caprion/mscinli_2_class huggingface dataset:
Domain: Software and its engineering
Premise: As a result, many potentially useful research results lie fallow, to be reinvented sometimes by practitioners but, typically, in a more limited and, hence, less useful form that is imposed by the specific technology and problem on hand.
Hypothesis: shunned by industry, many researchers do indeed turn to investigating problems that are of little or no practical relevance, which, nevertheless, allows them to publish and thus maintain or enhance their academic standings.
Relation: contrasting
Domain: NeurIPS
Premise: Recently, deep generative models have emerged as effective techniques for image animation and video retargeting [2,41,3,42,27,28,37,40,31,21].
Hypothesis: generative Adversarial Networks (GANs) [14] and Variational Auto-Encoders (VAEs) [20] have been used to transfer facial expressions [37] or motion patterns [3] between human subjects in videos.
Relation: entailment
Domain: NeurIPS
Premise: In order to integrate the objects in the expected way defined by the scene graph as well as maintaining the visual appearance of the objects, we designed a Crop Refining Network as well as an attentionbased Object-Image Fuser, which can encode the spatial arrangements and visual appearance of the objects as well as their pair-wise interactions into one scene canvas.
Hypothesis: it can encode the complicated interactions between the objects.
Relation: reasoning
Domain: Security and privacy
Premise: Second, for concrete­ness we chose our construction to be in the Turing Machine model, however, we believe it could be fairly easily adjusted to a different model such as RAM by simply letting the next position be a function of the state into [0, S-1] as opposed to moving the head position.
Hypothesis: our proof struc­ture would remain the same with this minor change to the construction.
Relation: entailment
Domain: Security and privacy
Premise: Type-based CFIs [2, 24, 31, 42] reduce the average indirect targets (AIT) by checking indirect branches with type.
Hypothesis: mCFI [31] checks type and number of parameters, Forward-edge CFI [42] checks the number of parameters, Safedispatch [24] and vfGuard [2] check class type for virtual function calls.
Relation: entailment
Domain: Networks
Premise: The ASIC handles all traffic that can be served from the hardware table and passes the rest to the software for­warders.
Hypothesis: the moment the rule is promoted to the hard­ware table, there is packet reordering (directly proportional to the amount of buffering in the software forwarder and the hardware-table update latency tup), but throughput climbs to line rate within 20ms.
Relation: neutral
Domain: NeurIPS
Premise: Our analysis then consists of two main parts where each part bounds one of the terms in the RHS above.
Hypothesis: we first prove that given the partial comparisons seen so far, we can obtain a relatively good estimation to the rank of the arrived element, and then in the second part, we show that we can typically find an unassigned position in the close proximity of this estimated rank to assign to it.
Relation: entailment
Domain: Networks
Premise: HyPer4 can preemptively declare statically bound registers, counters, and meters for every table that emulates the match piece of a match-action stage, but this approach is likely to prove infeasible for many hardware P4 targets.
Hypothesis: registers can vary in width, which for the preemptive ap­proach would require HyPer4 registers be declared with sufficient width to cover the maximum need, resulting in a lot of wasted register memory for the average case.
Relation: entailment
Domain: Networks
Premise: Since these down link sequences need to be detected prior to synchronization, they must have low streaming auto-correlations, both with themselves and the other sequences in the orthogonal set.
Hypothesis: since the sequences must be detectable without knowledge of when they start, the receiver must perform a full correlation at every sample, thus a time-shift of the sequences must produce a low correlation; otherwise it could cause an erroneous detection.
Relation: entailment
Domain: Networks
Premise: If the TCAM can store four rules, we cannot select the four rules with highest traffic volume (i.e., R2, R3, R5, and R6), because packets that should match R1 (with pattern 000) would match R2 (with pattern 00*); similarly, some packets (say with header 110) that should match R4 would match R5 (with pattern 1*0).
Hypothesis: rules R2 and R5 depend on rules R1 and R4, respectively.
Relation: entailment
Domain: Software and its engineering
Premise: To address these problems, we developed an automated compiler support based on the latest advances in polyhedral frameworks (e.g., [5, 36]) to greatly reduce the human design effort currently required to create effectively synthesizable specification of designs using HLS tools.
Hypothesis: we developed compiler support for source-to-source transformations to optimize critical resources such as memory bandwidth to off-chip memory and on-chip buffer capacity [27].
Relation: entailment
Domain: Networks
Premise: The design of light beacons is driven by the observation that while the perceived light intensity represents the sum of all incoming light rays, these light rays can be separated in the frequency domain if they flash at different frequencies.
Hypothesis: if we transform a time series of perceived light intensity to the frequency domain using Fast Fourier Transform (FFT), we can observe frequency power peaks at the frequencies at which these light rays flash.
Relation: entailment
Domain: Hardware
Premise: We represent user-defined data types as statically-sized bit vectors in hardware; if a definition is recursive, we cannot determine how many bits to assign to the recursive component at compile time.
Hypothesis: we replace recursive portions of these data types with a pointer data type e.g. a non-empty integer list defined as Cons Int List becomes Cons Int Ref, where Ref can be defined as a primitive numeric type.
Relation: reasoning
Domain: Hardware
Premise: The similarity between spatial computing and an assembly line and with data routing in general, calls for examining the relevance of various buffering, queuing and routing schemes and constructs used there to the organization and interconnection of operand memory in a spatial computer.
Hypothesis: it is important to create a set of such constructs and to implement them efficiently as building blocks in the hardware (just like lookup tables, flip flops, multipliers and connections).
Relation: entailment
Domain: Security and privacy
Premise: Because the links between the application and its libraries are only resolved at load time and because they are stored in the form of easily readable symbol information, attackers can study which external functionality the program requires and influence the linking process to their own advantage.
Hypothesis: attackers can hook a shared library, which means that run-time invocations to shared library functionality are redirected to code controlled by the attacker.
Relation: entailment
Domain: Hardware
Premise: Inside this droplet, the luminescent process takes place or not, according to the presence or absence of signal- Figure 3: Graphic representation of the adopted encoding approach ing molecules.
Hypothesis: downstream the signaling molecule detector, a sequence of luminescent and non luminescent droplets is produced, where the luminescent droplet represents the bit 1 and the non-luminescent droplet represents the bit 0 .
Relation: reasoning
Domain: NeurIPS
Premise: In contrast, this paper shows that Step Decay schedules, which cut the learning rate by a constant factor every constant number of epochs (i.e., the learning rate decays geometrically) offer significant improvements over any polynomially decaying step size schedule.
Hypothesis: the behavior of the final iterate with step decay schedules is off from the statistical minimax rate by only log factors (in the condition number for the strongly convex case, and in T in the non-strongly convex case).
Relation: entailment
Domain: Security and privacy
Premise: To provide secure and efficient communication among components, SeCage follows the idea of separating control plane from data plane by using the VMFUNC feature from Intel's hardware­assisted virtualization support.
Hypothesis: seCage first designates the security policy on which secret compartment is invokable by another compartment to the CPU, and allows such an invocation from being done without the hypervisor intervention.
Relation: entailment
Domain: Software and its engineering
Premise: Thus, given that the number of rewrite operations for STT-MRAM is a perfectly feasible number for main memory or CPU cache, STT-MRAM has the potential of serving as the main memory of computer systems like DRAM does today.
Hypothesis: one disadvantage of STT-MRAM is that the write energy will be 102 times larger than that of DRAM.
Relation: contrasting
Domain: NeurIPS
Premise: Notice that the F1 score is relatively stable with respect to the hyperparameter change.
Hypothesis: changing λ from 0.001 to 0.1 the performance over the dataset changes by at most 1.2% in F1 score, while changing ρinter and ρintra from 0.01 to 10, the performance changes by at most 0.6% and 2.1%, respectively.
Relation: entailment
Domain: Hardware
Premise: We select an energy efficient RF size for comparison against the nonoptimized and the pruned radix-2 implementations.
Hypothesis: if the vector register width is W , the number of memory  min(m, 2N k ) subset_acc(N, m, k, W ) = (7) W We use the ceiling operator so that if the number of non-zero points is not a multiple of SIMD width W , then the additional load fetches the remaining values.
Relation: neutral
Domain: Networks
Premise: ROCH capabilities are demonstrated through multiple graphical user interfaces (GUIs) at the relay (R1, R2), destination (D), and trace recorder (TR) nodes.
Hypothesis: gUIs at both secondary relay nodes illustrate real-time SINR and queueing information, while the laptop-PC, that controls the secondary destination and primary receiver (PRx) nodes, compares the instantaneous throughput between the primary link (PTx– PRx) and the secondary two-hop network. 
Relation: entailment
Domain: NeurIPS
Premise: We ask for algorithms that enforce well-studied statistical fairness constraints across two protected populations (we focus on the "equal opportunity" constraint of [18], which enforces equalized false positive rates or false negative rates, but our techniques also apply to other statistical fairness constraints like "statistical parity" [12]).
Hypothesis: we ask for algorithms that satisfy these constraints (with respect to the unknown underlying distribution) at every round of the learning procedure.
Relation: entailment
Domain: NeurIPS
Premise: This cannot be guaranteed with robust splits without pruning since the tree construction process is greedy, and some training examples are also influenced by splits at different branches.
Hypothesis: in order to control the upper bound on the robust loss globally over the whole tree as in (8), and not just for the current subtree as in ( 9), we need a post-hoc approach that takes into account the structure of the whole tree.
Relation: reasoning
Domain: NeurIPS
Premise: In particular, pruning more than 60% of the Enc-Dec attention heads will result in catastrophic performance degradation, while the encoder and decoder self-attention layers can still produce reasonable translations (with BLEU scores around 30) with only 20% of the original attention heads.
Hypothesis: encoder-decoder attention is much more dependent on multi-headedness than self-attention.
Relation: entailment
Domain: Networks
Premise: When either happens, NetProxy intercepts the ARP request, changes the source hardware address (SHA) of the requester with that of the ENI of the proxy gateway, and finally forwards the modified packet on the other network interface.
Hypothesis: similarly, Figure 3b shows that the density distribution of the number of packets in the EN during each interval has much gentler slopes than that describing the conditions in the IN in the same intervals.
Relation: neutral
Domain: Software and its engineering
Premise: A model derived by WI can be used to generate artificial workloads that are close to the actual workload but may have random mutations/omissions.
Hypothesis: we can now define the high-level problem solved by WI: Given an input sequence T that we will treat as training data, we wish to compute k and a Markov model Mθ(k) ∈ Mk such that Mθ(k) is a “good” abstraction for the process that generated T.
Relation: neutral
Domain: NeurIPS
Premise: Here, for a given prior P(c * ), the goal of learning is to identify C * either exactly (when Î» = 0) or approximately (when Î» > 0), while bounding the expected number of queries (x it , x jt ) made to the ground-truth matrix Î£, the expectation being over the noise, and possibly over P(c * ).
Hypothesis: if C is the clustering produced by the algorithm after it stops, we would like to prove upper bounds on E[d H (Î£ * , C)], as related to the number of active learning rounds, as well as to the properties of the prior distribution.
Relation: entailment
Domain: Hardware
Premise:  Additionally, since an application typically processes much more data than the number of instructions executed, most prior cache locking methods, if applied directly to the data cache, would require a large data cache and/or potentially result in runtime overhead in terms of performance and/or energy, since complex runtime analysis wou6 ld be required due to the inherent runtime variability of data caching [21].
Hypothesis: we propose a new methodology for leveraging cache locking for data cache performance and energy consumption optimizations in general purpose embedded systems.
Relation: reasoning
Domain: Networks
Premise: Multi-user MIMO (MU-MIMO) is a hallmark of the recent WLAN standard 802.11ac, with which an Mtx-antenna AP can simultaneously transmit up to Mtx data streams to different users, thus achieving a maximum degree-of-freedom (DoF) of Mtx.
Hypothesis: exploiting MU-MIMO's full potential is a non-trivial issue in practice.
Relation: contrasting
Domain: Hardware
Premise: To evaluate the RA, ProSA-RA suggests using a checklist-based inspection approach named FERA (Framework for Evaluation of Reference Architectures) [30].
Hypothesis: we followed the recommendation and used FERA to conduct an evaluation in order to verify: (i) completeness of general information related to the construction and content of the architectural views; (ii) adequacy for releasing of the architectural description of our RA; and (iii) viability and change possibilities of our RA.
Relation: reasoning
Domain: Networks
Premise: Informally, these properties can be stated as follows: (A) The attacker should not be able to use a service of an honest RP as an honest user.
Hypothesis: the attacker should not get hold of (be able to derive from his current knowledge) a service token issued by an honest RP for an ID of an honest user (browser), even if the browser was closed and then later used by a malicious user, i.e., after a CLOSECORRUPT (see Section 3.3).
Relation: entailment
Domain: Networks
Premise: However, users might still be impacted, even when the RAN accessibility KPI is good, because users that lost radio coverage are not even accounted for in the KPI calculation or when the root cause of the service problems are beyond the RAN (e.g., congestion on a core network element). 
Hypothesis: service disruption occurs when accessibility is bad, but accessibility being good does not necessarily imply that service is good.
Relation: entailment
Domain: Software and its engineering
Premise: one should determine the appropriate formalism level for describing domain elements (e.g., legis­lation, standards, quality requirements, and system com­pliance), infrastructure elements (e.g., software elements, hardware elements, and architectural styles), application el­ements (e.g., constraints and functional requirements), and crosscutting elements (e.g., domain terminology, external communication, decisions, and internal communication) of reference architectures for embedded systems.
Hypothesis: a formal description can support the automated comparison and/or selection of components for instantiating these refer­ence architectures.
Relation: entailment
Domain: Security and privacy
Premise: Although we now handle a reactive functionality, we stress that the cash that is deposited at the beginning of the protocol is dis­tributed only at the end (i.e., no cash distribution occurs in any intermediate stage).
Hypothesis: secure cash distribution provides a means to keep the cash deposited in escrow while parties learn output from each stage's function evaluation, and thus can revise their inputs to a later stage.
Relation: entailment
Domain: Software and its engineering
Premise: On the basis of a successful alignment of expectations and pre-study, the industrial PhD project proved in our case to be a good instrument for bringing the technology safely from TRL 5 to TRL 7, that is, through the valley of death.
Hypothesis: there are still challenges and risks: The strong involvement of a company hiring the PhD candidate and typically paying two thirds of the salary gives a risk of the company not being able (or willing to) complete the project.
Relation: contrasting
Domain: Networks
Premise: Based on this novel approach, we develop service process characterizations for fading channels with finite blocklength channel coding, leading to novel probabilistic delay bounds that can give a fundamental insight into the capabilities and limitations of wireless networks when facing low-latency M2M applications.
Hypothesis: we show that the Shannon capacity model significantly overestimates the delay performance for such applications, which would lead to insufficient resource allocations.
Relation: entailment
Domain: Networks
Premise: For example, in ZigBee devices, the packet-level RSSI information is sampled at only 31.25KHz, while the phase­shift is obtained at 4MHz by the ZigBee PHY for (de)modulation.
Hypothesis: the throughput of packet-level CTCs is inherently bounded by a low sampling rate (e.g., KHz) in contrast to hundreds of Kbps for native ZigBee communication.
Relation: entailment
Domain: Security and privacy
Premise: Koppula, Lewko and Waters [KLW15] devise a fully suc­cinct garbling scheme for Turing machines from non-succinct IO and one way functions, using techniques that extend those of [CHJV15].
Hypothesis: in their garbling scheme the runtime, space and description size of the garbled program are proportional to those of the Turing machine representa­tion of the plaintext program.
Relation: entailment
Domain: Networks
Premise: In [6][7], they provide simple closed-form expressions for coverage probability, which is the probability that a randomly deployed UE can achieve a target SINR in heterogeneous network with SBSs.
Hypothesis: this result implies that the number of hidden nodes is proportional to intensity .of SBSs Figure 6: CDF of the number of hidden nodes (without consideration of UE's location) Figure 7: CDF of the number of hidden nodes (with consideration of UE s location) 5.
Relation: neutral
Domain: NeurIPS
Premise: The total number of inversions can be approximated within a factor of 2 by the Spearman's footrule.
Hypothesis: we can write the cost of Algorithm 1 (up to a factor 2) as follows: This basically breaks the cost of the algorithm in two parts: one is the cost incurred by the estimation step and the other one is the cost of the assignment step.
Relation: reasoning
Domain: NeurIPS
Premise: More importantly, since it is based on adversarially generated data points, the adversarial estimator is expected to differ significantly from the test error rate if the model is overfitted to the test set, providing a way to detect test set overfitting.
Hypothesis: the test error rate and the adversarial error estimate (calculated based on the same test set) must be close if the test set and the model are independent, and are expected to be different in the opposite case.
Relation: reasoning
Domain: NeurIPS
Premise: The generative adversarial network (GAN) [15] is arguably the most successful generative model in recent years, which have shown a remarkable progress across a broad range of applications, e.g., image synthesis [5,21,40], data augmentation [49,18] and style transfer [58,10,34].
Hypothesis: as its advanced variant, the conditional GANs (cGANs) [31] have gained a considerable attention due to its class-wise controllability [9,42,10] and superior quality for complex generation tasks [39,33,5].
Relation: entailment
Domain: NeurIPS
Premise: Various versions of this fundamental problem have been studied before (see e.g., Drineas et al., 2006Drineas et al., , 2011Dhillon et al., 2013;Ma et al., 2015;Raskutti and Mahoney, 2016;Thanei et al., 2017, and the references therein).
Hypothesis: this is because iid projections distort the geometry of Euclidean space due to their non-orthogonality.
Relation: neutral
Domain: NeurIPS
Premise: Without further constraints over m, directly applying AIRL to learning the reward function (by augmenting each component of AIRL with an additional context variable m inferred by q Ïˆ ) could simply ignore m, which is similar to the case of InfoGAIL [21].
Hypothesis: some connection between the reward function and the latent context variable m need to be established.
Relation: reasoning
Domain: Networks
Premise: During our implementation, we faced a problem related to the fact that the LTE model in OPNET (which follows the 3GPP spec­ifications for single frequency networks) considers these settings to be static during the entire running time of a simulation.
Hypothesis: the LTE configuration node cannot be adjusted dynamically, and its initial settings (including the MCS assigned for each MBMS bearer) cannot be changed after the deployment phase.
Relation: entailment
Domain: NeurIPS
Premise: A related line of work [KLS09, ABL17, DKK + 16, LRV16, DKK + 17, DKK + 18, DKS18, KKM18, DKS19, DKK + 19] has given polynomial time robust estimators for a range of learning tasks.
Hypothesis: [KLS09, ABL17, DKS18, DKK + 19] obtained efficient PAC learning algorithms for halfspaces with malicious noise [Val85,KL93], under the assumption that the uncorrupted data comes from a "tame" distribution, e.g., Gaussian or isotropic log-concave.
Relation: entailment
Domain: Software and its engineering
Premise: In the next section, an application of the method on a self-adaptive system is presented using hypothetical data.
Hypothesis: the quantified NFRs, P(NFRi), P(NFRID), risks as­sociated with each NFR, the outcome attributes, their weights, values, and the percentage effectiveness of each design deci­sion in mitigating the risk are all inspired by empirical data provided by a prototype of the case study.
Relation: entailment
Domain: Security and privacy
Premise: Our participants are somewhat uncomfortable with sharing some data with applications, particularly their contact information (62.3%), profile data (61%) or access to their messages or call data (60%).
Hypothesis: they expressed less discomfort providing access to their browser history (37.6%), Internet connectivity (38.5%) and calendar data (45.8%)(Kruskal-Wallis .2(8)=59.74, p<0.001).
Relation: contrasting
Domain: Software and its engineering
Premise: The Internet Engineering Task Force (IETF) is making a big effort for the standardization of protocols that are aware of energy and processing limitations within the smart objects.
Hypothesis: the IETF working group for Routing Over Low-power and Lossy networks (ROLL) focuses on the design of an IPv6 Routing Protocol for Low power and lossy networks (RPL) [21].
Relation: entailment