verifying the convolutional layer, the activation function of ReLU and the max pooling. Our scheme is highly efficient in practice. It can support the large CNN of VGG16 with 15 million parameters and 16 layers. It only takes 88.3 seconds to generate the proof, which is 1264× faster than existing schemes. The proof size is 341 kilobytes, and the verifier time is only 59.3 milliseconds. Our scheme can further scale to prove the accuracy of the same CNN on 20 images.
OSHA: A General-purpose One-way Secure Hash Algorithm

Secure hash functions are widely used cryptographic algorithms to secure diverse attacks. A one-way secure hash function is used in the various cryptographic area, for instance, password protection. However, most of the hash functions provide security based on static parameters and publicly known operations. Therefore, it becomes easier to attack by the attackers because all parameters and operations are predeﬁned. The publicly known parameters and predeﬁned operations make the oracle regenerate the key even though it is a one-way secure hash function. Moreover, the key (sensitive data) is mixed with the pre-deﬁned constant where an oracle may ﬁnd a way to discover the key. To address the above issues of the secure hash functions, we propose a novel and one-way secure hash algorithm, OSHA for short, to protect sensitive data against attackers. OSHA depends on a pseudo-random number generator to generate a private key. Moreover, OSHA mixes multiple private keys to generate a hash value. Furthermore, OSHA uses dynamic parameters, which is diﬃcult for adversaries to guess. Unlike conventional secure hash algorithms, OSHA does not depend on ﬁxed constants. It replaces the ﬁxed constant with the private keys. Also, the key is not mixed with the private keys; hence, there is no way to recover and reverse the process for the adversaries.
GoAT: File Geolocation via Anchor Timestamping

Blockchain systems are rapidly gaining traction. Decentralized storage systems like Filecoin are a crucial component of this ecosystem that aim to provide robust file storage through a Proof of Replication (PoRep) or its variants. However, a PoRep actually offers limited robustness. Indeed if all the file replicas are stored on a single hard disk, a single catastrophic event is enough to lose the file. We introduce a new primitive, Proof of Geo-Retrievability or in short PoGeoRet, that enables proving that a file is located within a strict geographic boundary. Using PoGeoRet, one can trivially construct a PoRep by proving that a file is in several distinct geographic regions. We define what it means for a PoGeoRet scheme to be complete and sound, in the process making important extensions to prior formalism. We propose GoAT , a practical PoGeoRet scheme to prove file geolocation. Unlike previous geolocation systems that make strong assumptions about storage providers and require dedicated anchors, GoAT makes minimal assumptions and uses existing timestamping servers on the internet as geolocation anchors. GoAT internally uses a communication-efficient Proof-of-Retrievability (PoRet) scheme in a novel way to achieve constant-size PoRet-component in its proofs. We validate GoAT ’s practicality by conducting an initial measurement study to find usable anchors and perform a real-world experiment. The results show that a significant fraction of the internet can be used as anchors and that GoAT achieves geolocation radii as low as 500km.
Client-Auditable Verifiable Registries

Veriﬁable registries allow clients to securely access a key-value mapping maintained by an untrusted server. Applications include distribution of public keys, routing information or software binaries. Existing proposals for veriﬁable registries rely on global invariants being audited whenever the registry is updated. Clients typically rely on trusted third-party auditors, as large registries become expensive to audit. We propose several new protocols for client-auditable registries that enable efﬁcient veriﬁcation of many updates to the registry, removing the need for third-party auditors. Our solutions use incrementally-veriﬁable computation (IVC) and/or RSA accumulators. Our evaluation shows that our constructions meet practical throughput requirements ( 60 updates / second), which is 100 × faster than naive solutions using IVC. Clients save 100 – 10 4 × bandwidth and computation costs over prior solutions requiring auditing every update.
Mystique: Efficient Conversions for Zero-Knowledge Proofs with Applications to Machine Learning

Recent progress in interactive zero-knowledge (ZK) proofs has improved the efficiency of proving large-scale computations significantly. Nevertheless, real-life applications (e.g., in the context of private inference using deep neural networks) often involve highly complex computations, and existing ZK protocols lack the expressiveness and scalability to prove results about such computations efficiently. In this paper, we design, develop, and evaluate a ZK system (Mystique) that allows for efficient conversions between arithmetic and Boolean values, between publicly committed and privately authenticated values, and between fixed-point and floating-point numbers. Targeting large-scale neural-network inference, we also present an improved ZK protocol for matrix multiplication that yields a 7× improvement compared to the state-of-the-art. Finally, we incorporate Mystique in Rosetta, a TensorFlow-based privacy-preserving framework. Mystique is able to prove correctness of an inference on a private image using a committed (private) ResNet-101 model in 28 minutes, and can do the same task when the model is public in 5 minutes, with only a 0.02% decrease in accuracy compared to a non-ZK execution when testing on the CIFAR10 dataset. Our system is the first to support ZK proofs about neural-network models with over 100 layers with virtually no loss of accuracy.
Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path

Networks found in the real-world are numerous and varied. A common type of network is the heterogeneous network, where the nodes (and edges) can be of different types. Accordingly, there have been efforts at learning representations of these heterogeneous networks in low-dimensional space. However, most of the existing heterogeneous network embedding suffers from the following two drawbacks: (1) The target space is usually Euclidean. Conversely, many recent works have shown that complex networks may have hyperbolic latent anatomy, which is non-Euclidean. (2) These methods usually rely on meta-paths, which requires domain-specific prior knowledge for meta-path selection. Additionally, different down-streaming tasks on the same network might require different meta-paths in order to generate task-specific embeddings. In this paper, we propose a novel self-guided random walk method that does not require meta-path for embedding heterogeneous networks into hyperbolic space. We conduct thorough experiments for the tasks of network reconstruction and link prediction on two public datasets, showing that our model outperforms a variety of well-known baselines across all tasks.
Projection-free Online Learning in Dynamic Environments

To efficiently solve high-dimensional problems with complicated constraints, projection-free online learning has received ever-increasing research interest. However, previous studies either focused on static regret that is not suitable for dynamic environments, or only established the dynamic regret bound under the smoothness of losses. In this paper, without the condition of the smoothness, we propose a novel projection-free online algorithm, and achieve an O(max{T^{2/3}V_T^{1/3},T^{1/2}}) dynamic regret bound for convex functions and an O(max{(TV_Tlog T)^{1/2},log T}) dynamic regret bound for strongly convex functions, where T is the time horizon and V_T denotes the variation of loss functions. Specifically, we first improve an existing projection-free algorithm called online conditional gradient (OCG) to enjoy small dynamic regret bounds with the prior knowledge of V_T. To work with unknowable V_T, we maintain multiple instances of the improved OCG that can handle different functional variations, and combine them with a meta-algorithm that can track the best one. Experimental results validate the efficiency and effectiveness of our algorithm.
Meta-Learning Effective Exploration Strategies for Contextual Bandits

In contextual bandits, an algorithm must choose actions given ob- served contexts, learning from a reward signal that is observed only for the action chosen. This leads to an exploration/exploitation trade-off: the algorithm must balance taking actions it already believes are good with taking new actions to potentially discover better choices. We develop a meta-learning algorithm, Mêlée, that learns an exploration policy based on simulated, synthetic con- textual bandit tasks. Mêlée uses imitation learning against these simulations to train an exploration policy that can be applied to true contextual bandit tasks at test time. We evaluate Mêlée on both a natural contextual bandit problem derived from a learning to rank dataset as well as hundreds of simulated contextual ban- dit problems derived from classification tasks. Mêlée outperforms seven strong baselines on most of these datasets by leveraging a rich feature representation for learning an exploration strategy.
Approximate Range Counting Under Differential Privacy

Range counting under diﬀerential privacy has been studied extensively. Unfortunately, lower bounds based on discrepancy theory suggest that large errors have to be introduced in order to preserve privacy: Essentially for any range space (except axis-parallel rectangles), the error has to be polynomial. In this paper, we show that by allowing a standard notion of geometric approximation where points near the boundary of the range may or may not be counted, the error can be reduced to logarithmic. Furthermore, our approximate range counting data structure can be used to solve the approximate nearest neighbor (ANN) problem and k-NN classiﬁcation, leading to the ﬁrst diﬀerentially private algorithms for these two problems with provable guarantees on the utility.
Stochastic Bandits with Graph Feedback in Non-Stationary Environments

We study a variant of stochastic bandits where the feedback model is specified by a graph. In this setting, after playing an arm, one can observe rewards of not only the played arm but also other arms that are adjacent to the played arm in the graph. Most of the existing work assumes the reward distributions are stationary over time, which, however, is often violated in common scenarios such as recommendation systems and online advertising. To address this limitation, we study stochastic bandits with graph feedback in non-stationary environments and propose algorithms with graph-dependent dynamic regret bounds. When the number of reward distribution changes L is known in advance, one of our algorithms achieves an Õ(√(αLT)) dynamic regret bound. We also develop an adaptive algorithm that can adapt to unknown L and attain an Õ(√(θLT)) dynamic regret. Here, α and θ are some graph-dependent quantities and T is the time horizon.
Instrumental Variable-based Identification for Causal Effects using Covariate Information

This paper deals with the identification problem of causal effects in randomized trials with noncompliance. In this problem, generally, causal effects are not identifiable and thus have been evaluated under some strict assumptions, or through the bounds. Different from existing studies, we propose novel identification conditions of joint probabilities of potential outcomes, which allow us to derive a consistent estimator of the causal effect. Regarding the identification conditions of joint probabilities of potential outcomes, the assumptions of monotonicity (Pearl, 2009), independence between potential outcomes (Robins & Richardson, 2011), gain equality (Li & Pearl, 2019) and specific functional relationships between cause and effect (Pearl, 2009) have been utilized. In contrast, without such assumptions, the proposed conditions enable us to evaluate joint probabilities of potential outcomes using an instrumental variable and a proxy variable of potential outcomes. The results of this paper extend the range of solvable identification problems in causal inference.