Miracle-dz/UniFAR-FTUs huggingface dataset:
Position-based Scaled Gradient for Model Quantization and Sparse Training

We propose the position-based scaled gradient (PSG) that scales the gradient depending on the position of a weight vector to make it more compression-friendly. First, we theoretically show that applying PSG to the standard gradient descent (GD), which is called PSGD, is equivalent to the GD in the warped weight space, a space made by warping the original weight space via an appropriately designed invertible function. Second, we empirically show that PSG acting as a regularizer to a weight vector is very useful in model compression domains such as quantization and sparse training. PSG reduces the gap between the weight distributions of a full-precision model and its compressed counterpart. This enables the versatile deployment of a model either as an uncompressed mode or as a compressed mode depending on the availability of resources. The experimental results on CIFAR-10/100 and Imagenet datasets show the effectiveness of the proposed PSG in both domains of sparse training and quantization even for extremely low bits.
Algebraic hardness versus randomness in low characteristic

We show that lower bounds for explicit constant-variate polynomials over fields of characteristic p > 0 are sufficient to derandomize polynomial identity testing over fields of characteristic p. In this setting, existing work on hardness-randomness tradeoffs for polynomial identity testing requires either the characteristic to be sufficiently large or the notion of hardness to be stronger than the standard syntactic notion of hardness used in algebraic complexity. Our results make no restriction on the characteristic of the field and use standard notions of hardness. We do this by combining the Kabanets-Impagliazzo generator with a white-box procedure to take pth roots of circuits computing a pth power over fields of characteristic p. When the number of variables appearing in the circuit is bounded by some constant, this procedure turns out to be efficient, which allows us to bypass difficulties related to factoring circuits in characteristic p. We also combine the Kabanets-Impagliazzo generator with recent "bootstrapping" results in polynomial identity testing to show that a sufficiently-hard family of explicit constant-variate polynomials yields a near-complete derandomization of polynomial identity testing. This result holds over fields of both zero and positive characteristic and complements a recent work of Guo, Kumar, Saptharishi, and Solomon, who obtained a slightly stronger statement over fields of characteristic zero.
Trialstreamer: Mapping and Browsing Medical Evidence in Real-Time

We introduce Trialstreamer, a living database of clinical trial reports. Here we mainly describe the evidence extraction component; this extracts from biomedical abstracts key pieces of information that clinicians need when appraising the literature, and also the relations between these. Specifically, the system extracts descriptions of trial participants, the treatments compared in each arm (the interventions), and which outcomes were measured. The system then attempts to infer which interventions were reported to work best by determining their relationship with identified trial outcome measures. In addition to summarizing individual trials, these extracted data elements allow automatic synthesis of results across many trials on the same topic. We apply the system at scale to all reports of randomized controlled trials indexed in MEDLINE, powering the automatic generation of evidence maps, which provide a global view of the efficacy of different interventions combining data from all relevant clinical trials on a topic. We make all code and models freely available alongside a demonstration of the web interface.
Semi-Supervised Learning: the Case When Unlabeled Data is Equally Useful

Semi-supervised learning algorithms attempt to take advantage of relatively inexpensive unlabeled data to improve learning performance. In this work, we consider statistical models where the data distributions can be characterized by continuous parameters. We show that under certain conditions on the distribution, unlabeled data is equally useful as labeled date in terms of learning rate. Specifically, let $n, m$ be the number of labeled and unlabeled data, respectively. It is shown that the learning rate of semi-supervised learning scales as $O(1/n)$ if $m\sim n$, and scales as $O(1/n^{1+\gamma})$ if $m\sim n^{1+\gamma}$ for some $\gamma>0$, whereas the learning rate of supervised learning scales as $O(1/n)$.
Cradle(s) of the Sun

The Sun likely formed as part of a group of stars. A close stellar flyby by one of the solar siblings is probably responsible for the sharp outer edge in the solar system's mass distribution. The frequency of such close flybys can be used to determine the likely type of birth environment of the solar system. Young stellar groups develop very quickly, expanding significantly within just a few million years. Here we model this strong dynamical development of young stellar groups and determine the resulting close flyby history. We find that solar system equivalents are predominantly produced in areas with stellar densities in the range 5 × 104 pc−3 < nlocal < 2 × 105 pc−3. Remarkably, we find that only two very distinct types of stellar groups can be considered as serious contestants as the cradle of the Sun—high-mass, extended associations (Mc > 20,000 M⊙) and intermediate-mass, compact clusters (Mc < 3000 M⊙). Present-day counterparts would be the association NGC 2244 and the M44 cluster, respectively. In these two types of stellar groups, close flybys take place at a sufficiently high rate, while not being too destructive either. A final decision between these two remaining options will require the incorporation of constraints from cosmochemical studies.
Multiplicity dependence of J/$\psi$ production at midrapidity in pp collisions at $\sqrt{s}$ = 13 TeV

Measurements of the inclusive J/$\psi$ yield as a function of charged-particle pseudorapidity density $\rm{d}N_\rm{ch}/\rm{d}\eta$ in pp collisions at $\sqrt{s}$ = 13 TeV with ALICE at the LHC are reported. The J/$\psi$ meson yield is measured at midrapidity ($|y|<0.9$) in the dielectron channel, for events selected based on the charged-particle multiplicity at midrapidity ($|\eta|<1$) and at forward rapidity ($-3.7<\eta<-1.7$ and $2.8<\eta<5.1$); both observables are normalized to their corresponding averages in minimum bias events. The increase of the normalized J/$\psi$ yield with normalized $\rm{d}N/\rm{d}\eta$ is significantly stronger than linear and dependent on the transverse momentum. The data are compared to theoretical predictions, which describe the observed trends well, albeit not always quantitatively.
Digital Neural Networks in the Brain: From Mechanisms for Extracting Structure in the World To Self-Structuring the Brain Itself

In order to keep trace of information, the brain has to resolve the problem of where information is and how to index new ones. We propose that the neural mechanism used by the prefrontal cortex (PFC) to detect structure in temporal sequences, based on the temporal order of incoming information, has served as second purpose to the spatial ordering and indexing of brain networks. We call this process, apparent to the manipulation of neural 'addresses' to organize the brain's own network, the 'digitalization' of information. Such tool is important for information processing and preservation, but also for memory formation and retrieval.
Learning Combinatorial Optimization on Graphs: A Survey With Applications to Networking

Existing approaches to solving combinatorial optimization problems on graphs suffer from the need to engineer each problem algorithmically, with practical problems recurring in many instances. The practical side of theoretical computer science, such as computational complexity, then needs to be addressed. Relevant developments in machine learning research on graphs are surveyed for this purpose. We organize and compare the structures involved with learning to solve combinatorial optimization problems, with a special eye on the telecommunications domain and its continuous development of live and research networks.
Mining Message Flows from System-on-Chip Execution Traces

Comprehensive and well-defined specifications are necessary to perform rigorous and thorough validation of system-on-chip (SoC) designs. Message flows specify how components of an SoC design communicate and coordinate with each other to realize various system functions. They are essential for efficient system-level validation and debug for SoC designs. However, in practice such specifications are usually not available, often ambiguous, incomplete, or even contain errors. This paper addresses that problem by proposing a specification mining framework, FlowMiner, that automatically extracts message flows from SoC execution traces. It also includes inference rules and optimization techniques to improve mining performance and reduce mining complexity. Evaluation of this framework in several experiments shows promising results.
First passage time study of DNA strand displacement

DNA strand displacement, where a single-stranded nucleic acid invades a DNA duplex, is pervasive in genomic processes and DNA engineering applications. The kinetics of strand displacement have been studied in bulk; however, the kinetics of the underlying strand exchange were obfuscated by a slow bimolecular association step. Here, we use a novel single-molecule Fluorescence Resonance Energy Transfer (smFRET) approach termed the “fission” assay to obtain the full distribution of first passage times of unimolecular strand displacement. At a frame time of 4.4 ms, the first passage time distribution for a 14-nt displacement domain exhibited a nearly monotonic decay with little delay. Among the eight different sequences we tested, the mean displacement time was on average 35 ms and varied by up to a factor of 13. The measured displacement kinetics also varied between complementary invaders and between RNA and DNA invaders of the same base sequence except for T→U substitution. However, displacement times were largely insensitive to the monovalent salt concentration in the range of 0.25 M to 1 M. Using a one-dimensional random walk model, we infer that the single-step displacement time is in the range of ∼30 µs to ∼300 µs depending on the base identity. The framework presented here is broadly applicable to the kinetic analysis of multistep processes investigated at the single-molecule level.
Dynamics-Aware Latent Space Reachability for Exploration in Temporally-Extended Tasks

Self-supervised goal proposal and reaching is a key component of efficient policy learning algorithms. Such a self-supervised approach without access to any oracle goal sampling distribution requires deep exploration and commitment so that long horizon plans can be efficiently discovered. In this paper, we propose an exploration framework, which learns a dynamics-aware manifold of reachable states. Given a new goal, our proposed method visits a state at the current frontier of reachable states (commitment/reaching) and then explores to reach the goal (exploration). This allocates exploration budget near the frontier of the reachable region instead of its interior. We target the challenging problem of policy learning from initial and goal states specified as images, and do not assume any access to the underlying ground-truth states of the robot and the environment. To keep track of reachable latent states, we propose a distance conditioned reachability network that is trained to infer whether one state is reachable from another within the specified latent space distance. So, given an initial state, we obtain a frontier of reachable states from that state. By incorporating a curriculum for sampling easier goals (closer to the start state) before more difficult goals, we demonstrate that the proposed self-supervised exploration algorithm, can achieve 20% superior performance on average compared to existing baselines on a set of challenging robotic environments, including on a real robot manipulation task.
Cohomology of algebraic varieties over non-archimedean fields

Abstract We develop a sheaf cohomology theory of algebraic varieties over an algebraically closed nontrivially valued nonarchimedean field K based on Hrushovski-Loeser’s stable completion. In parallel, we develop a sheaf cohomology of definable subsets in o-minimal expansions of the tropical semi-group $\Gamma _{\infty }$ , where $\Gamma $ denotes the value group of K. For quasi-projective varieties, both cohomologies are strongly related by a deformation retraction of the stable completion homeomorphic to a definable subset of $\Gamma _{\infty }$ . In both contexts, we show that the corresponding cohomology theory satisfies the Eilenberg-Steenrod axioms, finiteness and invariance, and we provide natural bounds of cohomological dimension in each case. As an application, we show that there are finitely many isomorphism types of cohomology groups in definable families. Moreover, due to the strong relation between the stable completion of an algebraic variety and its analytification in the sense of V. Berkovich, we recover and extend results on the singular cohomology of the analytification of algebraic varieties concerning finiteness and invariance.
On the cop number of graphs of high girth

We establish a lower bound for the cop number of graphs of high girth in terms of the minimum degree, and more generally, in terms of a certain growth condition. We show, in particular, that the cop number of any graph with girth g $g$ and minimum degree δ $\delta $ is at least 1 g ( δ − 1 ) ⌊ g − 1 4 ⌋ $\frac{1}{g}{(\delta -1)}^{\lfloor \frac{g-1}{4}\rfloor }$ . We establish similar results for directed graphs. While exposing several reasons for conjecturing that the exponent 1 4 g $\frac{1}{4}g$ in this lower bound cannot be improved to ( 1 4 + ε ) g $(\frac{1}{4}+\varepsilon )g$ , we are also able to prove that it cannot be increased beyond 3 8 g $\frac{3}{8}g$ . This is established by considering a certain family of Ramanujan graphs. In our proof of this bound, we also show that the “weak” Meyniel's conjecture holds for expander graph families of bounded degree.
Intermittent decoherence blockade.

It has long been recognized that emission of radiation from atoms is not an intrinsic property of individual atoms themselves, but it is largely affected by the characteristics of the photonic environment and by the collective interaction among the atoms. A general belief is that preventing full decay and/or decoherence requires the existence of dark states, i.e., dressed light-atom states that do not decay despite the dissipative environment. Here, we show that, contrary to such a common wisdom, decoherence suppression can be intermittently achieved on a limited time scale, without the need for any dark state, when the atom is coupled to a chiral ring environment, leading to a highly non-exponential staircase decay. This effect, that we refer to as intermittent decoherence blockade, arises from periodic destructive interference between light emitted in the present and light emitted in the past, i.e., from delayed coherent quantum feedback.
The Hubble Space Telescope PanCET Program: An Optical to Infrared Transmission Spectrum of HAT-P-32Ab

We present a 0.3−5 μm transmission spectrum of the hot Jupiter HAT-P-32Ab observed with the Space Telescope Imaging Spectrograph and Wide Field Camera 3 instruments mounted on the Hubble Space Telescope, combined with Spitzer Infrared Array Camera photometry. The spectrum is composed of 51 spectrophotometric bins with widths ranging between 150 and 400 Å, measured to a median precision of 215 ppm. Comparisons of the observed transmission spectrum to a grid of 1D radiative-convective equilibrium models indicate the presence of clouds/hazes, consistent with previous transit observations and secondary eclipse measurements. To provide more robust constraints on the planet’s atmospheric properties, we perform the first full optical to infrared retrieval analysis for this planet. The retrieved spectrum is consistent with a limb temperature of K, a thick cloud deck, enhanced Rayleigh scattering, and ∼10× solar H2O abundance. We find log(Z/Z⊙) = , and compare this measurement with the mass–metallicity relation derived for the solar system.
c-Almost periodic type functions and applications

Abstract In this paper, we introduce several various classes of c-almost periodic type functions and their Stepanov generalizations, where c ∈ ℂ and |c| = 1. We also consider the corresponding classes of c-almost periodic type functions depending on two variables and prove several related composition principles. Plenty of illustrative examples and applications are presented.
Improving Segmentation for Technical Support Problems

Technical support problems are often long and complex. They typically contain user descriptions of the problem, the setup, and steps for attempted resolution. Often they also contain various non-natural language text elements like outputs of commands, snippets of code, error messages or stack traces. These elements contain potentially crucial information for problem resolution. However, they cannot be correctly parsed by tools designed for natural language. In this paper, we address the problem of segmentation for technical support questions. We formulate the problem as a sequence labelling task, and study the performance of state of the art approaches. We compare this against an intuitive contextual sentence-level classification baseline, and a state of the art supervised text-segmentation approach. We also introduce a novel component of combining contextual embeddings from multiple language models pre-trained on different data sources, which achieves a marked improvement over using embeddings from a single pre-trained language model. Finally, we also demonstrate the usefulness of such segmentation with improvements on the downstream task of answer retrieval.
Non‐uniform stomatal closure induced by water stress causes putative non‐stomatal inhibition of photosynthesis

summary The partial pressure of intercellular CO2 derived from gas exchange data for water-stressed grapevine (Vitis vinijera L.), oleander (Nerium oleander L.) and red-flowering gum (Eucalyptus ficifolia F. Muell.) does not indicate the actual average intercellular CO2 of the leaf. This can be determined from chlorophyll fluorescence signals and modelled from conventional gas exchange information. Water stress increases endogenous levels of abscisic acid and the leaves respond in a similar manner to that observed when exogenous abscisic acid is applied. Autoradiograms show that water stress results in non-uniform gas exchange over small areas of the leaf. Non-uniform stomatal closure can account for the apparent non-stomatal inhibition of photosynthesis in plants experiencing water stress.
The carbon implications of large‐scale afforestation of agriculturally marginal land with short‐rotation willow in Saskatchewan

Afforestation with short‐rotation coppice (SRC) willow plantations for the purpose of producing bioenergy feedstock was contemplated as one potential climate change mitigation option. The objectives of this study were to assess the magnitude of this mitigation potential by addressing: (i) the land area potentially available for SRC systems in the province of Saskatchewan, Canada; (ii) the potential biomass yields of SRC plantations; and (iii) the carbon implications from such a large‐scale afforestation program. Digital soils and land‐use data were used to identify, map, and group into clusters of similar polygons 2.12 million hectares (Mha) of agriculturally marginal land that was potentially suitable for willow in the Boreal Plains and Prairies ecozones in Saskatchewan. The Physiological Principles in Predicting Growth (3PG) model was calibrated with data from SRC experiments in Saskatchewan, to quantify potential willow biomass yields, and the Carbon Budget Model of the Canadian Forest Sector (CBM‐CFS3), was used to simulate stand and landscape‐level C fluxes and stocks. Short‐rotation willow plantations managed in 3 year rotations for seven consecutive harvests (21 years) after coppicing at Year 1 produced about 12 Mg ha−1 yr−1 biomass. The more significant contribution to the C cycle was the cumulative harvest. After 44 years, the potential average cumulative harvested biomass C in the Prairies was 244 Mg C ha−1 (5.5 Mg C ha−1 yr−1) about 20% higher than the average for the Boreal Plains, 203 Mg C ha−1 (4.6 Mg C ha−1 yr−1). This analysis did not consider afforestation costs, rate of establishment of willow plantations, and other constraints, such as drought and disease effects on biomass yield. The results must therefore be interpreted as a biophysical mitigation potential with the technical and economic potential being both lower than our estimates. Nevertheless, short‐rotation bioenergy plantations offer one potential mitigation option to reduce the rate of CO2 accumulation in the earth's atmosphere and further research is needed to operationalise such a mitigation effort.
Authenticating On-Body IoT Devices: An Adversarial Learning Approach

By adding users as a new dimension to connectivity, on-body Internet-of-Things (IoT) devices have gained considerable momentum in recent years, while raising serious privacy and safety issues. Existing approaches to authenticate these devices limit themselves to dedicated sensors or specified user motions, undermining their widespread acceptance. This paper overcomes these limitations with a general authentication solution by integrating wireless physical layer (PHY) signatures with upper-layer protocols. The key enabling techniques are constructing representative radio propagation profiles from received signals, and developing an adversarial multi-player neural network to accurately recognize underlying radio propagation patterns and facilitate on-body device authentication. Once hearing a suspicious transmission, our system triggers a PHY-based challenge-response protocol to defend in depth against active attacks. We prove that at equilibrium, our adversarial model can extract all information about propagation patterns and eliminate any irrelevant information caused by motion variances and environment changes. We build a prototype of our system using Universal Software Radio Peripheral (USRP) devices and conduct extensive experiments with various static and dynamic body motions in typical indoor and outdoor environments. The experimental results show that our system achieves an average authentication accuracy of 91.6%, with a high area under the receiver operating characteristic curve (AUROC) of 0.96 and a better generalization performance compared with the conventional non-adversarial approach.
Investigating Label Bias in Beam Search for Open-ended Text Generation

Beam search is an effective and widely used decoding algorithm in many sequence-to-sequence (seq2seq) text generation tasks. However, in open-ended text generation, beam search is often found to produce repetitive and generic texts, sampling-based decoding algorithms like top-k sampling and nucleus sampling are more preferred. Standard seq2seq models suffer from label bias due to its locally normalized probability formulation. This paper provides a series of empirical evidence that label bias is a major reason for such degenerate behaviors of beam search. By combining locally normalized maximum likelihood estimation and globally normalized sequence-level training, label bias can be reduced with almost no sacrifice in perplexity. To quantitatively measure label bias, we test the model's ability to discriminate the groundtruth text and a set of context-agnostic distractors. We conduct experiments on large-scale response generation datasets. Results show that beam search can produce more diverse and meaningful texts with our approach, in terms of both automatic and human evaluation metrics. Our analysis also suggests several future working directions towards the grand challenge of open-ended text generation.