Miracle-dz/UniFAR-FTUs huggingface dataset:
Therapeutic Response-Based Reclassification of Multiple Tumor Subtypes Reveals Intrinsic Molecular Concordance of Therapy Across Histologically Disparate Cancers

Cancers that are histologically defined as the same type of cancer often need a distinct therapy based on underlying heterogeneity; likewise, histologically disparate cancers can require similar treatment approaches due to intrinsic similarities. A comprehensive analysis integrated with drug response data and molecular alterations, particularly to reveal therapeutic concordance mechanisms across histologically disparate tumor subtypes, has not yet been fully exploited. In this study, we integrated pharmacological, genomic, and transcriptomic profiling data provided from the Cancer Genome Project (CGP) in a systematic in silico investigation of the pharmacological subtypes of cancers and the intrinsic concordance of molecular mechanisms leading to similar therapeutic responses across histologically disparate tumor subtypes. We further developed a novel approach to redefine cell-to-cell similarity and drug-to-drug similarity from the therapeutic concordance, providing a new point of view to study cancer heterogeneity. This study demonstrates how pharmacological and omics data can be used to systematically classify cancers in terms of response to various compounds and provides us with a purely therapy-oriented perspective to view tumor classifications independent of histology subtypes. The knowledge of pharmacological subtypes of 367 drugs are available via our website (http://www.hywanglab.cn/dtdb/), providing the resources for precision medicine in the perspective of therapeutic response-based re-classification of tumor.
Maintenance of Complex Trait Variation: Classic Theory and Modern Data

Numerous studies have found evidence that GWAS loci experience negative selection, which increases in intensity with the effect size of identified variants. However, there is also accumulating evidence that this selection is not entirely mediated by the focal trait and contains a substantial pleiotropic component. Understanding how selective constraint shapes phenotypic variation requires advancing models capable of balancing these and other components of selection, as well as empirical analyses capable of inferring this balance and how it is generated by the underlying biology. We first review the classic theory connecting phenotypic selection to selection at individual loci as well as approaches and findings from recent analyses of negative selection in GWAS data. We then discuss geometric theories of pleiotropic selection with the potential to guide future modeling efforts. Recent findings revealing the nature of pleiotropic genetic variation provide clues to which genetic relationships are important and should be incorporated into analyses of selection, while findings that effect sizes vary between populations indicate that GWAS measurements could be misleading if effect sizes have also changed throughout human history.
Myo-Inositol Levels in the Dorsal Hippocampus Serve as Glial Prognostic Marker of Mild Cognitive Impairment in Mice

Dementia is a devastating age-related disorder. Its therapy would largely benefit from the identification of susceptible subjects at early, prodromal stages of the disease. To search for such prognostic markers of cognitive impairment, we studied spatial navigation in male BALBc vs. B6N mice in combination with in vivo magnetic resonance spectroscopy (1H-MRS). BALBc mice consistently showed higher escape latencies than B6N mice, both in the Water Cross Maze (WCM) and the Morris water maze (MWM). These performance deficits coincided with higher levels of myo-inositol (mIns) in the dorsal hippocampus before and after training. Subsequent biochemical analyses of hippocampal specimens by capillary immunodetection and liquid chromatography mass spectrometry-based (LC/MS) metabolomics revealed a higher abundance of glial markers (IBA-1, S100B, and GFAP) as well as distinct alterations in metabolites including a decrease in vitamins (pantothenic acid and nicotinamide), neurotransmitters (acetylcholine), their metabolites (glutamine), and acetyl-L-carnitine. Supplementation of low abundant acetyl-L-carnitine via the drinking water, however, failed to revert the behavioral deficits shown by BALBc mice. Based on our data we suggest (i) BALBc mice as an animal model and (ii) hippocampal mIns levels as a prognostic marker of mild cognitive impairment (MCI), due to (iii) local changes in microglia and astrocyte activity, which may (iv) result in decreased concentrations of promnesic molecules.
Inverse computation of phase masks for two-layer diffractive optic element system

The inverse computation for the design of phase masks in two layers is investigated and a novel algorithm presented. Phase masks are also referred to as phase-only holograms or diffractive optic elements (DOE). A two layer system can transform an arbitrary specified light field at an input plane to a desired light field at an output plane. The light field includes both intensity and phase. Such a system can be cascaded for higher level functionality. There are two computations involved. The first computes a sensitivity matrix symbolically. The elements of the matrix hold the variation in each element at the output plane with variation in each element of both phase screens. An element of this matrix is provided for reference. The second algorithm iteratively updates the phase screen values to bring the output field to that desired. On each iteration, the algorithm performs a forward computation from input to output. The phase values are updated using the sensitivity matrix and the error at the output relative to that desired. A unique solution is not required, only one that provides the required transformation from input plane to output plane.
Case Report: Evolution of a Severe Vascular Refractory Form of ECD Requiring Liver Transplantation Correlated With the Change in the Monocyte Subset Analysis

Erdheim–Chester disease is a rare histiocytosis characterized by iconic features associated with compatible histology. Most patients have somatic mutations in the MAP-kinase pathway gene, and the mutations occur in CD14+ monocytes. Differentiation of the myeloid lineage plays a central role in the pathogenesis of histiocytosis. Monocytes are myeloid-derived white blood cells, divided into three subsets, but only the CD14++CD16− “classical monocyte” can differentiate into dendritic cells and tissue macrophages. Since most mutations occur in CD14+ cells and since ECD patients have a particular monocytic phenotype resembling CMML, we studied the correlation between disease activity and monocytic subset distribution during the course of a severe vascular form of ECD requiring liver transplantation. During early follow-up, increased CD14++CD16− “classical monocyte” associated with decreased CD14lowCD16++ “non-classical monocyte” correlated with disease activity. Further studies are needed to confirm the use of monocyte as a marker of disease activity in patients with ECD.
SUBSURFACE DRIP DISPERSAL OF RESIDENTIAL EFFLUENT: II. SOIL HYDRAULIC CHARACTERISTICS

Changes in soil water retention, pore size distribution, and saturated hydraulic conductivity due to wastewater application by subsurface drip systems were investigated at two sites in Texas. Undisturbed soil core samples were collected from different locations and depths around drip emitters and from outside the drip fields. Core samples were used to determine hydraulic conductivity and soil water retention. Application of wastewater resulted in increased soil water retention, decreased volume of pores with large radii, and a decrease in saturated hydraulic conductivity. The major influence of wastewater application on soil hydraulic properties occurred slightly below the drip emitter. This decrease in hydraulic conductivity reflected the decrease in volume of large soil pores compared to that of small pores. Application of treated effluent had more influence on soil hydraulic properties in the transect along the drip lateral than in a transect perpendicular to the drip lateral emitter.
MHz2k: MPC from HE over Z 2 k with New Packing, Simpler Reshare, and Better ZKP

We propose a multi-party computation (MPC) protocol over Z 2 k secure against actively corrupted majority from somewhat homomorphic encryption. The main technical contributions are: (i) a new eﬃcient packing method for Z 2 k -messages in lattice-based somewhat homomorphic encryption schemes, (ii) a simpler reshare protocol for level-dependent packings, (iii) a more eﬃcient zero-knowledge proof of plain-text knowledge on cyclotomic rings Z [ X ] /Φ M ( X ) with M being a prime. Integrating them, our protocol shows from 2.2x upto 4.8x improvements in amortized communication costs compared to the previous best results. Our techniques not only improve the eﬃciency of MPC over Z 2 k considerably, but also provide a toolkit that can be leveraged when designing other cryptographic primitives over Z 2 k .
Iterated Inhomogeneous Polynomials

Let p be a polynomial mod N , and let p ( i ) ( x ) be the result of iterating the polynomial i times, starting at x . In the case where p ( x ) = x 2 and N is the product of two large primes, p ( i ) both has a natural group structure, and also appears hard to compute in time less than i . This gives rise to interesting cryptographic applications such as time-lock puzzles and veriﬁable delay functions. In this work, we consider p ( x ) = 2 x 2 + 3 x + 1 and N = 2 n . Our initial hope was that certain features of this polynomial could be likewise used to build interesting cryptographic applications. To the contrary, and perhaps surprisingly, we show that p ( i ) ( x ) can be computed in time logarithmic in i . Moreover, we show that the discrete log problem—ﬁnding i given x and p ( i ) ( x )—can also be computed eﬃciently. We conclude with some interesting future directions to explore.
Ambiguity Detection and Textual Claims Generation from Relational Data

Computational fact checking, (given a textual claim and a table, verify if the claim holds w.r.t. the given data) and data-to-text generation (given a subset of cells, produce a sentence describing them) exploit the relationship between relational data and natural language text. Despite promising results in these areas, state-of-the-art solutions simply fail in managing “data-ambiguity", i.e., the case when there are multiple interpretations of the relationship between the textual sentence and the relational data. To tackle this problem, we present Pythia, a system that, given a relational table 𝐷 , generates textual sentences that contain factual ambiguities w.r. t. the data in 𝐷 . Such sentences can then be used to train target applications in handling data-ambiguity. In this paper, we discuss how Pythia generates data ambiguous sentences for a given table in an unsupervised fashion using data profiling and query generation. We then show how two existing downstream applications, namely data-to-text and computational fact checking, benefit from Pythia’s generated sentences, improving the state-of-the-art results without manual user effort.
Public-key Quantum money with a classical bank

Quantum money is a main primitive in quantum cryptography, that enables a bank to distribute to parties in the network, called wallets, unclonable quantum banknotes that serve as a medium of exchange between wallets. While quantum money suggests a theoretical solution to some of the fundamental problems in currency systems, it still requires a strong model to be implemented; quantum computation and a quantum communication infrastructure. A central open question in this context is whether we can have a quantum money scheme that uses "minimal quantumness", namely, local quantum computation and only classical communication. Public-key semi-quantum money (Radian and Sattath, AFT 2019) is a quantum money scheme where the algorithm of the bank is completely classical, and quantum banknotes are publicly verifiable on any quantum computer. In particular, such scheme relies on local quantum computation and only classical communication. The only known construction of public-key semi-quantum is based on quantum lightning (Zhandry, EUROCRYPT 2019), which is based on a computational assumption that is now known to be broken. In this work, we construct public-key semi-quantum money, based on quantum-secure indistinguishability obfuscation and the sub-exponential hardness of the Learning With Errors problem. The technical centerpiece of our construction is a new 3-message protocol, where a classical computer can delegate to a quantum computer the generation of a quantum state that is both, unclonable and publicly verifiable.
The Case for Multi-task Active Learning Entity Resolution (Discussion Paper)

Entity Resolution (ER) is a multi-task process that aims to detect different records in dirty datasets that refer to the same real-world entity. It is a building block for any data integration and cleaning framework. The state-of-the-art ER approaches heavily rely on supervised Machine Learning, hence on the availability of training data—which is notoriously hard to get in many contexts. To overcome this burden, many Active Learning (AL) strategies have been applied to every single task of ER to minimize the amount of training data by judiciously choosing which data to label. Yet, no study has been conducted to apply AL to ER in a holistic way. This paper aims to fill this gap by experimentally studying on real-world datasets how to tackle ER as a multi-task AL problem and provides guidelines on how to balance AL on the different ER tasks to get better results.
Activity Daily Living prediction with Marked Temporal Point Processes (Discussion Paper)

The increasingly large availability of sensors in modern houses, due to the establishment of home assistants, allow to think in terms of smart houses where behaviours can be automatized based on user habits. Common tasks required to this aim include activity prediction, i.e., the task of forecasting what is the next activity a human is going to perform in the smart space based on past sensor logs. In this discussion paper 1 , we outline a novel activity prediction method for smart houses based on the seminal probabilistic method named Marked Temporal Point Process Prediction.
Alternative Methods of Stabilizing Degrading Stream Channels in Western Iowa: Phase I

Since the beginning of channel straightening at the turn of the century, the streams of western Iowa have degraded 1.5 to 5 times their original depth. This vertical degradation is often accompanied by increases in channel widths of 2 to 4 times the original widths. The deepening and widening of these streams has jeopardized the structural safety of many bridges by undercutting footings or pile caps, exposing considerable length of piling, and removing soil beneath and adjacent to abutments. Various types of flume and drop structures have been introduced in an effort to partially or totally stabilize these channels, protecting or replacing bridge structures. Although there has always been a need for economical grade stabilization structures to stop stream channel degradation and protect highway bridges and culverts, the problem is especially critical at the present time due to rapidly increasing construction costs and decreasing revenues. Benefits derived from stabilization extend beyond the transportation sector to the agricultural sector, and increased public interest and attention is needed.
Min-Entropic Optimality

We introduce the notion of Min-Entropic Optimality thereby providing a framework for arguing that a given algorithm computes a function better than any other algorithm. An algorithm is k ( n ) Min-Entropic Optimal if for every distribution D with min-entropy at least k ( n ), its expected running time when its input is drawn from D is at most a multiplicative constant larger than the expected running time (also with respect to D ) of any other algorithm that computes the same function. Min-Entropic Optimality is a relaxation of the well established notion of instance optimality (when k ( n ) = 0). Thereby, Min-Entropic Optimality provides a meaningful notion of optimality, even in scenarios where instance optimality is inherently impossible to achieve (for instance, in the super-linear regime). We analyze basic properties of this notion and prove that for many values of k ( n ) there exist functions that have Min-Entropic Optimal algorithms. We further show that some natural search problems, such as k -Sum, are unlikely to have optimal algorithms under this notion.
A neural network strategy for supervised classification via the Learning Under Privileged Information paradigm

Devising new methodologies to handle and analyse Big Data has become a fundamental task in our increasingly service-oriented and interconnected society. One of the problems arising while handling such data is that, for a given set of entities, not all the entities may be described at the same level of detail, i.e., the number of features describing each entity may vary. In general cases, in order to apply classic data science methods, it is necessary to have a common features set over a data set. This will then correspond to the maximum number of common features among the entities, resulting in a loss of information for the entities for which additional information may be available. In order to exploit such additional information, the Learning Using Privileged Information (LUPI) paradigm has been proposed, based on the use of the teacher role in the learning process. In this schema the teacher acquires a strategic position, by exploiting at the training stage some additional privileged information about the entities, which will not be available at the test stage. In this work, we apply this paradigm in the context of neural networks, by proposing a LUPI based deep learning architecture able to exploit a larger set of attributes at training time, with the aim to improve classification performances on a set of entities associated to a reduced attribute set. Experimental results show how the proposed approach improves upon the ones applying the same schema to classic machine learning methods (e.g., SVM).
Sleepy Channels: Bitcoin-Compatible Bi-directional Payment Channels without Watchtowers

Payment channels (PC) are a promising solution to the scalability issue of cryptocurrencies, allowing users to perform the bulk of the transactions off-chain without needing to post everything on the blockchain. Many PC proposals however, suffer from a severe limitation: Both parties need to constantly monitor the blockchain to ensure that the other party did not post an outdated transaction. If this event happens, the honest party needs to react promptly and engage in a punishment procedure. This means that prolonged absence periods (e.g., due to a power outage) may be exploited by malicious users. As a mitigation, the community has introduced watchtowers , a third-party monitoring the blockchain on behalf of off-line users. Unfortunately, watchtowers are either trusted, which is critical from a security perspective, or they have to lock a certain amount of coins, called collateral, for each monitored PC in order to be held accountable, which is ﬁnancially infeasible for a large network. We present Sleepy Channels , the ﬁrst bi-directional PC protocol without watchtowers (or any other third party) that supports an unbounded number of payments and does not require parties to be persistently online. The key idea is to conﬁne the period in which PC updates can be validated on-chain to a short, pre-determined time window, which is where the PC parties have to be online. This behavior is incentivized by letting the parties lock a collateral in the PC, which can be adjusted depending on
Predicting Next-use Mobile Apps Using App Semantic Representations

: Using the app usage history of a target user as a basis, this study proposes a novel method for predicting next-use mobile apps of the user that can assist the user in selecting an app from a list of installed apps. The proposed method is designed to train a next-use app prediction model using semantic representations of the usage histories of other users (source users) to deal with the user and app cold-start problems of an app prediction system in which training data from a target user beginning to use the system and training data related to newly installed or released apps are considered to be insu ﬃ cient. We predict the usage of apps by a target user by leveraging the semantic similarities between the apps that are installed on the smartphones of the source users and the apps that are installed on the smartphone of a target user, permitting us to predict next-use apps regardless of the apps installed in the target user’s smartphone. We evaluate our method using the actual app usage data collected from 100 participants over a period of approximately 70 days with 300,000 app usage histories.
An Operation-based Versioning Approach for Synchronous and Asynchronous Collaboration in Graphical Modeling Tools

In the domain of Model-driven Engineering (MDE), modeling of software and technical systems is often a collaborative and interactive activity performed by several people. However, existing tools do not offer sufficient collaboration features as reported by studies conducted with industrial practitioners. In this paper, we introduce a new operation-based approach enabling both synchronous and asynchronous collaboration in graphical modeling tools. The presented approach is capable of conflict detection, resolving, branching, and merging. Furthermore, we demonstrate how a seamless transition between both collaboration modes can be ensured. We define user performed edit operations, such as adding a new block or changing a property’s value, as first-class citizens. Edit operations do not have to be atomic and can result in multiple atomic operations which are finally applied to the local model. Both kinds of operations are persisted in a sequential history and they are also distributed to other connected clients through a central server, which ensures a global unified history across all clients. Due to the sequential history consisting of operations and the change information they contain, we can apply a conflict detection method that is able to narrow down conflicts to the minimal possible set of individual conflicting operations for manual resolution while automatically merging the remaining changes.
From Digital to Computational Humanities: The VAST Project Vision

In the shift from Digital to Computational Humanities, the role of artificial intelligence, data science and digital technologies is fundamental to achieve advances and results. As a concrete example of Computational Humanities Research , we present the vision of the EU H2020 VAST project ( Values Across Space and Time ) recently started, where cutting edge digital technologies and knowledge modeling tools are in place to study how the meaning of European moral values has been expressed, transformed, and appropriated throughout time, going back to the stories that helped to shape part of the European culture.
Conversational OLAP (Discussion Paper)

The democratization of data access and the adoption of OLAP in scenarios requiring hand-free interfaces push towards the creation of smart OLAP interfaces. In this paper, we describe COOL, a framework devised for COnversational OLap applications. COOL interprets and translates a natural language dialog into an OLAP session that starts with a GPSJ (Generalized Projection, Selection, and Join) query and continues with the application of OLAP operators. The interpretation relies on a formal grammar and on a repository storing metadata and values from a multidimensional cube. In case of ambiguous text description, COOL can obtain the correct query either through automatic inference or user interactions to disambiguate the text.